Search

New methods of ship propulsion are required to meet the targets for reducing global emissions of greenhouse gases. Hydrogen-powered fuel cells can be used to greatly reduce emissions, facilitating renewable shipping. Fuel cell durability is recognised as a large barrier to widespread fuel cell application. The goal of this graduation project is to develop a model which can be used to examine the trade-off between hydrogen consumption and degradation in the operation of a fuel cell/battery hybrid ship. The main research question is defined as follows: ”How does incorporating fuel cell degradation in the energy management system of a hybrid marine propulsion system affect the resulting fuel consumption and degradation?” An optimisation approach is used to develop an energy management system. A quadratic mathematical model is constructed using the fuel cell’s polarisation curve as its basis. Linear relations for a fuel cell’s current-voltage relation are used to describe the fuel cell power as a quadratic current-power relation. Fuel cell degradation is included in the model by defining various operating conditions and relating them to degradation rates reported in the literature. In turn, the degradation influences the polarisation curve, thereby limiting the fuel cell’s performance. This quadratic approach to modelling the interplay between fuel cell degradation and the fuel cell’s performance is the main contribution to the literature. The proposed energy management system is used to determine a vessel’s optimal operation, depending on the objective function applied. Two objectives are defined: minimisation of fuel consumption and minimisation of fuel cell degradation. Besides testing these single objectives, a weighted-sum approach to multi-objective optimisation is carried out using hydrogen and fuel cell system costs. The optimal operation of the vessel is analysed based on key performance indicators such as fuel consumption, fuel cell, Marine Technology | Ship Design

For statistical inference on regression models with a diverging number of covariates, the existing literature typically makes sparsity assumptions on the inverse of the Fisher information matrix. Such assumptions, however, are often violated under Cox proportion hazards models, leading to biased estimates with under-coverage confidence intervals. We propose a modified debiased lasso method, which solves a series of quadratic programming problems to approximate the inverse information matrix without posing sparse matrix assumptions. We establish asymptotic results for the estimated regression coefficients when the dimension of covariates diverges with the sample size. As demonstrated by extensive simulations, our proposed method provides consistent estimates and confidence intervals with nominal coverage probabilities. The utility of the method is further demonstrated by assessing the effects of genetic markers on patients' overall survival with the Boston Lung Cancer Survival Cohort, a large-scale epidemiology study investigating mechanisms underlying the lung cancer.

ACM SIGIR;ACM SIGWEB, 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 -- 21 October 2023 through 25 October 2023 -- 193792, Graph Neural Network (GNN) training and inference involve significant challenges of scalability with respect to both model sizes and number of layers, resulting in degradation of efficiency and accuracy for large and deep GNNs. We present an end-to-end solution that aims to address these challenges for efficient GNNs in resource constrained environments while avoiding the oversmoothing problem in deep GNNs. We introduce a quantization based approach for all stages of GNNs, from message passing in training to node classification, compressing the model and enabling efficient processing. The proposed GNN quantizer learns quantization ranges and reduces the model size with comparable accuracy even under low-bit quantization. To scale with the number of layers, we devise a message propagation mechanism in training that controls layer-wise changes of similarities between neighboring nodes. This objective is incorporated into a Lagrangian function with constraints and a differential multiplier method is utilized to iteratively find optimal embeddings. This mitigates oversmoothing and suppresses the quantization error to a bound. Significant improvements are demonstrated over state-of-the-art quantization methods and deep GNN approaches in both full-precision and quantized models. The proposed quantizer demonstrates superior performance in INT2 configurations across all stages of GNN, achieving a notable level of accuracy. In contrast, existing quantization approaches fail to generate satisfactory accuracy levels. Finally, the inference with INT2 and INT4 representations exhibits a speedup of 5.11 × and 4.70 × compared to full precision counterparts, respectively. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM., Engineering and Physical Sciences Research Council, EPSRC: EP/T51794X/1

ACM SIGIR;ACM SIGWEB, 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 -- 21 October 2023 through 25 October 2023 -- 193792, Graph Neural Network (GNN) training and inference involve significant challenges of scalability with respect to both model sizes and number of layers, resulting in degradation of efficiency and accuracy for large and deep GNNs. We present an end-to-end solution that aims to address these challenges for efficient GNNs in resource constrained environments while avoiding the oversmoothing problem in deep GNNs. We introduce a quantization based approach for all stages of GNNs, from message passing in training to node classification, compressing the model and enabling efficient processing. The proposed GNN quantizer learns quantization ranges and reduces the model size with comparable accuracy even under low-bit quantization. To scale with the number of layers, we devise a message propagation mechanism in training that controls layer-wise changes of similarities between neighboring nodes. This objective is incorporated into a Lagrangian function with constraints and a differential multiplier method is utilized to iteratively find optimal embeddings. This mitigates oversmoothing and suppresses the quantization error to a bound. Significant improvements are demonstrated over state-of-the-art quantization methods and deep GNN approaches in both full-precision and quantized models. The proposed quantizer demonstrates superior performance in INT2 configurations across all stages of GNN, achieving a notable level of accuracy. In contrast, existing quantization approaches fail to generate satisfactory accuracy levels. Finally, the inference with INT2 and INT4 representations exhibits a speedup of 5.11 × and 4.70 × compared to full precision counterparts, respectively. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM., Engineering and Physical Sciences Research Council, EPSRC: EP/T51794X/1

The paper addresses the problem of accelerating predictive control of non-linear system models augmented with Gaussian processes (GP-MPC). Due to the non-linear and stochastic prediction model, predictive control of GP-based models requires to solve a stochastic optimization problem. Different model simplification methods have to be applied to reformulate this problem to a deterministic, non-linear optimization task that can be handled by a numerical solver. As these problems are still complex, especially with exact moment calculations, real-time implementation of GP-MPC is extremely challenging. The existing solutions accelerate the computations at the solver level by linearizing the non-linear optimization problem and applying sequential convexification. In contrast, this paper proposes a novel GP-MPC solution approach that without linearization formulates a series of surrogate quadratic programs (QP-s) to iteratively obtain the solution of the original non-linear optimization problem. The first step is embedding the non-linear mean-variance dynamics of the GP-MPC prediction model in a linear parameter-varying (LPV) structure and rewriting the constraints in parameter-varying form. By fixing the scheduling trajectory at a known variation (based on previously computed or initial state-input trajectories), optimization of the input sequence for the remaining varying linear model reduces to a linearly constrained quadratic program. After solving the QP, the non-linear prediction model is simulated for the new control input sequence and new scheduling trajectories are updated. The procedure is iterated until the convergence of the scheduling, that is, the solution of the QP converges to the solution of the original non-linear optimization problem. By designing a reference tracking controller for a 4DOF robot arm, we illustrate that the convergence is remarkably fast and the approach is computationally advantageous compared to current solutions. The proposed method

Convex optimization is at the heart of many performance-critical applications across a wide range of domains. Although many high-performance hardware accelerators have been developed for specific optimization problems in the past, designing such accelerator is a challenging task and the resulting computing architecture is often so specific to the targeted application that they can hardly be reused even in a related application within the same domain. To accelerate general-purpose optimization solvers that must operate on diverse user input during run time, an ideal hardware solver should be able to adapt to the provided optimization problem dynamically while achieving high performance and power-efficiency. In this work, a hardware-accelerated general-purpose quadratic program solver, called RSQP, with reconfigurable functional units and data path that facilitate problem-specific customization is presented. RSQP uses a string-based encoding to describe the problem structure with fine granularity. Based on this encoding, functional units and datapath customized to the sparsity pattern of the problem are created by solving a dictionary-based lossless string compression problem and a mixed integer linear program respectively. RSQP has been integrated to accelerate the general-purpose quadratic programming solver OSQP and has been tested using an extensive benchmark with 120 optimization problems from 6 application domains. Through architectural customization, RSQP achieves up to 7× performance improvement over its baseline generic design. Furthermore, when compared with a CPU and a GPU-accelerated implementation, RSQP achieves up to 31.2× and 6.9× end-to-end speedup on these benchmark programs, respectively. Finally, the FPGA accelerator operates at up to 6.6× lower dynamic power consumption and up to 22.7× higher power efficiency over the GPU implementation, making it an attractive solution for power-conscious datacenter applications. © 2023 Copyright held by the owner

This study presents a new signal and filter design procedure for long-range multiple-input multiple-output (MIMO) radars with a pulse length shorter than the return time of signals reflected by the most distant targets. The proposed algorithm adopts and radically improves the method of alternating the optimization of filters and signals, which was recently published by the same authors. It introduces full control of the signal envelope variation during the pulse, preserving acceptable peak-to-average power ratio (PAR) and signal-to-noise ratio (SNR) loss values. This state-of-the-art method allows calculation simplicity and is important for high-speed computations in adaptive applications. A gradient algorithm was used for the signal amplitude and phase optimization. The signal amplitude was controlled using a special signal construction consisting of two complex exponentials., Tato studie presentuje novou metodu návrhu signálů a filtrů pro radary MIMO s impulzy, kratšími než je doba návratu signálu, odražených od nejvzdálenějších cílů. Navržený algoritmus využívá a radikálně zlepšuje metodu střídavé optimalizace filtrů a signálů, nedávno publikovanou stejnými autory. Nová metoda zavádí řízení modulace obálky signálu, přičemž umožňuje zachovat přijatelné hodnoty poměru maximálního ke střednímu výkonu (PAR) a ztrátu poměru signál/šum (SNR). Nová metoda zachovává výpočetní jednoduchost výchozí metody, což je důležité zejména z hlediska rychlosti výpočtů v adaptivních aplikacích. Pro optimalizaci amplitudy a fáze vysílaných signálů je využit gradientní algoritmus, přičemž kontrola amplitudy obálky je realizována pomocí zvláštní konstrukce signálu, skládajícího se ze dvou komplexních exponenciál.

Hybrid Unmanned Aerial Vehicles UAV are vehicles capable of take-off and landing vertically like helicopters while maintaining the long-range efficiency of fixed-wing aircraft. Unfortunately, due to their wing area, these vehicles are sensitive to wind gusts when hovering. One way to increase the hovering wind-rejection capabilities of hybrid UAV is through the addition of extra actuators capable of directing the thrust of the rotors. Nevertheless, the ability to control UAVs with many actuators is strictly related to how well the Control Allocation problem is solved. Generally, to reduce the problem complexity, conventional (CA) methods make use of linearized control effectiveness in order to optimize the inputs that achieve a certain control objective. We show that this simplification can lead to oscillations if it is applied to thrust vectoring vehicles, with pronounced non-linear actuator effectiveness. When large control objectives are requested or actuators saturate, the linearized effectiveness based CA methods tend to compute a solution far away from the initial actuator state, invalidating the linearization. A potential solution could be to impose limits on the solution domain of the linearized CA algorithm. However, this solution only reduces the oscillations at the expense of a lag in the vehicle acceleration response. To overcome this limitation, we present a fully nonlinear CA method, which uses an Sequential Quadratic Programming (SQP) algorithm to solve the CA problem. The method is tested and implemented on a single board computer that computes the actuator solution in real time onboard a dual axis tilting rotor quad-plane. Flight test experiments confirm the problem of severe oscillations in the linearized effectiveness CA algorithms and show how the only algorithm able to optimally solve the CA problem is the presented Nonlinear method., Control & Simulation

Background: Inequalities in access to stroke care and the workload of physicians have been a challenge in recent times. This may be resolved by allocating physicians suitable for the expected demand. Therefore, this study analyzes whether reallocation using an optimization model reduces disparities in spatial access to healthcare and excessive workload. Methods: This study targeted neuroendovascular specialists and primary stroke centers in Japan and employed an optimization model for reallocating neuroendovascular specialists to reduce the disparity in spatial accessibility to stroke treatment and workload for neuroendovascular specialists in Japan. A two-step floating catchment area method and an inverted two-step floating catchment area method were used to estimate the spatial accessibility and workload of neuroendovascular specialists as a potential crowdedness index. Quadratic programming has been proposed for the reallocation of neuroendovascular specialists. Results: The reallocation of neuroendovascular specialists reduced the disparity in spatial accessibility and the potential crowdedness index. The standard deviation (SD) of the demand-weighted spatial accessibility index improved from 125.625 to 97.625. Simultaneously, the weighted median spatial accessibility index increased from 2.811 to 3.929. Additionally, the SD of the potential crowdedness index for estimating workload disparity decreased from 10,040.36 to 5934.275 after optimization. The sensitivity analysis also showed a similar trend of reducing disparities. Conclusions: The reallocation of neuroendovascular specialists reduced regional disparities in spatial accessibility to healthcare, potential crowdedness index, and disparities between facilities. Our findings contribute to planning health policies to realize equity throughout the healthcare system.

[Resumen]: En el contexto del mundo financiero en constante cambio y complejidad, este trabajo aborda la aplicación de redes neuronales artificiales y programación cuadrática en la gestión de carteras financieras. Se destaca la importancia de caracterizar adecuadamente las series temporales financieras para realizar pronósticos más precisos y se examina el potencial de la combinación de las redes neuronales convolucionales y LSTM para mejorar la previsión de series de tiempo. En el proceso de composición de carteras, se aplica la programación cuadrática como una técnica eficiente para lograr una distribución óptima de activos financieros. En conclusión, el enfoque de combinar redes neuronales artificiales y programación cuadrática muestra promesa en la gestión de carteras financieras, pero es necesario un estudio más profundo y exhaustivo para determinar su eficiencia óptima. Este trabajo sienta las bases para futuras investigaciones, destacando la importancia de utilizar datos actualizados y configurar adecuadamente los modelos para lograr una gestión de carteras más informada y efectiva en un entorno financiero en constante evolución., [Abstract]: In the context of the financial world in constant change and complexity, this work deals with the application of artificial neural networks and quadratic programming in the management of financial portfolios. The importance of properly characterizing financial time series for more accurate forecasting is highlighted, and the potential of combining convolutional neural networks and LSTM to improve time series forecasting is examined. In the portfolio composition process, quadratic programming is applied as an efficient technique to achieve an optimal distribution of financial assets. In conclusion, the approach of combining artificial neural networks and quadratic programming shows promise in the management of financial portfolios, but a deeper and more exhaustive study is necessary to determine its optimal efficiency. This paper lays the groundwork for future research, highlighting the importance of using up-to-date data and properly configuring models to achieve more informed and effective portfolio management in an ever-evolving financial environment., [Resumo]: No contexto do mundo financeiro en constante cambio e complexidade, este traballo trata sobre a aplicación das redes neuronais artificiais e da programación cuadrática na xestión de carteiras financeiras. Destaca a importancia de caracterizar adecuadamente as series temporales financeiras para unha previsión máis precisa e examínase o potencial de combinar redes neuronais convolucionais e LSTM para mellorar a previsión de series temporais. No proceso de composición da carteira aplícase a programación cuadrática como técnica eficiente para conseguir unha distribución óptima dos activos financeiros. En conclusión, o enfoque de combinar redes neuronais artificiais e programación cuadrática resulta prometedor na xestión de carteiras financeiras, pero é necesario un estudo máis profundo e exhaustivo para determinar a súa eficiencia óptima. Este traballo senta as bases para futuras investigacións, destacando a importancia de utilizar datos actualizados e de configurar adecuadamente os modelos para lograr unha xestión de carteira máis informada e eficaz nun entorno financeiro en constante evolución

The level of waste management varies significantly from one EU state to another and therefore they have different starting position regarding reaching defined EU targets. The forecast of waste production and treatment is essential information for the expected future EU targets fulfilment. If waste treatment does not meet the targets under the current conditions, it is necessary to change waste management strategies. This contribution presents a universal approach for forecasting waste production and treatment using optimisation models. The approach is based on the trend analysis with the subsequent data reconciliation (quadratic programming). The presented methodology also provides recommendations to include the quality of trend estimate and significance of territory in form of weights in objective function. The developed approach also allows to put into context different methods of waste handling and production. The variability of forecast is described by prediction and confidence intervals. Within the EU forecast, the expected demographic development is taken into account. The results show that most states will not meet EU targets with current trend of waste management in time. Presented methodology is developed at a general level and it is a suitable basis for strategic planning at the national and transnational level.

In recent years, mobile robots have gained tremendous attention from the entire society: the industry is aiming at selling more intelligent products while the academia is improving their performance from all perspectives. Real world examples include autnomous driving vehicles, multirotors, legged robots, etc. One of the challenging tasks commonly faced by all game players, and all robotics platforms, is to plan motion or locomotion of the robot, calculate an optimal trajectory according to certain criterion and control it accordingly. Difficulty of solving such task usually arises from high-dimensionality and complexity of the system dynamics, fast changing conditions imposed as constraints and necessity for real-time deployment. This work proposes a method over the aforementioned mission by solving an optimal control problem in a receding horizon fashion. Unlike the existing Sequential Linear Quadratic [1] algorithm which is a continuous-time variant of Differential Dynamic Programming [2], we tackle the problem in a discretized multiple shooting fashion. Sequential Quadratic Programming is employed as optimization technique to solve the constrained Nonlinear Programming iteratively. Moreover, we apply trust region method in the sub Quadratic Programming to handle potential indefiniteness of Hessian matrix as well as to improve robustness of the solver. Simulation and benchmark with previous method have been conducted on robotics platforms to show the effectiveness of our solution and superiority under certain circumstances. Experiments have demonstrated that our method is capable of generating trajectories under complicated scenarios where the Hessian matrix contains negative eigenvalues (e.g. obstacle avoidance)., De senaste åren har mobila robotar fått enorm uppmärksamhet från hela samhället: branschen siktar på att sälja mer intelligenta produkter samtidigt som akademin förbättrar sina prestationer ur alla perspektiv. Exempel på verkligheten inkluderar autonoma körande fordon, multirotorer, robotar med ben, etc. En av de utmanande uppgifterna som vanligtvis alla spelare och alla robotplattformar står inför är att planera robotens rörelse eller rörelse, beräkna en optimal bana enligt vissa kriterier och kontrollera det därefter. Svårigheter att lösa en sådan uppgift beror vanligtvis på hög dimensionalitet och komplexitet hos systemdynamiken, snabbt föränderliga villkor som åläggs som begränsningar och nödvändighet för realtidsdistribution. Detta arbete föreslår en metod över det tidigare nämnda uppdraget genom att lösa ett optimalt kontrollproblem på ett vikande horisont. Till skillnad från den befintliga Sequential Linear Quadratic [1] algoritmen som är en kontinuerlig tidsvariant av Differential Dynamic Programming [2], tar vi oss an problemet på ett diskretiserat multipelfotograferingssätt. Sekventiell kvadratisk programmering används som optimeringsteknik för att lösa den begränsade olinjära programmeringen iterativt. Dessutom tillämpar vi trust region-metoden i den sub-kvadratiska programmeringen för att hantera potentiell obestämdhet av hessisk matris samt för att förbättra lösarens robusthet. Simulering och benchmark med tidigare metod har utförts på robotplattformar för att visa effektiviteten hos vår lösning och överlägsenhet under vissa omständigheter. Experiment har visat att vår metod är kapabel att generera banor under komplicerade scenarier där den hessiska matrisen innehåller negativa egenvärden (t.ex. undvikande av hinder).

We develop in this work a new dimension reduction method for high-dimensional settings. The proposed procedure is based on a principal support vector machine framework where principal projections are used in order to overcome the noninvertibility of the covariance matrix. Using a series of equivalences we show that one can accurately recover the central subspace using a projection on a lower dimensional subspace and then applying an ℓ1 penalization strategy to obtain sparse estimators of the sufficient directions. Based next on a desparsified estimator, we provide an inferential procedure for high-dimensional models that allows testing for the importance of variables in determining the sufficient direction. Theoretical properties of the methodology are illustrated and computational advantages are demonstrated with simulated and real data experiments.

The thesis provides design methods for developing Continuous Control SetModel Predictive Control and Linear Quadratic Regulator for Interior mounted Permanent Magnet Synchronous Motor drive. The software for these controllers are implemented in the prototype motor at China Euro Vehicle Technology (CEVT) to estimate the computation time. The performance of these controllers are compared with Field Oriented Control based on Proportional Integral regulator. The comparative analysis for these controllers are performed in Model-in the Loop environment in Simulink developed by the company. It is a back-to-back set up where the test motor is driven by a load motor. The test motor is torque controlled while load motor is speed controlled. The motor is driven by a 2-level Voltage Source Inverter using Space Vector Modulation. The switching frequency of operation is 10 kHz for all controllers. Two variants of Model Predictive Control is discussed in this project. First is the reference tracking predictive control modelled as current controller for the drive. The absence of integral action is verified. Secondly, explicit integral action in Model Predictive Control and Linear Quadratic Regulator is introduced to handle the parameter mismatches. The three controllers, namely Proportional Regulator, integral states Model Predictive Control and Linear Quadratic Regulator where tested for their steady state current and torque tracking accuracy. The steady state ripples on current and torque is compared. The transient performance of the controllers are compared for torque reference changes and load disturbances. Sensitivity of the controllers to parameter mismatches is tested. Finally, ease of tuning these controllers are discussed. The computation time for the individual controllers are obtained by testing it in a microcontroller., Avhandlingen tillhandahåller designmetoder för utveckling av kontinuerlig kontrolluppsättning - modell för prediktiv kontroll och linjär kvadratisk regulator för interiörmonterad synkronmotordrift med permanent magnet. Mjukvaran för dessa styrenheter är implementerad i prototypmotorn hos China Euro Vehicle Technology (CEVT) för att uppskatta beräkningstiden. Dessa regulatorer jämförs i deras prestanda med Field Oriented Control baserad på proportionell integralregulator. Den jämförande analysen för dessa kontroller utförs i Modelin the Loop-miljön i Simulink utvecklad av företaget. Det är en back-to-backuppsättning där testmotorn drivs av en lastmotor. Testmotorn är vridmomentstyrd medan lastmotorn är varvtalsstyrd. Motorn drivs av en spänningskälla med två nivåer som använder Space Vector Modulation. Omkopplingsfrekvensen för driften är 10 kHz för alla regulatorer. Två varianter av Model Predictive Control diskuteras i detta projekt. Först är den prediktiva referensspårningskontrollen modellerad som aktuell styrenhet för frekvensomriktaren. Frånvaron av integrerad åtgärd verifieras. För det andra introduceras explicit integrerad åtgärd i Model Predictive Control och Linear Quadratic Regulator för att hantera parameterfelmatchningar. De tre styrenheterna, nämligen proportionell regulator, inbyggda tillstånd Model Predictive Control och Linear Quadratic Regulator testades med avseende på deras ström- och vridmomentspårningsnoggrannhet. Fortfarande rippel på ström och vridmoment jämförs. Regulatorernas transienta prestanda jämförs för vridmomentreferensändringar och laststörningar. Regulatorernas känslighet för parameterfel har testats. Slutligen diskuteras enkelheten att ställa in dessa kontroller. Beräkningstiden för de enskilda styrenheterna erhålls genom att testa den i en mikrokontroller.

Autonomous systems are slowly moving into the mainstream with things like self driving cars and autonomous robots in storage facilities already in use today. The aim of this project is to simulate a virtual bakery with a baker-robot (agent)that is able to complete recipes within strict deadlines.Signal temporal logic (STL) is used to define instructions that can be understood by the agent. In order to carry out these instructions, a control barrier function (CBF) is used.CBFs are time and state dependent, are used to describe the desired behavior of the agent, and are designer made. If the CBF corresponding to the task is non-negative from beginning to end during the task, the task has been completed successfully.A virtual robot was used in this project and was tasked with moving to and staying in different areas, which represents picking up and dropping off ingredients, all whilst staying within the boundaries of the bakery. The focus of this work is on completing the large amount (10+) of sequential tasks required to completea recipe. The CBF remained positive during the task, and the task was completed successfully., Autonoma system börjar ta mer och mer plats i vardagen med saker som självkörande bilar och autonoma robotar i lagerlokaler som redan används idag. Syftet med det här projektet är att simulera ett virtuellt bageri med en bagarrobot (agent) som kan laga recept under strikta tidskrav. Signal temporal logic (STL) används för att definiera instruktioner som kan förstås av agenten. För att genomföra dessa instruktioner korrekt används en control barrier function (CBF). CBF:er är tidsoch tillståndsberoende, används för att beskriva agentens önskade beteende, och är skapade av en designer. Om CBF:en är positiv från början till slut under uppgiftens gång så har uppgiften genomförts som önskat. En virtuell robot användes i det här projektet och fick i uppdrag att flytta till och stanna inom olika områden, vilket representerar att plocka upp och lämna ingredienser, allt medan den vistas inom bageriets gränser. Fokus för detta arbete ligger på att slutföra den stora mängd (10+) av sekventiella uppgifter sim krävs för att laga ett recept. CBF:en var positiv under hela uppgiften, och uppgiften genomfördes framgångsrikt., Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

In recent years, mobile robots have gained tremendous attention from the entire society: the industry is aiming at selling more intelligent products while the academia is improving their performance from all perspectives. Real world examples include autnomous driving vehicles, multirotors, legged robots, etc. One of the challenging tasks commonly faced by all game players, and all robotics platforms, is to plan motion or locomotion of the robot, calculate an optimal trajectory according to certain criterion and control it accordingly. Difficulty of solving such task usually arises from high-dimensionality and complexity of the system dynamics, fast changing conditions imposed as constraints and necessity for real-time deployment. This work proposes a method over the aforementioned mission by solving an optimal control problem in a receding horizon fashion. Unlike the existing Sequential Linear Quadratic [1] algorithm which is a continuous-time variant of Differential Dynamic Programming [2], we tackle the problem in a discretized multiple shooting fashion. Sequential Quadratic Programming is employed as optimization technique to solve the constrained Nonlinear Programming iteratively. Moreover, we apply trust region method in the sub Quadratic Programming to handle potential indefiniteness of Hessian matrix as well as to improve robustness of the solver. Simulation and benchmark with previous method have been conducted on robotics platforms to show the effectiveness of our solution and superiority under certain circumstances. Experiments have demonstrated that our method is capable of generating trajectories under complicated scenarios where the Hessian matrix contains negative eigenvalues (e.g. obstacle avoidance)., De senaste åren har mobila robotar fått enorm uppmärksamhet från hela samhället: branschen siktar på att sälja mer intelligenta produkter samtidigt som akademin förbättrar sina prestationer ur alla perspektiv. Exempel på verkligheten inkluderar autonoma körande fordon, multirotorer, robotar med ben, etc. En av de utmanande uppgifterna som vanligtvis alla spelare och alla robotplattformar står inför är att planera robotens rörelse eller rörelse, beräkna en optimal bana enligt vissa kriterier och kontrollera det därefter. Svårigheter att lösa en sådan uppgift beror vanligtvis på hög dimensionalitet och komplexitet hos systemdynamiken, snabbt föränderliga villkor som åläggs som begränsningar och nödvändighet för realtidsdistribution. Detta arbete föreslår en metod över det tidigare nämnda uppdraget genom att lösa ett optimalt kontrollproblem på ett vikande horisont. Till skillnad från den befintliga Sequential Linear Quadratic [1] algoritmen som är en kontinuerlig tidsvariant av Differential Dynamic Programming [2], tar vi oss an problemet på ett diskretiserat multipelfotograferingssätt. Sekventiell kvadratisk programmering används som optimeringsteknik för att lösa den begränsade olinjära programmeringen iterativt. Dessutom tillämpar vi trust region-metoden i den sub-kvadratiska programmeringen för att hantera potentiell obestämdhet av hessisk matris samt för att förbättra lösarens robusthet. Simulering och benchmark med tidigare metod har utförts på robotplattformar för att visa effektiviteten hos vår lösning och överlägsenhet under vissa omständigheter. Experiment har visat att vår metod är kapabel att generera banor under komplicerade scenarier där den hessiska matrisen innehåller negativa egenvärden (t.ex. undvikande av hinder).

Autonomous systems are slowly moving into the mainstream with things like self driving cars and autonomous robots in storage facilities already in use today. The aim of this project is to simulate a virtual bakery with a baker-robot (agent)that is able to complete recipes within strict deadlines.Signal temporal logic (STL) is used to define instructions that can be understood by the agent. In order to carry out these instructions, a control barrier function (CBF) is used.CBFs are time and state dependent, are used to describe the desired behavior of the agent, and are designer made. If the CBF corresponding to the task is non-negative from beginning to end during the task, the task has been completed successfully.A virtual robot was used in this project and was tasked with moving to and staying in different areas, which represents picking up and dropping off ingredients, all whilst staying within the boundaries of the bakery. The focus of this work is on completing the large amount (10+) of sequential tasks required to completea recipe. The CBF remained positive during the task, and the task was completed successfully., Autonoma system börjar ta mer och mer plats i vardagen med saker som självkörande bilar och autonoma robotar i lagerlokaler som redan används idag. Syftet med det här projektet är att simulera ett virtuellt bageri med en bagarrobot (agent) som kan laga recept under strikta tidskrav. Signal temporal logic (STL) används för att definiera instruktioner som kan förstås av agenten. För att genomföra dessa instruktioner korrekt används en control barrier function (CBF). CBF:er är tidsoch tillståndsberoende, används för att beskriva agentens önskade beteende, och är skapade av en designer. Om CBF:en är positiv från början till slut under uppgiftens gång så har uppgiften genomförts som önskat. En virtuell robot användes i det här projektet och fick i uppdrag att flytta till och stanna inom olika områden, vilket representerar att plocka upp och lämna ingredienser, allt medan den vistas inom bageriets gränser. Fokus för detta arbete ligger på att slutföra den stora mängd (10+) av sekventiella uppgifter sim krävs för att laga ett recept. CBF:en var positiv under hela uppgiften, och uppgiften genomfördes framgångsrikt., Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

The fuel economy of a hybrid electric vehicle(HEV) is determined by its energy management strategy (EMS), while the conventional EMS usually suffers from enormous computation loads when solving a nonlinear optimization problem. To resolve this issue, this paper presents a computationally efficient EMS with close-to-optimal performance using very limited computation resources. Relying on the optimal solutions by offline dynamic programming (DP), a constrained model predictive control (MPC) can quickly determine the engine on/off status and then the torque split problem is solved by a value-based Pontryagin’s minimum principle (PMP). Two measures are taken to further reduce the online computation cost: by surface fitting, the tabular value function is replaced by piecewise linear polynomials and thus the memory occupation is greatly reduced; and by model approximation, the nonlinear torque split problem becomes a quadratic programming one that can be more rapidly solved. The testing results from processor-in-the-loop (PIL) simulation indicate that the proposed EMS can generate a fuel efficiency close to the one by DP, but saves 70% onboard memory space and 30% CPU utilization compared with the benchmark EMS without taking the two measures., Part of proceedings ISBN 978-1-6654-9043-6QC 20230227

When searching for information in a data collection, we are often interested not only in finding relevant items, but also in assembling a diverse set, so as to explore different concepts that are present in the data. This problem has been researched extensively. However, finding a set of items with minimal pairwise similarities can be computationally challenging, and most existing works striving for quality guarantees assume that item relatedness is measured by a distance function. Given the widespread use of similarity functions in many domains, we believe this to be an important gap in the literature. In this paper we study the problem of finding a diverse set of items, when item relatedness is measured by a similarity function. We formulate the diversification task using a flexible, broadly applicable minimization objective, consisting of the sum of pairwise similarities of the selected items and a relevance penalty term. To find good solutions we adopt a randomized rounding strategy, which is challenging to analyze because of the cardinality constraint present in our formulation. Even though this obstacle can be overcome using dependent rounding, we show that it is possible to obtain provably good solutions using an independent approach, which is faster, simpler to implement and completely parallelizable. Our analysis relies on a novel bound for the ratio of Poisson-Binomial densities, which is of independent interest and has potential implications for other combinatorial-optimization problems. We leverage this result to design an efficient randomized algorithm that provides a lower-order additive approximation guarantee. We validate our method using several benchmark datasets, and show that it consistently outperforms the greedy approaches that are commonly used in the literature., QC 20220930

The thesis provides design methods for developing Continuous Control SetModel Predictive Control and Linear Quadratic Regulator for Interior mounted Permanent Magnet Synchronous Motor drive. The software for these controllers are implemented in the prototype motor at China Euro Vehicle Technology (CEVT) to estimate the computation time. The performance of these controllers are compared with Field Oriented Control based on Proportional Integral regulator. The comparative analysis for these controllers are performed in Model-in the Loop environment in Simulink developed by the company. It is a back-to-back set up where the test motor is driven by a load motor. The test motor is torque controlled while load motor is speed controlled. The motor is driven by a 2-level Voltage Source Inverter using Space Vector Modulation. The switching frequency of operation is 10 kHz for all controllers. Two variants of Model Predictive Control is discussed in this project. First is the reference tracking predictive control modelled as current controller for the drive. The absence of integral action is verified. Secondly, explicit integral action in Model Predictive Control and Linear Quadratic Regulator is introduced to handle the parameter mismatches. The three controllers, namely Proportional Regulator, integral states Model Predictive Control and Linear Quadratic Regulator where tested for their steady state current and torque tracking accuracy. The steady state ripples on current and torque is compared. The transient performance of the controllers are compared for torque reference changes and load disturbances. Sensitivity of the controllers to parameter mismatches is tested. Finally, ease of tuning these controllers are discussed. The computation time for the individual controllers are obtained by testing it in a microcontroller., Avhandlingen tillhandahåller designmetoder för utveckling av kontinuerlig kontrolluppsättning - modell för prediktiv kontroll och linjär kvadratisk regulator för interiörmonterad synkronmotordrift med permanent magnet. Mjukvaran för dessa styrenheter är implementerad i prototypmotorn hos China Euro Vehicle Technology (CEVT) för att uppskatta beräkningstiden. Dessa regulatorer jämförs i deras prestanda med Field Oriented Control baserad på proportionell integralregulator. Den jämförande analysen för dessa kontroller utförs i Modelin the Loop-miljön i Simulink utvecklad av företaget. Det är en back-to-backuppsättning där testmotorn drivs av en lastmotor. Testmotorn är vridmomentstyrd medan lastmotorn är varvtalsstyrd. Motorn drivs av en spänningskälla med två nivåer som använder Space Vector Modulation. Omkopplingsfrekvensen för driften är 10 kHz för alla regulatorer. Två varianter av Model Predictive Control diskuteras i detta projekt. Först är den prediktiva referensspårningskontrollen modellerad som aktuell styrenhet för frekvensomriktaren. Frånvaron av integrerad åtgärd verifieras. För det andra introduceras explicit integrerad åtgärd i Model Predictive Control och Linear Quadratic Regulator för att hantera parameterfelmatchningar. De tre styrenheterna, nämligen proportionell regulator, inbyggda tillstånd Model Predictive Control och Linear Quadratic Regulator testades med avseende på deras ström- och vridmomentspårningsnoggrannhet. Fortfarande rippel på ström och vridmoment jämförs. Regulatorernas transienta prestanda jämförs för vridmomentreferensändringar och laststörningar. Regulatorernas känslighet för parameterfel har testats. Slutligen diskuteras enkelheten att ställa in dessa kontroller. Beräkningstiden för de enskilda styrenheterna erhålls genom att testa den i en mikrokontroller.

The level of waste management varies significantly from one EU state to another and therefore they have different starting position regarding reaching defined EU targets. The forecast of waste production and treatment is essential information for the expected future EU targets fulfilment. If waste treatment does not meet the targets under the current conditions, it is necessary to change waste management strategies. This contribution presents a universal approach for forecasting waste production and treatment using optimisation models. The approach is based on the trend analysis with the subsequent data reconciliation (quadratic programming). The presented methodology also provides recommendations to include the quality of trend estimate and significance of territory in form of weights in objective function. The developed approach also allows to put into context different methods of waste handling and production. The variability of forecast is described by prediction and confidence intervals. Within the EU forecast, the expected demographic development is taken into account. The results show that most states will not meet EU targets with current trend of waste management in time. Presented methodology is developed at a general level and it is a suitable basis for strategic planning at the national and transnational level.

We present a preconditioned interior-point algorithm tailored for input constrained quadratic programmings (QPs) arising in optimal control problems (OCPs). The implicit approach to OCPs results in large sparse QPs, which we utilized by a tailored Riccati recursion algorithm. The Riccati recursion algorithm requires the solution of a set of small dense linear sub-systems of equations. The proposed preconditioner is an easily invertible diagonal matrix, which we apply in every linear sub-system of equations. We solve a target tracking OCP for a linearized modified quadruple tank system in Matlab. The computational results indicate that ill-conditioning in the sub-systems are reduced and that the additional CPU time for preconditioning is negligible. Additionally, the paper presents a detailed description of the proposed algorithm and serves as an implementation guide for the algorithm.

We identify a source of numerical instability of quadratic programming problems that is hidden in its linear equality constraints. We propose a new theoretical approach to rewrite the original optimization problem in an equivalent reformulation using the singular value decomposition and substituting the ill-conditioned original matrix of the restrictions with a suitable optimally conditioned one. The proposed novel approach is showed, both empirically and theoretically, to solve ill-conditioning related numerical issues, not only when they depend on bad scaling and are relative easy to handle, but also when they result from almost collinearity or when numerically rank-deficient matrices are involved. Furthermore, our strategy looks very promising even when additional inequality constraints are considered in the optimization problem, as it occurs in several practical applications. In this framework, even if no closed form solution is available, we show, through empirical evidence, how the equivalent reformulation of the original problem greatly improves the performances of MatLab®’s quadratic programming solver and Gurobi®. The experimental validation is provided through numerical examples performed on real financial data in the portfolio optimization context.

This thesis presents solutions to robust and networked control problems using the semidefinite relaxation of quadratic optimization problems. In the first part, we solve the problem of exact computation of the phase and gain margins of multivariable control systems. These stability margins are studied using the concept of the Davis-Wielandt shell of complex matrices. Calculation of the phase and gain margins requires solving a quadratically constrained quadratic program (QCQP) and a parametrized quadratically constrained problem, respectively, which are known to be difficult in general. The semidefinite relaxation (SDR) technique is often used as a computationally efficient approximation technique to solve QCQPs. It turns out that the QCQPs formulated from the phase and gain margin problems in this thesis fall under the class of quadratic optimization problem, for which the SDR is exact. This is so in the sense that optimal values of the QCQP and its SDR are equal, and an optimal solution for the original QCQP problem can be obtained from an optimal solution of its SDR. Thus, we are able to propose computationally efficient algorithms to compute the phase and gain margins with an arbitrarily high precision. In addition, we define the gain-phase margin of MIMO control systems and propose an algorithm to compute its lower bound. In the second part, we study the networked stabilizability of discrete-time MIMO system over MIMO communication. The communication channel is modelled by a MIMO transceiver, which consists of a transmitter, a receiver and an AWGN MIMO channel. We assume that the MIMO transceiver is subject to transmission power constraints in transmitted signals. The aim is to find a fundamental limitation on the information constraints so as to stabilize the networked control system over MIMO communication. We obtain necessary and sufficient conditions on the predetermined admissible power level for networked stabilizability.

We have developed an automated and efficient scheme for the fitting of data using Curvature Constrained Splines (CCS), to construct accurate two-body potentials. The approach enabled the construction of an oscillation-free, yet flexible, potential. We show that the optimization problem is convex and that it can be reduced to a standard Quadratic Programming (QP) problem. The improvements are demonstrated by the development of a two-body potential for Ne from ab initio data. We also outline possible extensions to the method. Program summary Program Title: CCS CPC Library link to program files: http://dx.doi.org/10.17632/7dt5nzxgbs.1 Developer's repository link:gttp://github.com/aksam432/CCS Licensing provisions: GPLv3 Programming language: Python External routines/libraries: NumPy, matplotlib, ASE, CVXOPT Nature of problem: Ab initio quantum chemistry methods are often computationally very expensive. To alleviate this problem, the development of efficient empirical and semi-empirical methods is necessary. Two-body potentials are ubiquitous in empirical and semi-empirical methods. Solution method: The CCS package provides a new strategy to obtain accurate two body potentials. The potentials are described as cubic splines with curvature constraints.

We have developed an automated and efficient scheme for the fitting of data using Curvature Constrained Splines (CCS), to construct accurate two-body potentials. The approach enabled the construction of an oscillation-free, yet flexible, potential. We show that the optimization problem is convex and that it can be reduced to a standard Quadratic Programming (QP) problem. The improvements are demonstrated by the development of a two-body potential for Ne from ab initio data. We also outline possible extensions to the method. Program summary Program Title: CCS CPC Library link to program files: http://dx.doi.org/10.17632/7dt5nzxgbs.1 Developer’s repository link: http://github.com/aksam432/CCS Licensing provisions: GPLv3 Programming language: Python External routines/libraries: NumPy, matplotlib, ASE, CVXOPT Nature of problem: Ab initio quantum chemistry methods are often computationally very expensive. To alleviate this problem, the development of efficient empirical and semi-empirical methods is necessary. Two-body potentials are ubiquitous in empirical and semi-empirical methods. Solution method: The CCS package provides a new strategy to obtain accurate two body potentials. The potentials are described as cubic splines with curvature constraints.

Superconducting (SC) magnets for accelerator concepts are often synthesized by numerically optimizing magnetic field waveforms, a process that requires a subsequent solution of a constrained inverse problem to identify suitable SC magnet windings. When the desired field distribution is intuitive, the inverse process is facilitated by seeding preconceived coil distributions into design optimization methods for refinement. With more complex magnetic field distributions, an initial design may be unknown, and topology optimization tools are required to synthesize current distributions without a priori guidance from a subject matter expert. In this work, we develop a constrained inverse Biot-Savart topology optimization methodology that synthesizes optimal distributions of current density in racetrack-like SC coils. The problem structure is exploited through a computationally efficient quadratic programming formulation, and the method is applied to recently published magnetic field waveforms for a recirculating proton phase shifter, a proton therapy gantry, and dipole magnets with sharp field transitions. The method and results herein identify novel winding configurations that can help magnet designers bring accelerator concepts to fruition.

Superconducting (SC) magnets for accelerator concepts are often synthesized by numerically optimizing magnetic field waveforms, a process that requires a subsequent solution of a constrained inverse problem to identify suitable SC magnet windings. When the desired field distribution is intuitive, the inverse process is facilitated by seeding preconceived coil distributions into design optimization methods for refinement. With more complex magnetic field distributions, an initial design may be unknown, and topology optimization tools are required to synthesize current distributions without a priori guidance from a subject matter expert. In this work, we develop a constrained inverse Biot-Savart topology optimization methodology that synthesizes optimal distributions of current density in racetrack-like SC coils. The problem structure is exploited through a computationally efficient quadratic programming formulation, and the method is applied to recently published magnetic field waveforms for a recirculating proton phase shifter, a proton therapy gantry, and dipole magnets with sharp field transitions. The method and results herein identify novel winding configurations that can help magnet designers bring accelerator concepts to fruition.

This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.

Superconducting (SC) magnets for accelerator concepts are often synthesized by numerically optimizing magnetic field waveforms, a process that requires a subsequent solution of a constrained inverse problem to identify suitable SC magnet windings. When the desired field distribution is intuitive, the inverse process is facilitated by seeding preconceived coil distributions into design optimization methods for refinement. With more complex magnetic field distributions, an initial design may be unknown, and topology optimization tools are required to synthesize current distributions without a priori guidance from a subject matter expert. In this work, we develop a constrained inverse Biot-Savart topology optimization methodology that synthesizes optimal distributions of current density in racetrack-like SC coils. The problem structure is exploited through a computationally efficient quadratic programming formulation, and the method is applied to recently published magnetic field waveforms for a recirculating proton phase shifter, a proton therapy gantry, and dipole magnets with sharp field transitions. The method and results herein identify novel winding configurations that can help magnet designers bring accelerator concepts to fruition.

This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.

Numerical optimization solve problems efficiently where such efficiency is focused on the speed with which the optimal x* is achieved, is open line of research and strong work in the scientific community in order to achieve control systems in dynamic processes with response times of the order of milliseconds. A clear example of this, is the implementation of optimal controller’s combustion engines. For subsequent approach to the design and implementation of nonlinear model predictive control controllers, it has made a comparison of yields algorithms quadratic programming by active set with linearization restrictions, and sequential quadratic programming with single shooting technique to solve quadratic optimization problem formulation referred to a dynamic internal combustion engine of spark ignition, in embedded systems with real-time processing. © 2016, Springer-Verlag France.

Numerical optimization solve problems efficiently where such efficiency is focused on the speed with which the optimal x* is achieved, is open line of research and strong work in the scientific community in order to achieve control systems in dynamic processes with response times of the order of milliseconds. A clear example of this, is the implementation of optimal controller’s combustion engines. For subsequent approach to the design and implementation of nonlinear model predictive control controllers, it has made a comparison of yields algorithms quadratic programming by active set with linearization restrictions, and sequential quadratic programming with single shooting technique to solve quadratic optimization problem formulation referred to a dynamic internal combustion engine of spark ignition, in embedded systems with real-time processing. © 2016, Springer-Verlag France.

Numerical optimization solve problems efficiently where such efficiency is focused on the speed with which the optimal x* is achieved, is open line of research and strong work in the scientific community in order to achieve control systems in dynamic processes with response times of the order of milliseconds. A clear example of this, is the implementation of optimal controller’s combustion engines. For subsequent approach to the design and implementation of nonlinear model predictive control controllers, it has made a comparison of yields algorithms quadratic programming by active set with linearization restrictions, and sequential quadratic programming with single shooting technique to solve quadratic optimization problem formulation referred to a dynamic internal combustion engine of spark ignition, in embedded systems with real-time processing. © 2016, Springer-Verlag France.

We have developed an automated and efficient scheme for the fitting of data using Curvature Constrained Splines (CCS), to construct accurate two-body potentials. The approach enabled the construction of an oscillation-free, yet flexible, potential. We show that the optimization problem is convex and that it can be reduced to a standard Quadratic Programming (QP) problem. The improvements are demonstrated by the development of a two-body potential for Ne from ab initio data. We also outline possible extensions to the method. Program summary Program Title: CCS CPC Library link to program files: http://dx.doi.org/10.17632/7dt5nzxgbs.1 Developer’s repository link: http://github.com/aksam432/CCS Licensing provisions: GPLv3 Programming language: Python External routines/libraries: NumPy, matplotlib, ASE, CVXOPT Nature of problem: Ab initio quantum chemistry methods are often computationally very expensive. To alleviate this problem, the development of efficient empirical and semi-empirical methods is necessary. Two-body potentials are ubiquitous in empirical and semi-empirical methods. Solution method: The CCS package provides a new strategy to obtain accurate two body potentials. The potentials are described as cubic splines with curvature constraints.

The electric engine cooling system with the active grille shutter requires intelligent and predictive control to reach its full benefits on fuel economy and thermal management. Conventional control methods regulate the coolant temperature to a fixed value but do not directly minimize the vehicle's fuel/energy consumption. By contrast, we design a fuel minimization controller through solving constraint nonlinear optimization problems, whose cost function is the total fuel consumption and constraints are the vehicle's physical limits. To achieve high computational efficiency and sufficient accuracy, the optimization problem is solved by iterative convex quadratic programming and quasilinearization. The advantages of the proposed control method on both fuel economy and engine thermal management are demonstrated by simulations., QC 20200424, CONVENIENT, XPRES

The finite control set-model predictive control (FCS MPC) has been adopted as an excellent choice for the applications of multilevel converters during the last two decades for its salient performance. However, in the case of modular multilevel converters (MMCs), a high amount of calculation is always involved in the implementation, making the FCS-MPC less suitable especially for an MMC with a high number of submodules. To cope with the issue, this article proposes an MPC technique for the MMC with a very low calculation cost. In each sampling period, the arm voltage references of each phase are determined analytically by solving a constrained quadratic programming problem formulated from the cost function. Both a rigorous and simplified procedure is provided to solve the optimization problem. Then, the four nearest candi dates around the arm voltage references are evaluated, leading to a proper selection of arm voltage levels. Several experimental tests on an MMC prototype are carried out to validate the effectiveness of the proposed method. Results show that compared with the con ventional FCS-MPC method which evaluates all voltage-level com binations, the proposed scheme presents an apparent advantage in terms of calculation cost while achieving similar performance.

The finite control set-model predictive control (FCS MPC) has been adopted as an excellent choice for the applications of multilevel converters during the last two decades for its salient performance. However, in the case of modular multilevel converters (MMCs), a high amount of calculation is always involved in the implementation, making the FCS-MPC less suitable especially for an MMC with a high number of submodules. To cope with the issue, this article proposes an MPC technique for the MMC with a very low calculation cost. In each sampling period, the arm voltage references of each phase are determined analytically by solving a constrained quadratic programming problem formulated from the cost function. Both a rigorous and simplified procedure is provided to solve the optimization problem. Then, the four nearest candi dates around the arm voltage references are evaluated, leading to a proper selection of arm voltage levels. Several experimental tests on an MMC prototype are carried out to validate the effectiveness of the proposed method. Results show that compared with the con ventional FCS-MPC method which evaluates all voltage-level com binations, the proposed scheme presents an apparent advantage in terms of calculation cost while achieving similar performance.

The maneuvering control of autonomous vessels has been under extensive investigations by academic and industrial communities since it is one of the primary steps towards enabling unmanned shipping. In this paper, a model predictive control (MPC) approach is presented for trajectory tracking control of vessels which takes into account the thrust allocation (TA) problem in the presence of rotatable thrusters. In this approach, the TA problem is formulated over a finite horizon and solved with regard to the power consumption, changes in the angle and speed of actuators, and the operating constraints. In the proposed control approach, several linearization techniques have been employed to enable the adoption of quadratic programming approaches for solving the MPC's and TA's optimization problems. The performance of the proposed approach is evaluated through several simulation experiments using a replica vessel model., Transport Engineering and Logistics

In this paper we consider Model Predictive Control (MPC) design for roll angle control for a Fixed-Wing Unmanned Aerial Vehicle (UAV) with Multiple Segmented Control Surfaces. The challenge of roll angle control for a Fixed-Wing UAV consists of switching between inner and outer aileron-pairs with hard constraints due to safety, energy saving and switching actuators. The novelty consists of formulating a hybrid-control problem as a switched linear constrained MPC-QP problem and switched state observer design for Fixed-Wing UAV. A fast novel QP-solver based on the active-set QP-solver Hildreth is developed to meet the real-time implementation target, which is to stay below the sampling time of Ts = 10 ms. The designed MPC controllers are simulated using Matlab. Simulations and the CPU-time from the improved QP-solvers show MPC to be a very good choice for real-time roll angle control for Fixed-Wing UAVs., In this paper we consider model predictive control (MPC) design for roll angle control for a fixed-wing unmanned aerial vehicle (UAV) with multiple segmented control surfaces. The challenge of roll angle control for a fixed-wing UAV consists of switching between inner and outer aileron pairs with hard constraints due to safety, energy saving and switching actuators. The novelty consists of formulating a hybrid control problem as a switched linear constrained MPC-QP problem and switched state observer design for fixed-wing UAV. A fast novel QP-solver based on the active-set QP-solver Hildreth is developed to meet the real-time implementation sampling time of Ts = 10 ms. The designed MPC controllers are simulated using Matlab. Simulations and the CPU-time from the improved QP-solvers show MPC to be a very good solution for real-time roll angle control of fixed-wing UAVs.

Water and nitrogen are fundamental inputs for the development of any agricultural crop, and when they are correlated with the production obtained, there is an analytical function that roughly represents the production in relation to water and nitrogen. Considering that in the agribusiness and decision-making environment, it is important to know quantitatively the maximum production and maximum net revenue that a given crop generates in the case where these inputs are limited, in this work we present a computational procedure using duality theory and logarithmic barrier method to solve both problems. To evaluate the presented methodology, we performed some numerical tests based on data known for the cultures, melon, american lettuce, orange-pear and oats. Based on the results obtained, we may conclude that the presented methodology constitutes a reliable alternative, considering the results known in the literature for such cultures, Água e nitrogênio são insumos fundamentais para o desenvolvimento de qualquer cultura agrícola, e quando estão correlacionados com a produção obtida, há uma função analítica que representa aproximadamente a produção em relação à água e ao nitrogênio. Considerando que no ambiente do agronegócio e de tomada de decisão, é importante conhecer quantitativamente a produção máxima e a receita líquida máxima que uma determinada safra gera no caso em que esses insumos são limitados, neste trabalho, apresentamos um procedimento computacional usando a teoria da dualidade e o método da barreira logarítmica para resolver ambos os problemas. Para avaliar a metodologia apresentada, realizamos alguns testes numéricos com base em dados conhecidos para as culturas do melão, da alface americana, pêra laranja e aveia. Com base nos resultados obtidos, podemos concluir que a metodologia apresentada constitui uma alternativa confiável, considerando os resultados conhecidos na literatura para tais culturas.Palavras chave: Água, nitrogênio, dualidade, barreira logarítmica, programação quadrática.

The finite control set-model predictive control (FCS MPC) has been adopted as an excellent choice for the applications of multilevel converters during the last two decades for its salient performance. However, in the case of modular multilevel converters (MMCs), a high amount of calculation is always involved in the implementation, making the FCS-MPC less suitable especially for an MMC with a high number of submodules. To cope with the issue, this article proposes an MPC technique for the MMC with a very low calculation cost. In each sampling period, the arm voltage references of each phase are determined analytically by solving a constrained quadratic programming problem formulated from the cost function. Both a rigorous and simplified procedure is provided to solve the optimization problem. Then, the four nearest candi dates around the arm voltage references are evaluated, leading to a proper selection of arm voltage levels. Several experimental tests on an MMC prototype are carried out to validate the effectiveness of the proposed method. Results show that compared with the con ventional FCS-MPC method which evaluates all voltage-level com binations, the proposed scheme presents an apparent advantage in terms of calculation cost while achieving similar performance.

We consider the problem of reconstructing a complex-valued signal from its phase-only measurements. This framework can be considered as a generalization of the well-known one-bit compressed sensing paradigm where the underlying signal is known to be sparse. In contrast, the proposed formalism does not rely on the assumption of sparsity and hence applies to a broader class of signals. The optimization problem for signal reconstruction is formulated by first splitting the linear measurement vector into its phase and magnitude components and subsequently using the non-negativity property of the magnitude component as a constraint. The resulting optimization problem turns out to be a quadratic program (QP) and is solved using two algorithms: (i) alternating directions method of multipliers; and (ii) projected gradient-descent with Nesterov's momentum. Due to the inherent scale ambiguity of the phase-only measurement, the underlying signal can be reconstructed only up to a global scale-factor. We obtain high accuracy for reconstructing 1-D synthetic signals in the absence of noise. We also show an application of the proposed approach in reconstructing images from the phase of their measurement coefficients. The underlying image is recovered up to a peak signal-to-noise ratio exceeding 30 dB in several examples, indicating an accurate reconstruction., QC 20210322

The finite control set-model predictive control (FCS MPC) has been adopted as an excellent choice for the applications of multilevel converters during the last two decades for its salient performance. However, in the case of modular multilevel converters (MMCs), a high amount of calculation is always involved in the implementation, making the FCS-MPC less suitable especially for an MMC with a high number of submodules. To cope with the issue, this article proposes an MPC technique for the MMC with a very low calculation cost. In each sampling period, the arm voltage references of each phase are determined analytically by solving a constrained quadratic programming problem formulated from the cost function. Both a rigorous and simplified procedure is provided to solve the optimization problem. Then, the four nearest candi dates around the arm voltage references are evaluated, leading to a proper selection of arm voltage levels. Several experimental tests on an MMC prototype are carried out to validate the effectiveness of the proposed method. Results show that compared with the con ventional FCS-MPC method which evaluates all voltage-level com binations, the proposed scheme presents an apparent advantage in terms of calculation cost while achieving similar performance.

The finite control set-model predictive control (FCS MPC) has been adopted as an excellent choice for the applications of multilevel converters during the last two decades for its salient performance. However, in the case of modular multilevel converters (MMCs), a high amount of calculation is always involved in the implementation, making the FCS-MPC less suitable especially for an MMC with a high number of submodules. To cope with the issue, this article proposes an MPC technique for the MMC with a very low calculation cost. In each sampling period, the arm voltage references of each phase are determined analytically by solving a constrained quadratic programming problem formulated from the cost function. Both a rigorous and simplified procedure is provided to solve the optimization problem. Then, the four nearest candi dates around the arm voltage references are evaluated, leading to a proper selection of arm voltage levels. Several experimental tests on an MMC prototype are carried out to validate the effectiveness of the proposed method. Results show that compared with the con ventional FCS-MPC method which evaluates all voltage-level com binations, the proposed scheme presents an apparent advantage in terms of calculation cost while achieving similar performance.

Water and nitrogen are fundamental inputs for the development of any agricultural crop, and when they are correlated with the production obtained, there is an analytical function that roughly represents the production in relation to water and nitrogen. Considering that in the agribusiness and decision-making environment, it is important to know quantitatively the maximum production and maximum net revenue that a given crop generates in the case where these inputs are limited, in this work we present a computational procedure using duality theory and logarithmic barrier method to solve both problems. To evaluate the presented methodology, we performed some numerical tests based on data known for the cultures, melon, american lettuce, orange-pear and oats. Based on the results obtained, we may conclude that the presented methodology constitutes a reliable alternative, considering the results known in the literature for such cultures, Água e nitrogênio são insumos fundamentais para o desenvolvimento de qualquer cultura agrícola, e quando estão correlacionados com a produção obtida, há uma função analítica que representa aproximadamente a produção em relação à água e ao nitrogênio. Considerando que no ambiente do agronegócio e de tomada de decisão, é importante conhecer quantitativamente a produção máxima e a receita líquida máxima que uma determinada safra gera no caso em que esses insumos são limitados, neste trabalho, apresentamos um procedimento computacional usando a teoria da dualidade e o método da barreira logarítmica para resolver ambos os problemas. Para avaliar a metodologia apresentada, realizamos alguns testes numéricos com base em dados conhecidos para as culturas do melão, da alface americana, pêra laranja e aveia. Com base nos resultados obtidos, podemos concluir que a metodologia apresentada constitui uma alternativa confiável, considerando os resultados conhecidos na literatura para tais culturas.Palavras chave: Água, nitrogênio, dualidade, barreira logarítmica, programação quadrática.

Water and nitrogen are fundamental inputs for the development of any agricultural crop, and when they are correlated with the production obtained, there is an analytical function that roughly represents the production in relation to water and nitrogen. Considering that in the agribusiness and decision-making environment, it is important to know quantitatively the maximum production and maximum net revenue that a given crop generates in the case where these inputs are limited, in this work we present a computational procedure using duality theory and logarithmic barrier method to solve both problems. To evaluate the presented methodology, we performed some numerical tests based on data known for the cultures, melon, american lettuce, orange-pear and oats. Based on the results obtained, we may conclude that the presented methodology constitutes a reliable alternative, considering the results known in the literature for such cultures, Água e nitrogênio são insumos fundamentais para o desenvolvimento de qualquer cultura agrícola, e quando estão correlacionados com a produção obtida, há uma função analítica que representa aproximadamente a produção em relação à água e ao nitrogênio. Considerando que no ambiente do agronegócio e de tomada de decisão, é importante conhecer quantitativamente a produção máxima e a receita líquida máxima que uma determinada safra gera no caso em que esses insumos são limitados, neste trabalho, apresentamos um procedimento computacional usando a teoria da dualidade e o método da barreira logarítmica para resolver ambos os problemas. Para avaliar a metodologia apresentada, realizamos alguns testes numéricos com base em dados conhecidos para as culturas do melão, da alface americana, pêra laranja e aveia. Com base nos resultados obtidos, podemos concluir que a metodologia apresentada constitui uma alternativa confiável, considerando os resultados conhecidos na literatura para tais culturas.Palavras chave: Água, nitrogênio, dualidade, barreira logarítmica, programação quadrática.

In this paper we consider Model Predictive Control (MPC) design for roll angle control for a Fixed-Wing Unmanned Aerial Vehicle (UAV) with Multiple Segmented Control Surfaces. The challenge of roll angle control for a Fixed-Wing UAV consists of switching between inner and outer aileron-pairs with hard constraints due to safety, energy saving and switching actuators. The novelty consists of formulating a hybrid-control problem as a switched linear constrained MPC-QP problem and switched state observer design for Fixed-Wing UAV. A fast novel QP-solver based on the active-set QP-solver Hildreth is developed to meet the real-time implementation target, which is to stay below the sampling time of Ts = 10 ms. The designed MPC controllers are simulated using Matlab. Simulations and the CPU-time from the improved QP-solvers show MPC to be a very good choice for real-time roll angle control for Fixed-Wing UAVs., In this paper we consider model predictive control (MPC) design for roll angle control for a fixed-wing unmanned aerial vehicle (UAV) with multiple segmented control surfaces. The challenge of roll angle control for a fixed-wing UAV consists of switching between inner and outer aileron pairs with hard constraints due to safety, energy saving and switching actuators. The novelty consists of formulating a hybrid control problem as a switched linear constrained MPC-QP problem and switched state observer design for fixed-wing UAV. A fast novel QP-solver based on the active-set QP-solver Hildreth is developed to meet the real-time implementation sampling time of Ts = 10 ms. The designed MPC controllers are simulated using Matlab. Simulations and the CPU-time from the improved QP-solvers show MPC to be a very good solution for real-time roll angle control of fixed-wing UAVs.

The maneuvering control of autonomous vessels has been under extensive investigations by academic and industrial communities since it is one of the primary steps towards enabling unmanned shipping. In this paper, a model predictive control (MPC) approach is presented for trajectory tracking control of vessels which takes into account the thrust allocation (TA) problem in the presence of rotatable thrusters. In this approach, the TA problem is formulated over a finite horizon and solved with regard to the power consumption, changes in the angle and speed of actuators, and the operating constraints. In the proposed control approach, several linearization techniques have been employed to enable the adoption of quadratic programming approaches for solving the MPC's and TA's optimization problems. The performance of the proposed approach is evaluated through several simulation experiments using a replica vessel model., Transport Engineering and Logistics