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The subject of the paper is the mathematical model to manage the logistics-maintenance process of overhead type cranes operated in critical systems. The study case of the paper is a hot rolling mills system of a steel plant with critical overhead cranes operating with hazard conditions and continuous operation. The model output is an optimal overhead cranes maintenance-task distribution minimizing the production line risk stopped and the model input is a digital database with historical worker-orders related with the operation, maintenance-logistics and management process of the hot rolling mills plant. The mathematical model behind the maintenance task making-decision process is a stochastic no-linear optimization model with bounded constraint that evaluates a risk global-system behaviour indicator based on Monte Carlo simulations. The definition and description volume of work in this investigation is extensive, reason why the current paper focusses the attention on model definition and dataflow concepts, specifically data collection, filtering, analysis, and storage from different sources, leaving open the application and numerical results for future works.

The aim of this paper is to investigate three species Lotka-Volterra system with the immigration and harvesting effect. This work verifies the parametric conditions for the existence of the non-negative equilibrium points and its asymptotic stability. First, the sign stability of the system is analyzed through signed digraphs associated with the Jacobian matrix. Second, the asymptotic stability of the system is obtained by attaining the suitable parameters which are satisfying the Routh-Hurwitz stability criterion. In this model, the focus is mainly on the role of the immigration effect of every species. The paper is concluded with the immigration effect that supports to stabilize the unstable system and how the numerical result provides the guarantee to achieve the theoretical results.

The topic of this paper is to solve two types of problems in controlled system dynamics formulated in terms of vector-valued criteria whose application depends on the type of ordering for the scalar participants of each such criterion. The first problem is that of optimal control under vector criterion with ordering of the Pareto type. The problem is to indicate the dynamics of the Pareto front. The second is that of finding vector-valued controls of the minmax type. Here the internal problem of dynamic maximization is due to a vector criterion with given type of ordering while the external problem is that of vector-valued dynamic minimization under another type of ordering. A similar situation arises for controls of the maxmin type. The paper indicates a variety of solution formulas that describe vector-valued dynamic interrelations for the problems of minmax and maxmin. The solutions are reached by using the Hamiltonian formalism. The suggested vector type of control problem settings are motivated by structure of system dynamics for physical motions, economics, finance, environmental models and related issues. Examples of applications are indicated.

It is a well-established fact that an unstable zero may appear when a minimum-phase continuous-time system is discretized by a zero-order hold. Therefore, a feedforward controller cannot be obtained based on the inverse system; this is because it becomes unstable. Herein, an exact linearization method for a continuous-time system with a pulse-width modulation-type (PWM-type) input has been proposed showing that unstable zeros of the linearized discrete-time system can be moved to the stable region by altering the pulse-centers location. This enables the construction of a stable feedforward controller that achieves an output perfect tracking control. However, the current paper shows a trade-off between the stability of the stabilized zero and the maximum pulse-width. This prevents the unstable zero from moving to a high stability region, and shows some oscillation at the output of the feedforward controller. To address this, a zero-phase error filter has been introduced to reduce the oscillation. Further, a nonlinear deadbeat controller is also proposed, which can be applied to second and higher order systems. The effectiveness of the proposed methods are presented in this paper by performing simulations.

This paper presents a dynamic output-feedback stabilization problem of descriptor Markovian jump systems with generally uncertain transition rates. First, a new necessary and sufficient condition to relax inequalities including generally uncertain transition rates is introduced. For the closed-loop systems with a dynamic output-feedback controller, the stabilization criterion is achieved as non-convex matrix inequalities. For the obtained criterion, this paper gives an improved necessary and sufficient condition in terms of linear matrix inequalities under completely known transition rates. Then, the proposed condition is extended for the descriptor Markovian jump systems with generally uncertain transition rates. To show the validity of the proposed control, a numerical example is given.

In Industry 4.0 era, rapid changes to the global landscape of manufacturing are transforming industrial plants in increasingly more complex digital systems. One of the most impactful innovations generated in this context is the “Digital Twin”, a digital copy of a physical asset, which is used to perform simulations, health predictions and life cycle management through the use of a synchronized data flow in the manufacturing plant. In this paper, an innovative approach is proposed in order to contribute to the current collection of applications of Digital Twin in manufacturing: a Digital Shadow cloud-based application to enhance quality control in the manufacturing process. In particular, the proposal comprises a Digital Shadow updated on high performance computing cloud infrastructure in order to recompute the performance prediction adopting a variation of the computer-aided engineering model shaped like the actual manufactured part. Thus, this methodology could make possible the qualification of even not compliant parts, and so shift the focus from the compliance to tolerance requirements to the compliance to usage requirements. The process is demonstrated adopting two examples: the structural assessment of the geometry of a shaft and the one of a simplified turbine blade. Moreover, the paper presents a discussion about the implications of the use of such a technology in the manufacturing context in terms of real-time implementation in a manufacturing line and lifecycle management.

This paper presents a performance optimization methodology for the discrete-time sliding mode control (SMC) with the decoupled disturbance compensator (DDC) and the auxiliary state (AS) for an industrial position control system under control input saturation and disturbance. The discrete-time SMC with DDC and AS (SDA) method prevents windup phenomena in the switching function and the disturbance estimation error. Therefore, it provides robust performance under control input saturation, disturbances, as well as parametric uncertainties. However, it is difficult to relate several design parameters to the desired performance. In this paper, a systematical design framework for the discrete-time SDA method is developed. Based on the phase portrait of the error state, the error can be made to converge to zero with a fast speed and less oscillation under control input saturation by an offline tuning process. An optimization process is performed in terms of the peak value of the control input. Both numerical simulations and experimental results show the effectiveness of the developed tuning methodology for the discrete-time SDA method.

One of the main issues related to integral control is windup, which occurs when, possibly due to a fault, the input signal u of the plant reaches a value outside the allowed input range U. This paper presents an integral controller with anti-windup, called saturating integrator, for a single-input single-output nonlinear plant having a curve of locally exponentially stable equilibrium points that correspond to constant inputs in U. A closed-loop system is formed by connecting the saturating integrator in feedback with the plant. The control objective is to make the output signal y of the plant track a constant reference r, while not allowing its input signal u to leave U. Using singular perturbation methods, we prove that, under reasonable assumptions, the equilibrium point of the closed-loop system is exponentially stable, with a “large” region of attraction. Moreover, when the state of the closed-loop system converges to this equilibrium point, then the tracking error tends to zero. A step-by-step procedure is presented to perform the closed-loop stability analysis, by finding separately a Lyapunov function for the reduced (slow) model and a Lyapunov function for the boundary-layer (fast) system. Afterwards, a Lyapunov function for the closed-loop system is built as a convex combination of the two previous ones, and an upper bound on the controller gain is found such that closed-loop stability is guaranteed. Finally, we show that if certain stronger conditions hold, then the domain of attraction of the stable equilibrium point of the closed-loop system can be made large by choosing a small controller gain.

In this paper, the problem of detection and reconstruction of cyber-attacks in linear cyber-physical systems is considered. The class of cyber-attacks described in this paper can corrupt the states or the outputs of a cyber-physical system. An attack monitor based on High-Order sliding mode is proposed to reconstruct the cyber-attack. A First Order Approximation Filter is proposed to ensure global stability and convergence results. Using sliding mode techniques, an attack compensation is developed for square plants, guarateeing finite time convergence to the output tracking while rejecting the effects of the cyber-attack.

This paper deals with the design of static pre- and post- compensators to transform stable, non-square LTI systems into the class of strongly strict negative imaginary systems. The pre-compensator plays the role of a control allocator while the post-compensator does sensor blending in order to make a non-square system square along with satisfying the strongly strict negative imaginary property. A specific structure of the post-compensator is also given that guarantees a feasible solution of the LMI conditions when applied to systems with number of outputs greater than or equal to number of inputs. The proposed pre- and post- compensators can also stabilize a non-square plant in closed-loop upon satisfying a particular DC-gain condition and furthermore, they can be utilized to develop a simple constant input tracking framework for non-square systems. The LMI-based design methodology offers a numerically tractable solution framework and hence the easy implementation of the proposed scheme in practical applications. Illustrative examples are provided throughout the paper to demonstrate the usefulness of the proposed results in widening the scope of the negative imaginary theory to non-square LTI systems (e.g. safety-critical systems having redundant sensors and actuators).

For highly precise motion of a galvanometer scanner that tracks a periodic motion reference, learning control significantly decreases the tracking error. To achieve higher quality motion by reducing the angular sensor noise, this paper investigates inversion-based iterative control (IIC) that can learn only at the fundamental and harmonic frequencies of the periodic motion reference. This enables to separate the compensable tracking error from the noise to be eliminated during learning in the frequency domain. The analysis in the paper reveals a tradeoff for the noise reduction in the IIC design, and this paper proposes an equation to quickly tune a design parameter in the tradeoff for better performance. Furthermore, the effectiveness of the IIC algorithm is experimentally demonstrated for a galvanometer scanner. When the galvanometer scanner tracks a 20 Hz triangular motion of ± 10 degrees, the IIC successfully decreases the residual tracking error by 41 % to 2.83 ×10-4 deg, by utilizing the noise reduction.

The paper discusses fuzzy comparison matrices, consistency check, weight prioritization methods and weight evaluation methods in fuzzy group analytic hierarchy process. There are various methods of weight prioritization, however, they are not critically evaluated. In the paper, two measures are introduced for the evaluation of the group weights. Then, a new method is proposed to improve the process of deriving weights and use it in an application compared to another common three methods. Our results show that the new method is a good method for deriving weights of indexes.

Short-term power load forecasting plays a significant role in power system security management. The prediction model in this paper is the grey wolf optimization algorithm to optimize kernel extreme learning machine (GWO-KELM). First, the Kalman filter is used to reduce the noise for the random noise interference existing in the power load data. Then determine the input and output of the prediction model. In this paper, the ELM model of three different kernel functions is used for comparative experiments, and the mean absolute percentage error is used as the evaluation model index. It is concluded from the experimental results that the GWO-KELM model used in this article has the advantages of high prediction accuracy and strong generalization ability, so it is practicable to apply the model to short-term electric load forecasting.

In this paper, a project oriented course on guidance and control of autonomous aerial vehicles (UAVs) is presented. The paper describes the different modules of the course and how they are addressed using the project oriented approach. The project uses a quadrotor UAV that is used as the case study during the course. In this course, the students learn how to mathematically model quadrotor UAV flight characteristics, develop feedback control algorithms to enable stable flight control and fuse sensor measurements using Kalman filter techniques to estimate the UAV position and orientation. Students develop these concepts through both simulation and interaction with UAV real measurements. Throughout the course, students build a full 6-degree-of-freedom simulation of controlled quadrotor UAV flight using MATLAB and Simulink.

This paper considers the problem of network overloading in the power distribution networks of Pakistan, often resulting from the inability of the transmission system to transfer power from source to end-user during peak loads. This results in frequent power-outages and consumers at such times have to rely on alternative energy sources, e.g. Uninterrupted Power Supply (UPS) systems with batteries to meet their basic demand. In this paper, we propose a demand response framework to eliminate the problem of network overloading. The flexibility provided by the batteries at different houses connected to the same grid node is exploited by scheduling the flow of power from mains and batteries and altering the charging-discharging patterns of the batteries, thereby avoiding network overloading and any tripping of the grid node. This is achieved by casting the problem in an optimal control setting based on a prediction of power demand at a grid node and then solving it using a model predictive control strategy. We present a case study to demonstrate the application and efficacy of our proposed framework. Copyright (C) 2020 The Authors.

The speed with which an ego-vehicle follows a lead vehicle through traffic can significantly affect the former’s fuel consumption, safety, average speed, and ride comfort. This paper merges these objectives and constraints into a unified trajectory optimization problem. One of the paper’s goals is to provide a unified formulation of problems traditionally tackled independently, e.g., platooning, fuel-minimizing vehicle speed trajectory optimization, etc. Another key goal is to demonstrate the degree to which Differential Dynamic Programming (DDP) provides a conceptually attractive and computationally inexpensive decomposition of the resulting multi-objective problem. In this decomposition, perturbations from an optimal steady-state vehicle trajectory are controlled using a linear quadratic regulation (LQR) law obtained analytically through DDP. We examine the performance of this controller simulating a representative urban drive cycle with a lead vehicle. We also perform a sensitivity study on the parameters in the objective and explore their effect in both fuel economy and deviations from nominal headway distance. Finally, we explore the effect of different levels of collaboration between vehicles by assuming the lead vehicle shares its predicted future average acceleration.

The presented paper deals with the area of research on multibody mechanical systems focused on underactuated systems, where the impact of the missing actuator is compensated by means of mathematical modeling. Several approaches based on the dynamics of individual bodies make it possible to obtain the mathematical model of the mechanical system. This paper focuses on summarizing and comparing Lagrange’s and Kane’s method, describing the steps that need to be applied to obtain a model for a particular mechanical system in each case. The methods are subsequently applied to selected benchmark underactuated systems such as cart-pole, acrobot, and reaction wheel pendulum, where the aim is to show that both produce the same equations of motion. The corresponding procedures are implemented in MATLAB as part of a custom application with graphical user interface.

This paper addresses the problem of Static Output Feedback (SOF) stabilization for continuous-time linear systems subject to norm-bounded parameter uncertainties. Usually this issue leads to the feasibility of a Bilinear Matrix Inequality (BMI), which is difficult to linearize to get non conservative Linear matrix inequality (LMI) conditions. In this paper, by means of some technical lemmas, we transform the BMI into a new LMI with a line search over two scalar variables. The obtained LMI conditions are less conservative than those existing in the literature. Numerical evaluations are presented to show the superiority of the proposed method.

This paper presents an observer based nonlinear control for a flexible beam clamped on a rotating inertia. The considered model is composed by a set of Partial Differential Equations (PDEs) interconnected with an Ordinary Differential Equation (ODE), with control input in the ODE. The control problem consists in orienting the beam at the desired position, maintaining the flexible vibrations as low as possible. To this end, it is presented a nonlinear controller that depends on the beam’s state. An Observer is designed to reconstruct the infinite dimensional state, and the estimated state is used in the nonlinear controller instead of the real one. Assuming well-posedness of the closed loop system, it is shown the exponential convergence of the estimated state, and the asymptotic stability of the closed loop system. Numerical simulations are presented to characterize the closed loop behaviour with different choices of observer’s parameters.

This paper proposes a topology identification and impedance estimation (TIIE) method for the distribution network with distributed generation (DG) units. When the DG unit is connected to the distribution grid, the power injections between the different buses in the distribution grid are no longer independent of each other. This paper demonstrates that the topology identification result has to be corrected when the existing voltage measured based methods are applied to the distribution network involving DG units. To solve the problem, we develop the TIIE method to correct the topology identification error by using the estimation of line impedance. The proposed method does not require any prior knowledge of the network. The case results show a high accuracy on the connectivity identification as well as the estimation of line parameter.

Meteorology plays a key role in the reliability of power equipment and the operation of power system while the meteorological information that we get isn’t accuracy. Many experts put forward different methods to solve this problem and these methods have made some achievements. Another angle is found in this paper, the system of micrometeorological data collection and application (SMDCA) is designed to solve this problem. There are three levels in the system, including micrometeorology collection, data transmission, data application. The SMDCA is based on internet of things for power systems (ITPS) in this paper. And it not only can collect micrometeorological information but also have the ITPS’s function of intelligent interconnection. The SMDCA’s applications in intelligent interconnection are designed and reviewed detailly. And it’s confirmed that the application of SMDCA in ITPS has a good effect on the accuracy of related calculation in power system.

The two-tank problem is often considered as a challenging benchmark problem of process control, owing to its non-linear nature and non-minimum phase behavior. Non-linearity arises due to the dependence of non-linear outlet flow associated with the tank level. Hence, it is significant to investigate the dynamics of the system. This paper utilizes a novel idea of transforming the non-linear Stochastic Differential Equations (SDEs) to an appropriate form of the SDEs that preserves non-linear effects. In this paper, first, the Carleman linearization technique is explored to arrive at the bi-linearized two-tank SDEs. Then, we utilize the Fokker-Planck equation for the estimation of the bi-linearized two-tank problem. The theoretical results corroborated with numerical simulations highlight the effectiveness of the proposed Carleman linearization-based estimation method in contrast to the benchmark EKF-prediction method, i.e. without observation.

In this paper, a BP neural network and an LSTM network are applied respectively to the prediction of Coronavirus Disease 2019 (COVID-19) in Wuhan, China and South Korea. The methods do not require specific theories of modelling and the predicted values can be obtained as long as the conventional parameters are set. The mean absolute percentage error (MAPE) of all the experiments are below 5% and the values of the determinable coefficient R2 are all larger than 0.9. The experiments show that the models can fit the actual values well and make relatively accurate predictions. As of March 29, 2020, the cumulative number of confirmed cases in Wuhan is expected to reach 50,068 using BP neural networks and 49,972 using LSTM network, respectively. As of April 13, 2020, the cumulative number of confirmed cases in South Korea is expected to reach 8,862 using BP neural networks and 8,716 using LSTM network, respectively. The models of neural networks are effective in predicting the trend of the COVID-19 epidemic, which is meaningful to prevent and control the epidemic.

Fast convergent, accurate, computationally efficient, parallelizable, and robust matrix inversion and parameter estimation algorithms are required in many time-critical and accuracy-critical applications such as system identification, signal and image processing, network and big data analysis, machine learning and in many others. This paper introduces new composite power series expansion with optionally chosen rates (which can be calculated simultaneously on parallel units with different computational capacities) for further convergence rate improvement of high order Newton-Schulz iteration. New expansion was integrated into the Richardson iteration and resulted in significant convergence rate improvement. The improvement is quantified via explicit transient models for estimation errors and by simulations. In addition, the recursive and computationally efficient version of the combination of Richardson iteration and Newton-Schulz iteration with composite expansion is developed for simultaneous calculations. Moreover, unified factorization is developed in this paper in the form of tool-kit for power series expansion, which results in a new family of computationally efficient Newton-Schulz algorithms.

This paper presents a distributed set-membership estimator for linear full-coupled systems affected by bounded disturbances. The estimator makes use of a recently developed multi-hop subspace decomposition of the system that allows to transform the dynamic matrix into an upper triangular matrix, decoupling the influence of the non-observable modes to the observable ones. This way, each agent has to compute sets to encompass the observable dynamics on the one hand, and the unobservable dynamics on the other. The sets are mathematically described by zonotopes. Due to the multi-hop decomposition, the agents are able to design different gains for the observable and the unobservable part, pursuing the reduction in volume of the corresponding sets. The paper presents the solution for the two-agents case. Simulations are given to compare the proposed solution with existing ones in the field.

This paper deals with a topology for a class of interconnected systems, referred to as a highly interconnected system, consisting of interconnected plants and local controllers. We address the respective cyber attack surfaces as well as a design approach for detection and isolation of covert cyber attacks. For each pair of plant and controller, a cyber attack is implemented by a malicious agent, and its detection and isolation are achieved by associating the controller with two observers. These observers estimate the states of the plant, and compare the estimated states to determine if a neighbouring plant is under a covert cyber attack. The paper presents the modelling of the topology, the analysis of the covertness of cyber attacks, the design approach for the detection and isolation as well as a required existence condition. Simulation results are provided for the application of the design approach to interconnected pendula systems that are subject to a covert cyber attack.

This paper presents an incompressible particle flow filtering method that does not require an auxiliary filter by estimating log-density gradients directly from particles. Particle flow filter (PFF) is likely to avoid particle impoverishment and degeneracy problems that occur in particle filters because particles themselves move toward desired density to perform measurement updates. There are various implementation forms for PFF depending on the assumptions made about the flow. This paper deals with PFF using incompressible flow. Incompressible PFF requires the log-density gradient to calculate the flow. The well-known gradient estimation method for incompressible PFF is a finite difference method collaborating with k nearest neighbors(kNN) method. Since this method requires the prior knowledge about the prior density value in each particle, it is necessary to use an auxiliary filter or a density estimation technique. As a result, the performance of an auxiliary filter or a density estimation technique can directly affect the PFF performance, and the finite difference method is more likely to be inaccurate than directly estimating the log-density gradient. Therefore, this paper presents a PFF structure applying least-squares log-density gradient (LSLDG) method that estimates the log-density gradient directly from particles. In order to verify the performance of the presented structure, this paper performs both single and multiple target tracking simulations. Simulation results demonstrate that the presented structure has a relatively good estimation performance and works more robustly for various situations.

Anti-angiogenic drugs are relatively new tools in cancer therapy with very low side-effects compared to earlier approaches. These drugs inhibit the formation of new blood vessels in the tumor, thus cutting its cells from nutrient supply. As proliferating tumor cells have very intensive metabolism, the lack of nutrients provides a significant barrier for the growth rate of the tumor. In the recent years it has been shown that the dosage protocol of these drugs may be critical in terms of their efficiency. In addition, several papers are considering model-based methods for therapy optimization and potential feedback control of tumor growth, based on the application of anti-angiogenic drugs. In this paper we use the framework of reaction kinetic systems to formulate simple models for important mechanisms present in vascular tumor growth, and analyze their identifiability properties assuming various plausible measurable variables. The conclusions of the article may contribute to experiment design regarding identification of such and similar models.

General problems of optimal trajectory generation and of optimal space-time rendezvous for autonomous underwater vehicles affected by time-varying fluid flows are formulated and solved in the framework of dynamic programming. The optimal solutions include optimal trajectories, as well as departure times and positions. The approach consists in using the principle of optimality (PO) to embed, for example, an optimal time to reach a target problem from some fixed position and time into a more general problem of finding the optimal time to reach a target from any point and time. The solution of this general problem is given by the value function, the solution of a Hamilton-Jacobi-Bellman equation (HJBE) which expresses the PO in an infinitesimal form. The HJBE is solved using an efficient parallel numerical solver. The problems of interest are solved either by minimizing the value function over one or more variables (e.g., time) or by using level sets of the value function to coordinate departure times for multiple vehicles to rendezvous at a given target. The paper presents a description and an illustration of the approach and briefly discusses how value-function-based calculations provide a very effective way to solve complex motion planning and coordination problems. The discussion is aided by examples modeling real operational scenarios using current velocity forecasts from a state-of-the-art model of the Sado river estuary in Portugal.

Fuzzy logic system is commonly used in wind turbine fault detection. However, traditional fuzzy logic systems are built through human experience. The fuzzy logic system constructed in this way will have inaccurate problems. In order to solve this problem, this paper proposes a novel fuzzy logic system (FLS) based on neural network construction method to improve accuracy rate of wind turbine fault detection. First, a neural network construction method is proposed. Using this method, membership function can be constructed more accurately. Then, a FLS based on the extended data-driven membership function is proposed. When environment changes, such FLS can improve accuracy rate of wind turbine fault detection with the extended data-driven membership function. Finally, experiments using data from actual wind fields are performed, and the experimental results show that the method proposed in this paper is effective.

Nozzle clogging contributes heavily to quality issues seen during the process of continuous casting. The presence of clogging in the Submerged Entry Nozzle (SEN) can significantly change the flow patterns in the mould and therefore impact the quality of the steel product. Also, there is a high risk of inclusions due to parts of the clogging material breaking off and entering the mould. In this paper, we propose a new sensor setup that allows us to detect clogging in the SEN by monitoring the angle of the exiting jet. Based on this clog detection setup, a switched MPC controller is used to keep the angle of the exiting jet between the optimum ranges using an Electromagnetic Brake. This allows the controller to keep the angle of the jet in the optimum range even when clogging occurs in the nozzle. Experimental data from a laboratory scale continuous caster is used to derive the models for the controller.

This paper addresses the design of a parallel compensator and a stabilizing controller for the simplified crystal growth dynamics of the Czochralski (CZ) process, i.e., the process for the production of monocrystalline silicon ingots of uniform diameter. The diameter control of the produced ingots is achieved by a CCD camera measurement used to sense the radius of the boundary between the base of the growing crystal and the surrounding glowing meniscus — a raised melt surface connecting the crystal ingot with the flatter melt surface. Due to the intrinsic nature of the process, the bright ring radius measurement signal exhibits a non-minimum phase behaviour. A combination of the parallel compensator and a stabilizing controller is designed, such that the former provides for the mitigation of non-minimum phase behaviour, while the latter combined with the formal yields a suitable closed-loop performance.

Aeromechanical instabilities arising due to frequency coalescence of different modes of a helicopter rotor and fuselage system is investigated. The focus is on removing the small angle approximation typically used for ground resonance analysis and perform simulation of the resulting nonlinear model. The results reveal significantly different characteristics (existence of limit cycles) for the nonlinear model compared to linear model that is commonly used. These differences are primarily (though not necessarily) restricted to regions of frequency coalescence of more than one degrees of freedom present in the system. The paper does numerical simulations of different simplified models of coupled-rotor-fuselage systems used in the past by researchers and show that the nonlinear behavior is fundamental to the system. The results in the paper stand out because, in the past, limit cycle oscillations for coupled-rotor-fuselage systems were shown to exist only with nonlinearity associated with stiffness or damping parameter. The paper clearly shows that nonlinearity inherent in the lead-lag and flap equations will result in limit cycle oscillations and this is also demonstrated through experimental studies.

The paper presents a small-scale electric vehicle framework for vehicle control education and research. The main goal of the project is to serve as a good experimental platform for the students on any level of vehicle mechatronics education. It offers wide range of possibilities for embedded system, control design and machine learning applications. The proposed system is a redesigned version of our former experimental vehicle framework. The project was scheduled for two main goals. One was to design and implement a Robot Operating System based vehicle with Ackermann steering. The other was to develop a platform, which can be highly integrated into vehicle control education. In the paper the system architecture, the sensors and the control units are detailed, furthermore the educational benefits and some use cases are presented.

It is well-known that students learn substantially more if a lecture is complemented by active inquiry-based activities and problem solving than from only passively listening to a lecture. Naturally, this requires the teacher to include elements into the course such as laboratories, group projects, tutorials and interactive e-learning modules. For the most effective teaching concept, all such components of the course work are tightly intertwined and work together towards the same goal and learning objectives. Therefore, we show in this paper how Matlab apps can be included into the wider effort of learning facilitation in an introductory automatic control course. Among other advantages, Matlab apps can offer an unlimited number of exercises and examples that can be generated automatically, they offer interactive interfaces that can include gamification aspects such as levels and highscore lists to increase the motivation, and they can be done at the students’ own pace. The course evaluation and the feedback from students at the University of Stuttgart suggest that the inclusion of the Matlab apps presented in this paper is indeed a success.

This paper addresses the problem of controlling an Autonomous Surface Vehicle (ASV) in rough sea-states, with a view towards minimising wave-induced forces, whilst maintaining headway. This is a challenging control application since, and as is derived in the paper, the interaction between the vessel and the wave disturbance is nonlinear and coupled. This subsequently motivates the novel application of the Real Time Iteration Scheme (RTI) for Nonlinear Model Predictive Control (NMPC) of the ASV. Analysis of the resulting control signal provides an important insight into the role of the wave encounter frequency. Specifically, by actuating at twice the average wave encounter frequency, the nonlinear controller is able to reduce the wave forces, compared to an open-loop controller that achieves the same average velocity.

This paper extends off-policy reinforcement learning to the multi-agent case in which a set of networked agents communicating with their neighbors according to a time-varying graph collaboratively evaluates and improves a target policy while following a distinct behavior policy. To this end, the paper develops a multi-agent version of emphatic temporal difference learning for off-policy policy evaluation, and proves convergence under linear function approximation. The paper then leverages this result, in conjunction with a novel multi-agent off-policy policy gradient theorem and recent work in both multi-agent on-policy and single-agent off-policy actor-critic methods, to develop and give convergence guarantees for a new multi-agent off-policy actor-critic algorithm. An empirical validation of these theoretical results is given.

In this paper, we address the problem of nonlinear continuous-discrete adaptive observer design for a class of system with sampled data measurements subject to sensor nonlinearities. The main difficulty of the considered class of system is coming from the fact that the output equation contains unknown parameters which renders the design of a classical sampled data observer difficult. To overcome this difficulty, we propose a new online continuous-discrete adaptive observer which ensures a simultaneous exponential convergence of both states and parameters. Comparing to other observer structures, our design is characterized by a simpler structure thanks to the introduction of a parametric adaptation law. To show the efficiency of our proposed approach, numerical simulations have been performed for different values of sampling time. In addition, the delayed-sampled measurements case is also illustrated.

In contrast to a vector non-linear stochastic differential equation (SDE) describing the satellite dynamics under the ‘fluctuating aerodynamic torque’, this paper analyses a second-order fluctuation equation for the radial perturbation about the given orbit. The second-order fluctuation equation for the radial perturbation has found its application for the satellite orbital stability. After accomplishing a phase space formulation, we arrive at the two-dimensional SDE. Most notably, the inaccurate choice of stochastic integral describing the satellite stochastic dynamics will have influence on their estimation, stability and control. For this reason, we develop a noise equation of the satellite dynamics in the Stratonovich setting. The satellite dynamics in the Stratonovich sense can be expressed equivalently in the Ito setting by accounting additional correction terms in the system non-linearity term of the SDE. This paper develops the estimation theory of satellite dynamics via the Stratonovich calculus. The analytic findings are useful to the trajectory estimation of the orbiting satellite under the influence of atmospheric dust perturbations, where the observations are not available.

The adoption of fractional calculus in control systems has enabled the synthesis of new controllers with fractional-order derivatives and integrals. Several optimization-based methods for tuning of linear fractional-order controllers have been explored. However, few have considered the stability of the closed-loop system during optimization. This paper presents a model-driven method for tuning of fractional-order controllers based on a heuristic optimization technique and the experimental use of Nyquist’s stability criterion to enforce closed-loop stability of fractional-order systems. The proposed frequency domain tuning method enables tuning of linear fractional-order controllers with few to medium number of parameters. The method can handle both fractional-order linear and integer-order linear plant models and controllers. To assist the experimental use of Nyquist’s stability criterion, a function for drawing a Logarithmic amplitude polar diagram has been developed. Simulation results of the method applied to a nanopositioning system in atomic force microscopy suggest that the proposed method can be used for optimization of fractional-order controllers while enforcing closed-loop stability. Given that the system can be stabilized with the given controller. Matlab code building on the FOTF toolbox and global optimization toolbox is provided.

A load frequency control (LFC) scheme for modern power systems employs an open communication network to transmit control/measurement signals. The usage of the open communication network in power system makes the LFC scheme more vulnerable to communication network attacks, for example, a denial-of-service (DoS) attack. This attack will prevent a certain amount of transmission of the signals so as to degrade the performance of LFC scheme, or even lead to instability of power system. Therefore, this paper concerns LFC scheme for single-area power system with energy-limited DoS attack based on switching method. Compared with the latest schemes only considering duration of a DoS attacks, the proposed LFC takes fully into account both the duration and attack frequency of the DoS attack. A switching LFC model for a single-area power system is firstly constructed based on the characteristic of periodical sampling and DoS attacks. Then, an exponential stability criterion is developed in terms of the duration and frequency of a DoS attack. Next, a feasible controller of LFC scheme is derived by solving linear matrix inequalities in the criterion. Finally, a detailed design algorithm of LFC scheme is presented for a known DoS attack or an unknown DoS attack. The effectiveness of the proposed LFC scheme is evaluated on a single-area power system under DoS attacks. The proposed LFC scheme is compared with a robust LFC scheme and an event-triggered H∞ LFC scheme. The obtained contrasting results demonstrate that the proposed LFC scheme can defend DoS attacks with limited energy, and can perform better control effect in the presence of load fluctuations.

This paper investigates the extended state observer (ESO) based friction compensation at low velocities with only the position measurement. ESO is an effective model-free friction compensation technique and thus is employed in this paper. Based on the describing function analysis, it is revealed that the higher the observer bandwidth is, the larger the velocity feedback gain should be to suppress the limit cycle. However, the available damping provided by the derivative control is restricted when the signal-to-noise ratio of the velocity is low. Under such a condition, the observer bandwidth cannot be high and the friction compensation performance is thus limited. To solve this conflict, a switching control law based on the ESO is proposed to compensate the friction in a fast manner and suppress the limit cycle simultaneously. The switching strategy aims to determine when the disturbance compensation should be added in the control signal to eliminate the friction induced oscillations without making the system response become sluggish. Such an idea is enlightened by the fact that nonlinear modifications to the integral action are always needed in practice. Hardware experiments are performed on a brushless DC motor to validate the effectiveness of the proposed compensation scheme.

This paper studies the driver and context changes during the operation of a hybrid electric vehicle (HEV) and their influence on fuel consumption. Firstly, a context estimation model to recognize driving styles is developed based on machine learning techniques, for which a realistic scenario with simulation of urban mobility (SUMO) and car modeling platform (IPG Carmaker) integration is designed. Secondly, a novel context-aware control strategy based on model predictive control with extended prediction self-adaptive control (MPC-EPSAC) strategy is proposed. The control objective is to achieve optimal torque-split distribution, while optimizing fuel consumption in the parallel HEV. The simulation results suggest that an improvement in fuel economy can be achieved when the driving style in the control loop is adequately considered. 3rd IFAC Workshop on Cyber-Physical & Human Systems CPHS 2020 keywords: Context aware control, Context estimation, Machine learning, Human-in-the-loop control, Cyber physical systems, Model predictive control (MPC), Self-optimization ispartof: pages:768-773 ispartof: IFAC PAPERSONLINE vol:53 issue:5 pages:768-773 ispartof: 3rd IFAC Workshop pn Cyber-Physical and Human Systems (CPHS) location:PEOPLES R CHINA, Beijing date:3 Dec - 5 Dec 2020 status: published

The paper contributes to developing algorithms for motion planning and motion control for mechanical systems with two and more passive degrees of freedom by exploring a challenging example in details. As shown, some of arguments of motion planning methods developed for systems of underactuation degree one can be generalized for novel demanding settings, while corresponding arguments and concepts for controller design should be substantially reconsidered and updated. Rigorous theoretical results are well supported by numerical studies.

In this paper, we introduce a learning approach for the controller structure in coalitional model predictive control (MPC) schemes. In this context, the local control entities can dynamically perform in a decentralized manner or assemble into groups of controllers that coordinate their control actions, i.e., coalitions. Such control strategy aims at maximizing system performance while reducing the coordination and computation burden. In this paper, we pose a multi-armed bandit problem where the arms are a set of possible controller structures and the player performs as a supervisory layer that can periodically change the composition of the coalitions. The goal is to use real-time observations to progressively learn the controller structure that best suits the needs of the system. A heuristic learning algorithm and illustrative results are provided.

We present in this paper a timed discrete event model for system identification with the aim of fault detection, called Timed Automaton with Outputs and Conditional Transitions (TAOCT). The TAOCT is an extension of a recent untimed model proposed in the literature, called Deterministic Automaton with Outputs and Conditional Transitions (DAOCT). Differently from the DAOCT, where only the logical behavior of the discrete event system is considered, the TAOCT takes into account information about the time that the events are observed, and, for this reason, it can be used for the detection of faults that cannot be detected by using untimed models, such as faults that lead the fault detector to deadlocks. The TAOCT represents the fault-free system behavior, and a fault is detected when the observed behavior is different from the behavior predicted by the model, considering both logical and timing information. A practical example is used to illustrate the results of the paper.

The SARS-Cov-2 is a type of coronavirus that has caused the COVID-19 pandemic. In traditional epidemiological models such as SEIR (Susceptible, Exposed, Infected, Removed), the exposed group E does not infect the susceptible group S. A distinguishing feature of COVID-19 is that, unlike with previous viruses, there is a distinct “asymptomatic” group A, who do not show any symptoms, but can nevertheless infect others, at the same rate as infected patients. This situation is captured in a model known as SAIR (Susceptible, Asymptomatic, Infected, Removed), introduced in Robinson and Stilianakis (2013). The dynamical behavior of the SAIR model is quite different from that of the SEIR model. In this paper, we use Lyapunov theory to establish the global asymptotic stabiilty of the SAIR model. Next, we present methods for estimating the parameters in the SAIR model. We apply these estimation methods to data from several countries including India, and show that the predicted trajectories of the disease closely match actual data.

This paper presents an implementable framework of output probability density function (PDF) control for a class of stochastic nonlinear systems which are subjected to non-Gaussian noises. The statistical properties of the system outputs can be adjusted by shaping the dynamic output probability density function to track the reference stochastic distribution. However, the dynamic probability density function evolution is very difficult to obtain analytically even if the system model and the stochastic distributions of the noises are known. Motivated by Monte Carlo simulation, the dynamic probability density function can be estimated by sampling data which forms the contribution of this paper. In particular, the sampling points are generated following the stochastic distribution of the noise for each instant. These points go through the system and generate the histogram for system outputs, then the dynamic model can be established based on the dynamic histogram which reflects the randomness and the nonlinear dynamics of the investigated system. Based on the established model, the output probability density function tracking can be achieved and the simulation results and discussions show the effectiveness and benefits of the presented framework.

There has been considerable focus on building inclusion and diversity into engineering careers and education, especially in developed countries, but despite this, the percentages for minorities remain unchanged over decades. The multi-cultural interaction of TECIS was a springboard for the launch of a working group to investigate the reasons for this lack of improvement. The genesis of this working group occurred in Sozopol, Bulgaria at the TECIS 2019 conference where twenty-three researchers from over ten countries came together to discuss the lack of women and other marginalized groups in engineering. The objective of this paper is threefold, to outline the future direction of the inclusion and diversity working group in TECIS, to support and foster greater knowledge of gender diversity in engineering education and to outline future research activities that could make a substantial contribution to our understanding of diversity issues in engineering in addition to making best practice recommendations that can be used in the engineering industry. The scope of this paper is limited to women in engineering. Future work will look at other inclusion and diversity issues in STEM.

Tensioned cable nets can be used as supporting structures for the efficient construction of lightweight building elements, such as thin concrete shell structures. To guarantee important mechanical properties of the latter, the tolerances on deviations of the tensioned cable net geometry from the desired target form are very tight. Therefore, the form needs to be readjusted on the construction site. In order to employ model-based optimization techniques, the precise identification of important uncertain model parameters of the cable net system is required. This paper proposes the use of Gaussian process regression to learn the function that maps the cable net geometry to the uncertain parameters. In contrast to previously proposed methods, this approach requires only a single form measurement for the identification of the cable net model parameters. This is beneficial since measurements of the cable net form on the construction site are very expensive. For the training of the Gaussian processes, simulated data is efficiently computed via convex programming. The effectiveness of the proposed method and the impact of the precise identification of the parameters on the form of the cable net are demonstrated in numerical experiments on a quarter-scale prototype of a roof structure., IFAC-PapersOnLine, 53 (2), ISSN:2405-8963, 21th IFAC World Congress. Proceedings