Annual Reviews in Control最新文献

In this paper, we focus on the optimal operation of a multi-agent system affected by uncertainty. In particular, we consider a cooperative setting where agents jointly optimize a performance index compatibly with individual constraints on their discrete and continuous decision variables and with coupling global constraints. We assume that individual constraints are affected by uncertainty, which is known to each agent via a private set of data that cannot be shared with others. Exploiting tools from statistical learning theory, we provide data-based probabilistic feasibility guarantees for a (possibly sub-optimal) solution of the multi-agent problem that is obtained via a decentralized/distributed scheme that preserves the privacy of the local information. The generalization properties of the data-based solution are shown to depend on the size of each local dataset and on the complexity of the uncertain individual constraint sets. Explicit bounds are derived in the case of linear individual constraints. A comparative analysis with the cases of a common dataset and of local uncertainties that are independent is performed.

In this survey, we describe controlled interacting particle systems (CIPS) to approximate the solution of the optimal filtering and the optimal control problems. Part I of the survey is focussed on the feedback particle filter (FPF) algorithm, its derivation based on optimal transportation theory, and its relationship to the ensemble Kalman filter (EnKF) and the conventional sequential importance sampling–resampling (SIR) particle filters. The central numerical problem of FPF—to approximate the solution of the Poisson equation—is described together with the main solution approaches. An analytical and numerical comparison with the SIR particle filter is given to illustrate the advantages of the CIPS approach. Part II of the survey is focussed on adapting these algorithms for the problem of reinforcement learning. The survey includes several remarks that describe extensions as well as open problems in this subject.

This article focuses on extending, disseminating and interpreting the findings of an IEEE Control Systems Society working group looking at the role of control theory and engineering in solving some of the many current and future societal challenges. The findings are interpreted in a manner designed to give focus and direction to both future education and research work in the general control theory and engineering arena, interpreted in the broadest sense. The paper is intended to promote discussion in the community and also provide a useful starting point for colleagues wishing to re-imagine the design and delivery of control-related topics in our education systems, especially at the tertiary level and beyond.

This article provides a comprehensive and illustrative presentation of the young field of encrypted control. In particular, we survey the evolution of encrypted controllers from their first appearance in 2015 until 2023 and derive a categorization into two generations mainly characterized by the utilized cryptographic methods. We further envision future developments and challenges of encrypted control. Throughout our presentation, we build less on technicalities but rather on intuitive tutorial-style explanations. This way, we intend to build a bridge from control engineering to cryptography and to make the interdisciplinary field of encrypted control more accessible.

This article provides an overview of certain direct data-driven control results, where control sequences are computed from (noisy) data collected during offline control experiments without an explicit identification of the system dynamics. For the case of noiseless datasets, we derive several closed-form data-driven expressions that solve a variety of optimal control problems for linear systems with quadratic cost functions of the state and input (including the linear quadratic regulator problem, the minimum energy control problem, and the linear quadratic control problem with terminal constraints), discuss their advantages and drawbacks with respect to alternative data-driven and model-based approaches, and showcase their effectiveness through a number of numerical studies. Interestingly, these results provide an alternative and explicit way of solving classic control problems that, for instance, does not require the solution of an implicit and recursive Riccati equation as in the model-based setting. For the case of noisy datasets, we show how the closed-form expressions derived in the noiseless setting can be modified to compensate for the bias induced by noise, and perform a sensitivity analysis to reveal favorable asymptotic robustness properties of the derived data-driven controls. We conclude the paper with some considerations and a discussion of outstanding questions and directions of future investigation.

Reconfiguration blocks are structures that have been successfully employed for fault-tolerant control. In this regard, the technique known as fault hiding usually inserts the reconfiguration blocks between the faulty system and the nominal controller to recover the system properties without modifying the controller. In addition to fault hiding, novel applications to reconfiguration blocks have been recently proposed, including to networked and cyber-secure control. This paper presents the key concepts related to the use of reconfiguration blocks and fault hiding. In addition, it presents an overview of the existing structures of reconfiguration blocks and the main methodologies to design those blocks for fault hiding. Moreover, it revises the main applications of reconfiguration blocks, including the emerging applications out of the fault-tolerant control scope. Finally, this paper also discusses the main challenges and further research directions related to this topic.

Thermodynamics historically developed out of a desire to quantify the maximal efficiency of early thermodynamic heat engines, especially through the work of French physicist Sadi Carnot. However, the more practical problem about quantifying the limits of power output that can be delivered from the system remained unclear due to the fact that quasistatic process requires infinite operation time, resulting in a vanishing power output. Recent advances in the field of stochastic thermodynamics appear to link the theory and practice, which enables us to mathematically analyze the maximal power and also control design of a thermodynamic heat engine on the microscopic scale. This review aims at summarizing and categorizing previous research on the optimal performance of two kinds of finite-time stochastic thermodynamic engines (a Carnot-like heat engine and the heat engine with a single heat bath) both in the linear and nonlinear response regimes. Thus, this is to be expected, estimated bounds for maximal power output and optimal control can provide physical insights and guidelines for engineering design. We start by reviewing the optimal performance for the Carnot-like engine that alternates between two heat baths of different constant temperatures. Then we discuss the fundamental bounds of the power output for the heat engine with a single periodic heat bath. In each setting, we provide a comprehensive analysis of the maximal power and efficiency both in the linear and nonlinear regimes. Finally, several challenges and future research directions are concluded.

The fundamental lemma by Jan C. Willems and co-workers is deeply rooted in behavioral systems theory and it has become one of the supporting pillars of the recent progress on data-driven control and system analysis. This tutorial-style paper combines recent insights into stochastic and descriptor-system formulations of the lemma to further extend and broaden the formal basis for behavioral theory of stochastic linear systems. We show that series expansions – in particular Polynomial Chaos Expansions (PCE) of $L^2$-random variables, which date back to Norbert Wiener’s seminal work – enable equivalent behavioral characterizations of linear stochastic systems. Specifically, we prove that under mild assumptions the behavior of the dynamics of the $L^2$-random variables is equivalent to the behavior of the dynamics of the series expansion coefficients and that it entails the behavior composed of sampled realization trajectories. We also illustrate the short-comings of the behavior associated to the time-evolution of the statistical moments. The paper culminates in the formulation of the stochastic fundamental lemma for linear time-invariant systems, which in turn enables numerically tractable formulations of data-driven stochastic optimal control combining Hankel matrices in realization data (i.e. in measurements) with PCE concepts.