Keywords: Identification for Robust Control
Abstract: This talk is concerned with system identification, in which a continuous-time system model is constructed from input-output data only. Continuous-time models are suitable for representing many physical systems and are highly consistent with control system design. Among various identification methods, identification in a closed-loop environment may be necessary in practice. In fact, closed-loop identification is inevitable to identify subsystems in large-scale networked systems or open loop unstable systems. Some difficulties of closed-loop identification are shown first, and then a new identification framework to overcome such difficulties will be introduced. In the framework, any information on controllers or excitation signals is not required, and the system identification can be performed in a unified way for stable/unstable systems in closed loop environments. Numerical examples show that it is effective in the presence of heavy measurement noises.

Keywords: Robust Stability and Performance Analysis, Aerospace
Abstract: Monte-Carlo simulations play a key role in most Verification and Validation (V&V) processes. It is however time-consuming and may fail to detect rare worst-case configurations. In such cases, when applicable, mu-analysis offers a nice alternative but does not provide any quantification of the probability of occurrence of the identified worst-cases. A control system can then be invalidated on the basis of unlikely events. Probabilistic mu-analysis was introduced in this context 20 years ago to bridge the gap between the two techniques, but until recently no practical tools were available. This paper summarizes recent advances on this topic. A practical algorithm for probabilistic gain, phase and disk margins analysis is first proposed, and then applied to a satellite high pointing system involving uncertain flexible modes.

Keywords: Aerospace, Optimal Control and Dynamic Optimization, Robust Optimization
Abstract: To guarantee the operational safety of unmanned aircraft systems (UASs) in short time scales, it is important to adopt the systems for planning conflict-free trajectories which are robust against the uncertainty of the tactical flight intent of neighboring UASs and the wind conditions. Thus, the present paper proposes a new decentralized trajectory optimization method that enables to track the strategic four-dimensional trajectories as much as possible while maintaining the necessary separation from the neighboring UASs under the above-stated uncertainty. The proposed method adopts a fully decentralized formulation for separation constraints which is enabled by the worst-case estimate of the uncertainty of the neighboring UAS’s decision and wind. The effectiveness of the method is demonstrated through numerical simulations.

Keywords: Robust Control, Aerospace
Abstract: Stratospheric balloons offer cost-effective platforms for optical payloads in the context of astronomy missions. During the 2018 flight of the Faint Intergalactic medium Redshifted Emission Balloon (FIREBall) experiment, the moon light was scattered from the surface of the balloon and re-directed into the telescope which resulted in degraded optical performance. To reduce this parasite effect, it is sought to increase the length of the flight train. However, this change in the mechanical design significantly modifies the dynamics of the system and the pointing performance must not be altered. In this purpose, a robust integrated control/structure co-design method is proposed. After deriving a Linear Fractional Transformation (LFT) model of the system, the co-design is tackled as a multi-objective, structured, robust H2/Hinfinity problem that is solved with a non-smooth optimization algorithm to maximize the train's length under constraints of pointing performance. By optimizing in a single iteration the controllers along with the structural parameter with regard to the worst-case configurations of the uncertain parameters, time-consuming procedures requiring not only to iterate between control and mechanical design, but also to analyze the robustness based on Monte-Carlo simulations, are avoided.

Keywords: H-Infinity Control, Identification, and Estimation, Aerospace, Robust Control
Abstract: We address the conversion problem from {it a priori} designed unstructured Linear Time-Invariant (LTI) controllers to observer-structured LTI controllers, whose structure is similar to but is not exactly the same as the so-called ``Luenberger observer-based controllers'', for Linear Parameter-Varying (LPV) or LTI Parameter-Dependent (LTIPD) plant systems. For this problem, we propose a method by designing appropriate state transformation matrices which minimize the errors between plant systems' states and transformed controllers' states for external inputs via parametrically multi-affine Linear Matrix Inequality (LMI) conditions. The proposed method embeds existing LTI controllers with the ability of observers while their control performance is unchanged. We introduce a numerical example and illustrate the usefulness of our method by embedding observer property to an {it a priori} designed H_{infty} flight controller.

Keywords: H-Infinity Control, Identification, and Estimation, Model and Controller Reduction, Aerospace
Abstract: In this paper, we investigate the design of linear active output feedback control for aircraft gust load alleviation, involving standard sensors and control surfaces. The control architecture is composed of (i) a robust observer, allowing for estimating some wing bending moments despite the wind perturbation, and (ii) a structured H∞-norm oriented control law exploiting this additional information. To cope with the complexity of the aeroelastic model and make the control and observer synthesis computationally tractable, a model reduction step, based on Petrov-Galerkin projections, is first applied. One specificity of this work is that the obtained projector is exploited in the observer design, linking the reduction and the estimation. The effectiveness of the methodology is illustrated through numerical simulation involving an open-access high complexity aeroelastic model.

Keywords: H-Infinity Control, Identification, and Estimation, Robust Control, Aerospace
Abstract: This paper investigates the use of structured H∞ control for the aerodynamic descent phase of a reusable rocket. A unified control strategy for attitude and position is proposed and verified. The architecture of the controller, together with the observed performance are discussed. Results are presented for CALLISTO, a reusable rocket demonstrator jointly developed by DLR, JAXA and CNES.

Keywords: Robust Stability and Performance Analysis, LMI and Convex Optimization in Control, Robust Control
Abstract: This article investigates the robust stability of uncertain continuous-time linear systems with parameters belonging to intervals. Differently from the techniques available in the literature, the proposed method, formulated in terms of an iterative algorithm based on LMIs, treats the interval bounds as optimization variables and can be used, for instance, to search for the maximum variation around nominal values. Experiments involving the fragility analysis of controllers and volume maximization of the polyhedral region centered on nominal parameters are presented to illustrate possible applications of the proposed technique.

Keywords: LMI and Convex Optimization in Control, Robust Control, Robust Nonlinear Control
Abstract: This paper addresses the problem of designing an anti-windup like compensator for discrete-time linear control systems with quantized input. The proposed compensator provides a correction signal proportional to the quantization error that is fed to the controller. The compensator is designed to ensure that solutions to the closed-loop systems converge in finite time into a compact set containing the origin that can be tuned by the designer. A numerically tractable algorithm with feasibility guarantees is provided for the design of the compensator. The proposed results are illustrated on an academic example and an open-loop unstable aircraft system.

Keywords: Robust Control, LMI and Convex Optimization in Control, Robust Optimization
Abstract: A new approach is presented to obtain a convex set of robust D-stabilizing fixed structure controllers, relying on Cauchy's argument principle. A convex set of D-stabilizing controllers around an initial D-stabilizing controller for a multi-model set is represented by an infinite set of Linear Matrix Inequalities (LMIs). By appropriate sampling of the D-stability boundary, a Semi-Definite Programming (SDP) is proposed that can be integrated in other synthesis approaches to ensure D-stability along other design specifications. To showcase utility of the proposed approach, two different examples are given: a boost converter with multi-model uncertainty and a laser-beam system modelled by an identified finite impulse response.

Keywords: LMI and Convex Optimization in Control, Robust Optimization, Analysis of Big Data Affected by Uncertainties
Abstract: This work bounds extreme values of state functions for a class of input-affine continuous-time systems that are affected by polyhedral-bounded uncertainty. Instances of these systems may arise in data-driven peak estimation, in which the state function must be bounded for all systems that are that are consistent with a set of state-derivative data records corrupted under L-infinity bounded noise. Existing occupation measure-based methods form a convergent sequence of outer approximations to the true peak value, given an initial set, by solving a hierarchy of semidefinite programs in increasing size. These techniques scale combinatorially in the number of state variables and uncertain parameters. We present tractable algorithms for peak estimation that scale linearly in the number of faces of the uncertainty-bounding polytope rather than combinatorially in the number of uncertain parameters by leveraging convex duality and a theorem of alternatives (facial decomposition). The sequence of decomposed semidefinite programs will converge to the true peak value under mild assumptions (convergence and smoothness of dynamics).

Keywords: Networked Systems, Communications
Abstract: Generative Adversarial Networks (GANs) have seen great research interest in recent years, due to both their ability to represent structure in data and generate novel samples. Anomaly detection, which discerns novel samples or patterns, is a well-known problem that can be studied using GANs with a fresh perspective, especially in novel application domains such as wireless communication networks. For these models to achieve an accurate representation of the underlying data distribution, significant volumes of data are required. If this data source is not centralised (e.g. stored at multiple hosts or data centres), non-standard training methods are required to achieve comparable performance to the centralised case. This paper presents the key collaborative training methods that have emerged in recent years that draw on the GAN's modular structure to achieve high performance while balancing computation, storage, and communication requirements and demonstrates their application to the task of anomaly detection using cognitive radios.

Keywords: Networked Systems, Robust Optimization, Robust Control for Distributed Parameter Systems
Abstract: Distributed model predictive control (DMPC) schemes have become a popular choice for networked control problems. Under this approach, local controllers use a model to predict its subsystem behavior during a certain horizon so as to find the sequence of inputs that optimizes its evolution according to a given criterion. Some convenient features of this method are the explicit handling of constraints and the exchange of information between controllers to coordinate their actuation and minimize undesired mutual interactions. However, we find that schemes have been developed naively, presenting flaws and vulnerabilities that malicious entities can exploit to gain leverage in cyber-attacks. The goal of this work is to raise awareness about this issue by reviewing the vulnerabilities of DMPC methods and surveying defense mechanisms. Finally, several examples are given to indicate how these defense mechanisms can be implemented in DMPC controllers.

Keywords: Networked Systems, Fault Detection in Uncertain Systems, Communications
Abstract: This paper presents an overview on the recent advances in the research of security of cyber-physical systems. We place particular emphases on consensus problems for multi-agent systems in hostile environments and their analyses on the resiliency against two types of attacks. First, we discuss a class of data injection attacks by focusing on the approach based on mean subsequence reduced (MSR) algorithms and their variants. Agents equipped with such algorithms will ignore their neighbors taking extreme state values. Characterizations on the properties necessary for network topologies and moreover a number of extensions with enhanced resiliency will be established. As the second class of attacks, the effects of denial-of-service (DoS) attacks will be examined in the context of multi-agent consensus. By employing a DoS model based on the energy constraints of the attacker, we will observe that robustness against such attacks may depend on system properties such as dynamics of the individual agents and network structures. Applications of the algorithms will be further discussed for clock synchronization in wireless sensor networks and control of a group of mobile robots.

Keywords: Automotive, Optimal Control and Dynamic Optimization, Computational Methods in Control
Abstract: This paper provides a way of hierarchically distributed control for large-scale urban road traffic networks based on the concept of "Glocal (Global/Local) Control." The proposed glocal control system consists of demand distribution control by dynamic route guidance (global control) and traffic signal control (local control), and they are collaborated each other through so called "glocal adaptor" in order to achieve both increase of traffic volume (global objective) and jam alleviation (local objective). We first formulate the structure of the glocal adaptor to make the balance of the global and local objectives and to provide the (sub)optimal reference of each layer. Second, we provide model predictive control-based algorithms for demand distribution control and distributed traffic signal control, which are completely independent. Finally, the effectiveness of the proposed method is verified by numerical experiments.

Keywords: Networked Systems, Optimal Control and Dynamic Optimization
Abstract: Clustering strategies are becoming increasingly relevant to boost the scalability of distributed control methods by focusing the cooperation efforts on highly coupled agents. They are also relevant in systems where failing communication links and plug-and-play events are considered, which demand increased flexibility and modularity. This article reviews commonalities and differences of those distributed strategies that exploit the degree of interaction between control agents to boost the mentioned properties, frequently leading to control structures where the communication network becomes a decision variable that may evolve dynamically. Taxonomies based on the control law employed, the criterion for selecting the network topology, its static/dynamical nature, the control architecture, and the provided theoretical properties, are given. Additionally, a review of applications in power networks, water systems, vehicle and traffic systems, renewable energy plants, and chemical processes is provided.

Keywords: Optimal Control and Dynamic Optimization, Computational Methods in Control
Abstract: A method to design an optimal sampled-data controller is proposed for a nonlinear system. It is based on the stable-manifold approach and extensive use of numerical methods and achieves optimal control performance for a given nonzero sampling period. In the method, the Hamiltonian system is considered for the posed optimal control problem and its stable manifold gives the optimal controller. Discretization of a given nonlinear system and time evolution of the Hamiltonian system need to be computed in the method and for the purpose numerical methods are used. The proposed method is applied to a pendulum as a numerical example, which shows that its swing-up is possible with a rather long sampling period.

Keywords: Optimal Control and Dynamic Optimization, LMI and Convex Optimization in Control, Networked Systems
Abstract: The paper proposes a novel time-optimal control that is also sparse in both time and space domain to take account of resource constraints in networked control systems. The control problem is described as minimization of a weighted sum of the terminal time and the L0 norm of multi-input control for a linear time-invariant dynamical system, with state and control constraints. In particular, we treat the constraint on the number of actuations at each time, which is described as an l0 norm constraint. Since the L0/l0 optimization problem is highly non-convex, we propose to solve a convex relaxation using L1 and l1 norms. We give sufficient conditions for the equivalence between the original L0/l0 problem and the relaxed L1/l1 problem. Based on the relaxation, we also propose a numerical computation method for the L1/l1 problem by sequential linear programming. We show numerical examples to illustrate the effectiveness of the proposed control method.

Keywords: LMI and Convex Optimization in Control
Abstract: Receding-Horizon Estimation (RHE) is an optimization approach that uses past series of the measured state and input of the plant and finds estimated statea based on linear programming or quadratic programming. It is known that RHE can estimate the plant state to which the extended Kalman filter cannot be applied due to modeling errors. This paper discusses the new computational form of RHE based on the principal dual gradient algorithm. The proposed form is expressed by the dynamical system, so we can consider the computational stability based on the dynamical system theory. This paper discusses the continuous-time representation of the RHE algorithm and filter characteristics to improve the convergence performance of the estimation.

Keywords: Hybrid Systems, LMI and Convex Optimization in Control
Abstract: We study an interconnection between a slow switched affine system and a fast LTI system. The lower dimensional slow and fast subsystems are computed via the singular perturbation approach. We consider the classical stabilization method based on the existence of a stable convex combination (while ignoring the fast dynamics) and we provide an LMI condition for checking ultimate boundedness when the fast dynamics is taken into account. Numerical example illustrates the main result.

Keywords: H-Infinity Control, Identification, and Estimation, Hybrid Systems, Robust Stability and Performance Analysis
Abstract: This paper deals with simultaneous interval state and fault estimation for continuous-time switched systems subject to unknown but bounded state disturbances. By taking the actuator fault vector as a part of an extended state vector, the original system is transformed into an augmented descriptor system. Under this augmented descriptor form, and by using a new structure of interval observers, the conservatism of observer gain matrices can be significantly reduced. The effect of disturbances is attenuated based on an L_{infty} approach. Then, the interval observer gains are computed by solving Linear Matrix Inequalities (LMIs) derived from a common Lyapunov function. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

Keywords: Optimal Control and Dynamic Optimization, Robust Adaptive Control
Abstract: Adaptively controlling and minimizing regret in unknown dynamical systems while controlling the growth of the system state is crucial in real-world applications. In this work, we study the problem of stabilization and regret minimization of linear over-actuated dynamical systems. We propose an optimism-based algorithm that leverages possibility of switching between actuating modes in order to alleviate state explosion during initial time steps. We theoretically study the rate at which our algorithm learns a stabilizing controller and prove that it achieves a regret upper bound of mathcal{O}(sqrt{T}).

Keywords: Aerospace
Abstract: AOCS (Attitude and Orbit Control Systems) and GNC (Guidance Navigation and Control) systems for European Space Missions are developed with robustness as critical need: robustness to varying platform physical properties, robustness to uncertain/unknown space environment conditions, robustness and adaptation to enable on board real-time autonomy. Verification being a demanding process for Space missions AOCS and GNC systems, the integration of robustness in the design process is a key asset. The talk presents the challenges of Control design for European Space Agency missions and Robust Control achievements on several past and flying missions and opens on the evolution of AOCS and GNC systems process and design techniques currently studied or foreseen in our technical roadmaps to cope with new challenges on enhanced autonomy, process optimisation for product lines production, New Space platforms,...