Search

We present a computational framework for stability analysis of systems of coupled linear Partial-Differential Equations (PDEs). The class of PDE systems considered in this paper includes parabolic, elliptic and hyperbolic systems with Dirichlet, Neuman and mixed boundary conditions. The results in this paper apply to systems with a single spatial variable. We exploit a new concept of state for PDE systems which allows us to include the boundary conditions directly in the dynamics of the PDE. The resulting algorithms are implemented in Matlab, tested on several motivating and illustrative examples, and the codes have been posted online. Numerical testing indicates the approach has little or no conservatism for a large class of systems and can analyze systems of up to 20 coupled PDEs.

Frequency-domain system representations offer important system analysis, control design and simulation tools. While this representation concept is well developed for linear time-invariant systems, there has been no general theory established for the linear parameter-varying (LPV) system class. This paper overviews and compares the existing concepts of frozen and instantaneous frequency-domain LPV representations, pointing out the need for a representation form where the effect of the spectrum of a varying scheduling trajectory can be clearly understood and analyzed on the output spectrum. For this purpose, the harmonic frequency response function (hFRF), known from the linear time-varying literature, in introduced for LPV systems. More specifically the paper demonstrates how the hFRF can be computed directly in the frequency domain for discrete-time affine input-output LPV systems.

Identifiability of linear dynamic networks typically depends on the presence and location of external (excitation or disturbance) signals, in relation to the topology of the parametrized network model set. For closed-loop identification, it is known that switching (non-parameterized) controllers can also provide excitation, thereby rendering the model set identifiable. In this paper, we derive verifiable conditions for network identifiability of the non-switching modules in presence of (non-parameterized) switching modules. These conditions generalize the classical result in closed-loop identification towards network identification. Furthermore, verifiable path-based conditions for identifiability in a generic sense are developed on the graph of the network model set.

The current limitation in the synthesis of distributed H2 controllers for linear interconnected systems is scalability due to non-convex or unstructured synthesis conditions. In this paper we develop convex and structured conditions for the existence of a distributed H2 controller for discrete-time interconnected systems with an interconnection structure that corresponds to an arbitrary graph. Neutral interconnections and a storage function with a block-diagonal structure are utilized to attain coupling conditions that are of a considerably lower computational complexity compared to the corresponding centralized H2 controller synthesis problem. The effectiveness and scalability of the developed distributed H2 controller synthesis method is demonstrated for small- to large-scale oscillator networks on a cycle graph.

text, 学術雑誌論文 / Journal Article, 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.

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.

We propose a non-linear model predictive scheme for planning fuel efficient maneuvers of small spacecrafts that shall rendezvous space debris. The paper addresses the specific issues of potential limited on-board computational capabilities and low-thrust actuators in the chasing spacecraft, and solves them by using a novel MatLab-based toolbox for real-time non-linear model predictive control (MPC) called MATMPC. This tool computes the MPC rendezvous maneuvering solution in a numerically efficient way, and this allows to greatly extend the prediction horizon length. This implies that the overall MPC scheme can compute solutions that account for the long time-scales that usually characterize the low-thrust rendezvous maneuvers. The so-developed controller is then tested in a realistic scenario that includes all the near-Earth environmental disturbances. We thus show, through numerical simulations, that this MPC method can successfully be used to perform a fuel-efficient rendezvous maneuver with an uncontrolled object, plus evaluate performance indexes such as mission duration, fuel consumption, and robustness against sensor and process noises © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

This paper proposes the use of the extent decomposition on homogeneous reaction systems for control purposes. The decomposition results in a Linear Parameter-Varying (LPV) representation, upon which parametric feedback and feedforward strategies are developed. In the first part of the paper, three different ways to obtain the Extent-Based LPV (ELPV) representation of the system are proposed. The representation is advantageous since the physical meaning of all the variables are kept, and it has a Jordan type of structure which is used to establish controllability conditions. In the second part, general parametric feedback and feedforward control laws are proposed for the ELPV system. The nonlinear state-parameter dependence is first considered in the feedback term. This fact allows converting the original ELPV system into a Linear Time Invariant (LTI) system, which is used to design optimal control laws for reference tracking. Finally, the performance of the control strategy for the ELPV system is illustrated in simulation and compared with a controller based on a constant-parameter LTI model (ELTI).

In this paper, we discuss the problem of efficient fluid mixing which is tackled by means of (approximate) dynamic programming from a switched systems perspective. In current practice, typically pre-determined periodic mixing protocols are used. As we will show in this paper, feedback control can be used to improve mixing significantly. To make this control problem tractable, temporal and spatial discretization is used by means of the cell-mapping method on the original infinite-dimensional fluid models. This translates the original control problem into the design of a (sub)optimal switching law that determines discrete mixing actions for a discrete-time switched linear system. Exploiting this switched systems perspective, a novel feedback law for mixing fluids is proposed inspired by suboptimal rollout policies in dynamic programming contexts. By design, this feedback law for mixing guarantees a performance improvement over any given (open-loop) periodic mixing protocol. This new design methodology is validated by means of simulations for the benchmark journal bearing flow showing improved performance with respect to periodic mixing strategies.

In recent years, Unmanned Aerial Vehicles (UAVs) have become feasible solutions to various applications such as military flight missions, search and rescue operations, and geological mapping. The attitude control systems for UAVs need to be very robust to ensure safe and stable flights. To improve the stability beyond conventional control methods, a novel Model Predictive Control (MPC) formulation is proposed. In this paper, the mathematical derivation of the MPC controller is discussed. The novelty lies in the three-termed MPC cost function for attitude flight stability. The control strategy introduced in this paper is validated with experiments on a hexacopter in a custom fabricated experimental rig and with outdoor flight tests. The results show that the UAV is able to follow the operator's aggressive manoeuvring commands. Based on the data obtained, the MPC controller appears to be a promising control method to improve UAVs’ performance.

Iterative Learning Control (ILC) can significantly improve performance of systems that perform repeating tasks. Although in practice many systems are multivariable, frequency-domain ILC design procedures often involve multi-loop single-input single-output (SISO) filters which do not explicitly address interaction in dynamics. The aim of this paper is to i) analyze multi-loop SISO ILC designs, ii) point out the importance of multivariable ILC design, and iii) develop the required multivariable design algorithms. Benefits of the proposed approaches over multi-loop SISO ILC are demonstrated on an industrial printer model example., Iterative Learning Control (ILC) can signi cantly improve performance of systems that perform repeating tasks. Although in practice many systems are multivariable, frequency-domain ILC design procedures often involve multi-loop single-input single-output (SISO) fi lters which do not explicitly address interaction in dynamics. The aim of this paper is to i) analyze multi-loop SISO ILC designs, ii) point out the importance of multivariable ILC design, and iii) develop the required multivariable design algorithms. Bene ts of the proposed approaches over multi-loop SISO ILC are demonstrated on an industrial printer model example.

In this paper, a systematic procedure to determine the equivalence factor in the equivalent consumption minimisation strategy (ECMS) is proposed. This is relevant when ECMS is not only used for controlling the power split between the internal combustion engine and the electric machine of a hybrid electric vehicle (HEV), but also for controlling several auxiliary systems. In this case, the number of controlled components and energy buffers increases, which causes the number of tunable equivalence factors to increase. The procedure to determine the equivalence factors proposed in this paper is based on the observation that ECMS can be considered as a time-invariant feedback policy and dynamic programming (DP) also yields a time-invariant feedback policy when the time horizon of the control problem approaches infinity and the disturbances are constant or absent. As the drive cycle can be considered as a (stationary stochastic) disturbance, we propose to formulate ECMS as the solution of a 1-step look-ahead stochastic dynamic program (1slSDP). This strategy results in an energy management strategy that performs close to optimal and yields similar fuel consumption results, when compared to a well-tuned ECMS. The absence of any parameters to be tuned and the fact that fuel consumption is similar to a well-tuned ECMS makes 1slSDP a useful strategy for energy management of HEVs.

In this paper, an integrated approach for designing energy management strategies concerning vehicle auxiliaries with on/off control is proposed. This approach provides the possibility of making different trade-offs between fuel economy and switching frequency. In this paper, we demonstrate the validity of this approach by using game theory for obtaining the online implementable energy management strategy while the design philosophy can be generally applied to other existing energy management strategies. A case study is carried out on a model of a heavy-duty truck with an electric power take off (ePTO) connected to a refrigerated semitrailer for which the cooling unit can only be switched on or off. The results show that different trade-offs can be made between fuel savings and switching frequency.

Many global identification approaches described in the literature for estimating linear parameter-varying (LPV) discrete-time state-space (SS) models with affine dependence on the scheduling parameter suffer heavily from the curse of dimensionality, making identification of moderate sized systems computationally intensive or infeasible. In this paper, we present a novel two-step approach to estimate LPV-SS models based on a single data set with varying scheduling signal by combining 1) LPV correlation analysis, and 2) a deterministic LPV realization scheme. Step 1 includes the estimation of the sub-Markov parameters of the system using correlation analysis of the involved signals. Subsequently, for Step 2, this paper presents a novel basis reduced exact Ho-Kalman like realization scheme, which uses only sub parts of the extended Hankel matrix. Therefore, the computational complexity is significantly reduced compared to the full scheme. To demonstrate that the basis reduction does not lead to a loss in performance, a simulation study is provided.

This paper presents a Matlab toolbox that implements methods for computing stabilizing terminal costs and sets for nonlinear model predictive control (NMPC). Given a discrete-time nonlinear model provided by the user, the toolbox computes quadratic/ellipsoidal terminal costs/sets and local control laws for the following options: (i) cyclically time-varying or standard terminal ingredients; (ii) first or quasi-second order Taylor approximation of the dynamics; (iii) linear or nonlinear local control laws. The YALMIP toolbox and the MOSEK solver are used for solving linear matrix inequalities and the IPOPT solver (with global search) is used for nonlinear programming. Simulation of the resulting stabilizing NMPC algorithms is provided using the CasADi toolbox.

Nonblockingness in supervisory control synthesis guarantees that from every reachable state in the system, some marked (final) state is reachable. Situations, where nonblockingness of multiple sets of marked states (multiple tasks) is imposed, are common. Especially for composite systems, the marking of each subsystem usually has an individual meaning. To deal with these situations, multitasking supervisory control can be used. Currently, in multitasking supervisory control, there is no requirement-feedback when the synthesized supervisor shows unexpected or undesired behaviour (caused by wrong modelling of requirements for example). In this paper a framework is proposed to generate a cause for the absence of a state for multitasking supervisory control. In particular, we propose a pre-synthesis transformation that ensures that the influence of the requirements on the absence of a state is visible in the cause.

Combined Energy and Emission Management (CEEM) problems are a class of optimal control problems that aim to minimize operational costs of (hybrid electric) powertrains with after-treatment system subject to constraints on emissions imposed by legislation. In this paper, a parallel-hybrid heavy-duty vehicle with a Variable Turbine Geometry (VTG) and an Exhaust-Gas Recirculation (EGR) system is considered. The CEEM problem is solved using Sequential Quadratic Programming (SQP) for which the powertrain and after-treatment models are approximated as smooth functions. It will be shown that solving the CEEM problem using SQP is computationally much more efficient when compared to other techniques like dynamic programming. It will also be shown that most of the benefits from CEEM come from the hybrid powertrain and not from regulating the VTG and ERG mass flows. Furthermore, zero emission zones and local emission constraints can also be included without too much effort.

Electric vehicles still account for a small share of the total amount of cars on the road. One of the major issues preventing a larger uptake is their higher upfront cost compared to petrol cars. We aim to address this issue by investigating a module-based product-family approach to take full advantage of economy-of-scale strategies, reducing research, development, and production costs of electric vehicles. This paper instantiates a concurrent design optimization framework, whereby different vehicle types share multiple modular powertrain components, whose size is jointly optimized to minimize the overall operational costs instead of being individually tailored. In particular, we focus on sizing battery and electric motors for a family of vehicles equipped with in-wheel motors. First, we identify a convex model of the powertrain, capturing the impact of modules' sizing and multiplicity on the mechanical power demand and the energy consumption of the vehicles. Second, we frame the concurrent powertrain design and operation problem as a second-order conic program that can be efficiently solved with global optimality guarantees. Finally, we showcase our framework for a family of three different vehicles: a city car, a compact car, and an SUV. Our results show that concurrently optimizing shared components increases the operational costs by 3.2% compared to individually tailoring them to each vehicle, a value that could be largely overshadowed by the benefits stemming from using the same components for the entire product family.

Data-driven modeling of nonlinear dynamical systems often require an expert user to take critical decisions a priori to the identification procedure. Recently an automated strategy for data driven modeling of \textit{single-input single-output} (SISO) nonlinear dynamical systems based on \textit{Genetic Programming} (GP) and \textit{Tree Adjoining Grammars} (TAG) has been introduced. The current paper extends these latest findings by proposing a \textit{multi-input multi-output} (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modelling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.

Lithography processes have advanced steps that need to be controlled accurately in order to achieve high production quality. The common approach is to have a setup phase in which the optimal parameters for each step of the process are explored manually by an operator. This paper introduces a process parameter selection system that can automate this exploration phase. Since a semiconductor manufacturing process is too complex to model mathematically as a whole, a model-based optimization technique is not preferred. Instead, a Gaussian process (GP) based Bayesian optimization (BO) method is applied to optimize the process parameters automatically. This method is designed according to the lithography process domain. To validate the performance of the GP based BO method, optimization experiments are run for eight different manufacturing processes. The results demonstrate that GP based BO obtains better process parameters that reduce the overlay error, an important quality metric, by 6.01% or 0.4 nm compared to the one achieved with the manual parameter selection process. Furthermore, the automated process parameter optimization requires much less expert user knowledge and can be completed in a shorter time. Considering the fact that the semiconductor manufacturers compete with each other with nanometric differences in features of their integrated circuit (IC) designs, this improvement could give a significant advantage in practical applications.

Developing automatic driving solutions and driver support systems requires accurate vehicle specific models to describe and predict the associated motion dynamics of the vehicle. Despite of the mature understanding of ideal vehicle dynamics, which are inherently nonlinear, modern cars are equipped with a wide array of digital and mechatronic components that are difficult to model. Furthermore, due to manufacturing, each car has its personal motion characteristics which change over time. Hence, it is important to develop data-driven modelling methods that are capable to capture from data all relevant aspects of vehicle dynamics in a model that is directly utilisable for control. In this paper, we show how Linear Parameter-Varying (LPV) modelling and system identification can be applied to reliably capture personalised model of the steering system of an autonomous car based on measured data. Compared to other nonlinear identification techniques, the obtained LPV model is directly utilisable for powerful controller synthesis methods of the LPV framework.

The paper addresses the identification of spatial-temporal mirror surface deformations as a result of laser-based heat load within the lithography process of integrated circuit production. The thermal diffusion and surface deformation are modeled by separation of the spatial-temporal effects using data-driven orthogonal decomposition. A novel tree adjoining grammar (TAG) and sparsity enhanced symbolic-regression-based learning methods are deployed to discover temporal dynamics that connect the spatial variation. The resulting data-driven procedure is applied to automatically synthetise a compact model representation of synthetic thermal effects induced mirror surface deformations.

This paper deals with the development of an empirical model to describe the dynamics of milk acidification process for the production of acid casein. The model is based on the dynamics of the pH profiles during the acidification process since pH is a good indicator of the status of the precipitation. Data from laboratory experiments has been used to identify the parameters in the proposed model. Calibration and validation results, with an independent data set, show that the model is able to predict accurately the pH at different temperatures and acid addition rates. Furthermore, the model has been used in a simulation study as an advisory tool to suggest acid addition, proving the parsimony of the model.

Spontaneous breathing effort of a mechanically ventilated patient can seriously deteriorate the treatment outcome if it is not taken into account when choosing the appropriate settings. This paper presents an improved spontaneous breathing effort model that can be used to develop improved mechanical ventilation algorithms. Through an indicative experimental study it is shown that this model accurately describes the spontaneous breathing effort of a healthy human test subject. In comparison to the commonly used sinusoidal halfwave effort model it is shown that the proposed model improved fitting quality by a factor two.

Motion systems are often subject to disturbances such as cogging, commutation errors, and imbalances, that vary with velocity and appear periodic in time for constant operating velocities. The aim of this paper is to develop a repetitive controller (RC) for disturbances that are not periodic in the time domain, yet occur due to an identical position-domain disturbance. A new spatial RC framework is developed, allowing to attenuate disturbances that are periodic in the position domain but manifest a-periodic in the time domain. A Gaussian process (GP) based memory is employed with a suitable periodic kernel that can effectively deal with the intermittent observations inherent to the position domain. A mechatronic example confirms the potential of the method.

In this paper, we propose a model for eco-driving that considers cornering effects. The proposed model purely relies on the geometric configuration of the vehicle and road. Consequently, we propose an eco-driving optimal control problem formulation that is suitable for both straight and curved trajectories in urban scenarios. Moreover, it can be applied for vehicles with front wheel drive (FWD) or rear wheel drive (RWD). We use a case study for an electric vehicle executing cornering maneuvers to validate the proposed approach and highlight its advantages with respect to traditional eco-driving formulations. Results show an approximated improvement of 8% in energy savings with respect to traditional eco-driving strategies, especially in trajectories with large curvatures.

This paper presents a model reduction approach for systems of hyperbolic partial differential equations (PDEs) with nonlinear boundary conditions. These systems can be decomposed into a feedback interconnection of a linear hyperbolic subsystem and a static nonlinear mapping. This structure motivates us to reduce the overall model complexity by only reducing the linear subsystem (the PDE part). We show that the linear PDE subsystem can effectively be approximated by a cascaded structure of systems of continuous time difference equations (CTDEs) and ordinary differential equations (ODEs), where the CTDE captures the infinite-dimensional nature of the PDE model. These systems are constructed by adapting an interpolation method based on frequency-domain data. Models in the form of hyperbolic PDEs with nonlinear boundary conditions are for example encountered in managed pressure drilling (MPD). The proposed technique is verified by application to such an MPD model.

This paper provides a solution to conflict resolutions between Autonomous Vehicles (AV) crossing an urban intersection. The conflict resolution problem is formulated as an optimal control problem, where the objective is to minimize the energy consumption of all the vehicles, while avoiding collisions. Since the problem has a combinatorial nature, it is tackled though a sequential mixed-integer quadratically constrained programming approach. Simulation results show that since the AVs do not need to follow specific driving rules, the intersection crossing order is chosen to optimize the overall energy consumption. The research outcome underlines the benefits of moving towards fully autonomous systems which will allow for higher traffic throughput. Furthermore, the proposed formulation is the starting point for future explorations towards real-time implementation.

Automated systems require controllers which guarantee machine safety and specified functionality even in case of occurring defects. In literature, several methods can be found for formally deriving a supervisor providing such guarantees, including the existence of failure recovery. In this paper, an extension is proposed so that the derived supervisor not only guarantees the existence of failure recovery, but also enforces a shortest path for it. To this end, a two-step procedure is defined for supervisor derivation, in which two algorithms are involved.

Recently Port Hamiltonian systems have been extended to encompass an implicit definition of the energy function of the system, by defining it in terms of a Lagrangian submanifold. In this paper, we extend the definition of Port Hamiltonian systems defined with respect to Lagrangian submanifold to a class of infinite-dimensional systems where the Lagrangian submanifold is defined by first-order differential operators. We show that this adds some port boundary variables and derive the energy balance equation. This construction is illustrated on the model of a flexible nanorod made of composite material.

Mechanical ventilators sustain life of patients that are unable to breathe (sufficiently) on their own. The aim of this paper is to improve pressure tracking performance of mechanical ventilators for a wide variety of sedated patients. This is achieved by utilizing the repetitive nature of sedated ventilation through repetitive control. A systematic design procedure of a repetitive controller for mechanical ventilation is presented. Thereafter, the controller is implemented in an experimental setup showing superior tracking performance for a variety of patients.

In this paper, an event-triggered, data-rate constrained observer for discrete-time linear systems with perturbations is presented. The system is connected to a remote location by a communication channel which can only transmit limited numbers of bits per time interval. The system is subject to perturbations in its state as well as errors in the output measurement. The objective is to reconstruct estimates of the state at the remote location, by sending messages over the communication channel. A new type of data-rate constrained observer which can be more efficient in terms of communication rate is presented. Relation between an admissible communication rate and the system parameters is evaluated. The observer’s efficiency is illustrated by simulations.

In the context of control and estimation under information constraints, restoration entropy measures the minimal required data rate above which a system can be regularly observed. The observer here is assumed to receive its state information through a communication channel of a finite bit-rate capacity. In this paper, we provide a new characterization of restoration entropy which does not require to compute any temporal limit, i.e., an asymptotic quantity. Our new formula is based on the idea of finding an adapted Riemannian metric on the state space that allows to ‘see’ the decisive quantity that determines the restoration entropy - a certain type of Lyapunov exponent - in only one step of time.

In this paper, we propose a novel decision maker design for an autonomous vehicle driving on a highway, considering safety and optimality, and which is scalable, i.e., remains computationally tractable for more complex situations. This is realized in two stages. First, all safe actions are found, and second, from these actions the optimal action is selected, according to (weighted) criteria that capture safety, comfort and efficiency. The design combines rule-based safety checks with the solution of a Markov decision process, found through a tree search algorithm, to fulfill the safe, smart and scalable requirements of the decision maker. The design is validated in simulation using eight different scenarios. The performance of the new design is compared to the performance of a rule-based controller. This comparison is done using three performance criteria that aim to capture safety, efficiency and comfort.

This paper presents a model-free methodology for the placement of pressure sensors in water distribution networks (WDNs) with the aim of performing leak detection/localization tasks. The approach is based on a custom genetic algorithm (GA) optimization scheme, which considers a population whose individuals are binary vectors encoding the network nodes with/without sensors. The optimization process pursues the minimization of a distance-based metric, computed considering the pipe distance from the possible sensors to the complete set of nodes of the network, hence removing the necessity of a hydraulic model of the WDN. The methodology is completed by means of an iterative clustering technique that seeks the enhancement of incoming individuals. The proposed methodology is tested over a well-known case study, L-TOWN from the BattLeDIM2020 challenge, in order to assess its performance., This work has been partially funded by the project PID2020-115905RB-C21 (L-BEST) funded by MCIN/ AEI /10.13039/501100011033.

The productivity and financial success of large-scale arable farming operations depends highly on how effectively resources are distributed among fields. Therefore, it is of interest to develop methods to determine (near optimal) resource inputs. In this paper we focus on computing the optimal irrigation policy for large-scale arable farming operations, where the number of irrigation machinery is much smaller than the number of fields that require irrigation inputs. We propose a model predictive control (MPC) framework that simultaneously computes the optimal division of irrigation over the fields and which irrigation machines should be allocated to which fields, such that the profit at the end of the season is maximized. The fact that the optimization of irrigation and allocation of irrigation machinery is done simultaneously makes our approach vastly different from strategies available in the literature. Another important novelty of our work is that we link short-term effects of crop growth to long-term effects on profit. The proposed framework has reasonable computation times when optimizing on a daily basis over many fields and irrigation machines and guarantees a feasible solution. The main principles of our approach are more widely applicable. Using simulations we demonstrate the robustness of the scheme with respect to changes in weather and benchmark it with respect to a heuristic approach.

Frequency Response Functions (FRFs) are essential for motion control design of complex mechatronic systems. The aim of this paper is to develop Optimal Experiment Design (OED) approaches for accurate identification of FRF models, with the particular focus on Multiple Input Multiple Output (MIMO) systems in closed-loop. The resulting optimization problem is shown to be highly challenging, especially for complex systems. A practical and feasible numerical approach based on a convex relaxation is developed. Experimental results on a next-generation wafer stage confirm that the quality of FRF measurements can be improved significantly using the proposed techniques.

Piezo-stepper actuators consist of piezo elements that are able to displace a mover over an infinite stroke through walking while maintaining the high accuracy and high stiffness properties of the piezo elements. The aim of this paper is to mitigate repetitive disturbances, that are introduced by the walking behaviour, to improve the performance of piezo-stepper actuators. The key challenge is to address repeating disturbances in the position domain, which may be varying in the time domain. A new position-domain iterative learning control procedure is presented that computes a signal in the commutation angle domain that mitigates the repetitive disturbances. The results from this procedure are exploited to determine an optimal waveform for the working range of the actuator. A significant improvement in performance is achieved after applying this waveform to a piezo-stepper actuator.

Piezo actuators are widely used in high-precision equipment where accurate nanometer positioning is required. Piezo material, like PZT, show significant hysteretic behaviour, typically needing position feedback control or solutions like charge amplification to minimize hysteresis effects. Other materials like LiNbO3 show linear behaviour themselves. Both, however, have an inherent high stiffness and lightly damped mechanical resonances leading to unwanted vibration. This paper investigates the use of self sensing in piezo's, using current and voltage in the electrical domain, for active damping purposes. In particular, the combination with charge control is shown, and a hysteresis cancellation technique is proposed.

The growing demand for increased vehicle efficiency has led to the introduction of waste heat recovery techniques in vehicle powertrains, with the organic Rankine cycle as the most interesting technique for automotive applications. In order to integrate the design of the waste heat recovery system into the design optimization of the full powertain, a novel modeling approach is suggested for capturing the key dynamics of a waste heat recovery system with low computational effort and number of design parameters, and which is scalable with the individual components of the cycle. The model is compared with a detailed finite volume model for a given organic Rankine cycle. Secondly, the organic Rankine cycle is integrated into a parallel hybrid vehicle model, and an optimization routine is proposed for obtaining the optimal control trajectories for the powertrain over a drive cycle. The main purpose of the waste heat recovery model designed in this paper in combination with the proposed optimization algorithm is to be used for powertrain design optimization studies.

We consider a networked control loop in which the sensors acquire partial state information and communicate to a remote controller through a lossy communication network. A scheduler, collocated with the sensors, decides to transmit a locally estimated state to the controller based on an event-triggered transmission policy with stochastic thresholds. Assuming that the local estimator either senses the communication channel or receives an ideal acknowledgment from the remote estimator, then the optimal control law can be shown to be a linear function of the conditional expectation of the state. However, the probability distribution of the state conditioned on the information available to the controller based on the mentioned transmission policy and network is not Gaussian, but rather described by a sum of Gaussians with an increasing number of terms at every time-step. We show that the optimal LQG control law can be determined without tracking this probability distribution for finding its expected value. Moreover, we establish that the stochastic event-triggered scheduler can be appropriately regulated in order to achieve a desired triggering probability at every time-step.

In this paper, a robust model predictive control (MPC) technique is introduced to control MIMO linear parameter-varying (LPV) systems subject to input-output constraints. The LPV system is represented in input-output form, which is a common form obtained through LPV system identification. The method guarantees asymptotic stability of the closed-loop system and provides integral action for a given piecewise constant reference trajectory based on past measurements of the inputs and outputs. The technique offers computationally more efficient and less conservative design approach compared to previous works. A simulation example is given to demonstrate the effectiveness of the proposed technique. 2018 European Research Council Scopus

The problem of sampled-data observer design is addressed for a class of state- and parameter-affine nonlinear systems. The main novelty in this class lies in the fact that the unknown parameters enter the output equation and the associated regressor is nonlinear in the output. Wiener systems belong to this class. The difficulty with this class of systems comes from the fact that output measurements are only available at sampling times causing the loss of the parameter-affine nature of the model (except at the sampling instants). This makes existing adaptive observers inapplicable to this class of systems. In this paper, a new sampled-data adaptive observer is designed for these systems and shown to be exponentially convergent under specific persistent excitation (PE) conditions that ensure system observability and identifiability. The new observer involves an inter-sample output predictor that is different from those in existing observers and features continuous trajectories of the state and parameter estimates.

Frequency Response Function (FRF) modeling of Linear Parameter Varying (LPV) systems facilitates analysis, controller design and parametric modeling of a large class systems, including position-dependent mechanical systems. The aim of this paper is to enable FRF identification of LPV systems using global experiments. This is achieved by developing an appropriate definition of the FRF for input-output LPV systems and by developing a method to compute a statistically optimal estimator of the FRF, which reduces to the Empirical Transfer Function Estimate (ETFE) for frozen scheduling. The developed method is successfully used to estimate a position-dependent FRF of a wide-format printer, confirming the potential of the approach.

Supervisory control synthesis for discrete-event systems can help in overcoming the growing complexity in the process of designing supervisors for cyber-physical systems. This is especially the case when fault-tolerance needs to be taken into account. The aim of this paper is to present a structured way of working that can be used for this purpose. Special attention is given to partitioning plant models and requirement models according to nominal and post-fault behavior. Extended finite-state automata and state-based requirement models are used as the modeling formalism. A case study involving a movable bridge (state space size: 8.4 × 1025) is presented to illustrate the proposed method.

Visually based velocity and position estimations are often used to reduce or remove the dependency of an unmanned aerial vehicle (UAV) on global navigation satellite system signals, which may be unreliable in urban canyons and are unavailable indoors. In this paper, a sensor-fusion algorithm based on an extended Kalman filter is developed for the velocity, position, and attitude estimation of a UAV using low-cost sensors. In particular an inertial measurement unit (IMU) and an optical-flow sensor that includes a sonar module and an additional gyroscope are used. The algorithm is shown experimentally to be able to handle measurements with different sampling rates and missing data, caused by the indoor, low-light conditions. State estimations are compared to a ground-truth pose history obtained with a motion-capture system to show the influence of the optical-flow and sonar measurements on its performance. Additionally, the experimental results demonstrate that the velocity and attitude can be estimated without drift, despite the magnetic distortions typical of indoor environments.

A key step in experimental modeling of mechatronic systems is Frequency Response Function (FRF) identification. Applying these techniques to systems where measurement time is limited leads to a situation where the accuracy is deteriorated by transient dynamics. The aim of this paper is to develop a local parametric modeling technique that improves the identification accuracy of a range of systems by exploiting prior knowledge. The method is to impose a prior on the approximate locations of the system poles. This leads to better fit results and enables an accurate variance characterization. As a special case, traditional LPM is recovered.

Automated Managed Pressure Drilling (MPD) is a method for fast and accurate pressure control in drilling operations. The achievable performance of automated MPD is limited, firstly, by the control system and, secondly, by the hydraulics model based on which this control system is designed. Hence, an accurate hydraulics model is needed that, at the same time, is simple enough to allow for the use of high performance controller design methods. This paper presents an approach for nonlinear Model Order Reduction (MOR) for MPD systems. For a single-phase flow MPD system, a nonlinear model is derived that can be decomposed into a feedback interconnection of a high-order linear subsystem and low-order nonlinear subsystem. This structure, under certain conditions, allows for a nonlinear MOR procedure that preserves key system properties such as stability and provides a computable error bound. The effectiveness of this MOR method for MPD systems is illustrated through simulations.