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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.

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.

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.

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.

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.

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.

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 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.

In the context of model-free optimization of dynamic nonlinear multiple-input-single-output (MISO) systems using extremum-seeking (ES), accurate and fast derivative estimation of the systems steady-state performance map is essential. This paper presents a generalized derivative estimator (DE) framework for unknown MISO static maps. To this extent, the map input is perturbed with sinusoidal dither signals with different frequencies. Using the proposed framework, the derivatives can be estimated up to an arbitrary order, for maps with an arbitrary number of inputs. Conditions on the dither frequencies are provided, which optimize the DE time-scale, such that derivative estimation is as fast as possible. Simulation examples are provided to demonstrate the effectiveness of the proposed framework.

Within hybrid electric vehicles (HEVs) predictive energy management strategies (EMSs) have the potential to reduce the fuel consumption compared to conventional EMSs, where the drive cycle is unknown. Typically, predictive EMSs require a future vehicle speed profile prediction. However, when prediction is inaccurate, the systems fuel reduction performance and robustness may be compromised. Among many influential factors, inaccurate prediction is mainly caused by uncertain dynamic traffic conditions, e.g. traffic and traffic lights. This paper develops a predictive EMS, which enhances the equivalent fuel consumption minimization strategy (ECMS) with real-time traffic flow data and traffic light position to maximize fuel reduction performance of HEVs. Moreover, a Monte Carlo approach is exploited to handle the traffic light uncertainty. Simulation results demonstrate the benefits of Monte Carlo approach in predictive EMS to enhance the robustness and fuel reduction performance up to 2-11[%] compared to conventional strategies for various battery capacities.

The analysis of industrial processes, modelled as descriptor systems, is often computationally hard due to the presence of both algebraic couplings and difference equations of high order. In this paper, we introduce a control refinement notion for these descriptor systems that enables analysis and control design over related reduced-order systems. Utilising the behavioural framework, we extend upon the standard hierarchical control refinement for ordinary systems and allow for algebraic couplings inherent to descriptor systems., Comment: 8 pages, 3 figures

Nowadays, there is an increasing societal demand for smart mobility solutions which can increase the throughput, comfort, and safety of driving on a road. As one of these solutions, vehicle following technologies like cooperative adaptive cruise control (CACC) are introduced. with the help of vehicle-to-vehicle (V2V) communication, CACC can reduce the safe minimal inter-vehicular distance. Additionally, sharing the vehicle intentions through V2V, enables multi-vehicle maneuvers to be cooperatively executable. In this paper, using the concept of artificial potential fields, a longitudinal control scheme for performing a cooperative merging maneuver will be suggested. Artificial potential fields are powerful enough for modeling and solving such a complex problem, where in addition to vehicle following other objectives such as merging should be addressed. The designed algorithms for vehicle cooperative maneuvering are verified through simulations.

Ideally, ironless linear motors can reach very high precision with simple classical commutation using three-phase sinusoidal currents. However, in reality, due to deviations from the design parameters, there are various parasitic forces and torques for which the classical commutation cannot compensate. An alternative solution is to formulate the commutation as an optimization problem and solve it numerically. This paper proposes a new optimization algorithm which is computationally efficient and well-suited to the commutation problem in the sense that it is capable of compensating for parasitic forces and torques while minimizing the dissipated power in the motors. Simulation results with a finite element method model are presented to demonstrate the effectiveness of the proposed commutation algorithm.

This paper presents a battery peak power shaving strategy for battery electric buses. The developed strategy restricts the battery charge/discharge power when the propulsion power demand is high to avoid high deterioration of the battery capacity during operation. Without reducing the propulsion power, the developed strategy optimizes the power demand of the passenger cabin air conditioning system while guaranteeing the passenger temperature comfort. An equivalent consumption minimization strategy (ECMS) technique is extended to include the thermal comfort of the passenger in the energy management system of the bus. The developed strategy is analytical, real-time implementable and verified via hardware-in-the-loop test. Simulation results demonstrate the capability for satisfying the propulsion power demand and passenger temperature comfort while reducing the battery peak power (up to 20% during acceleration) to restrict the battery degradation significantly.