Annual Reviews in Control最新文献

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.

Model Predictive Control (MPC) has recently gained increasing interest in the adaptive management of water resources systems due to its capability of incorporating disturbance forecasts into real-time optimal control problems. Yet, related literature is scattered with heterogeneous applications, case-specific problem settings, and results that are hardly generalized and transferable across systems. Here, we systematically review 149 peer-reviewed journal articles published over the last 25 years on MPC applied to water reservoirs, open channels, and urban water networks to identify common trends and open challenges in research and practice. The three water systems we consider are inter-connected, multi-purpose and multi-scale dynamical systems affected by multiple hydro-climatic uncertainties and evolving socioeconomic factors. Our review first identifies four main challenges currently limiting most MPC applications in the water domain: (i) lack of systematic benchmarking of MPC with respect to other control methods; (ii) lack of assessment of the impact of uncertainties on the model-based control; (iii) limited analysis of the impact of diverse forecast types, resolutions, and prediction horizons; (iv) under-consideration of the multi-objective nature of most water resources systems. We then argue that future MPC applications in water resources systems should focus on addressing these four challenges as key priorities for future developments.

Chlorine is a widely used disinfectant and proxy for water quality (WQ) monitoring in water distribution networks (WDN). Chlorine-based WQ regulation and control aim to maintain pathogen-free water. Chlorine residual evolution within WDN is commonly modeled using the typical single-species decay and reaction dynamics that account for network-wide, spatiotemporal chlorine concentrations only. Prior studies have proposed more advanced and accurate descriptions via multi-species dynamics. This paper presents a host of novel state-space, control-theoretic representations of multi-species water quality dynamics. These representations describe decay, reaction, and transport of chlorine and a fictitious reactive substance to reflect realistic complex scenarios in WDN. Such dynamics are simulated over space- and time-discretized grids of the transport partial differential equation and the nonlinear reaction ordinary differential equation. To that end, this paper (i) provides a full description on how to formulate a high fidelity model-driven state-space representation of the multi-species water quality dynamics and (ii) investigates the applicability and performance of different Eulerian-based schemes (Lax–Wendroff, backward Euler, Crank–Nicolson, and Implicit Upwind) and Lagrangian-based schemes (Method of Characteristics) in contrast with EPANET and its EPANET-MSX extension. Numerical case studies reveal that the Implicit Upwind scheme, Method of Characteristics, and Lax–Wendroff scheme outperform other schemes with reliable results under reasonable assumptions and limitations.

The problem of optimal state estimation via deterministic Kalman filtering in the time and in the frequency domains is considered. The frequency domain method based on spectral factorization, which was developed previously for linear quadratic optimal control, is extended here to Kalman filtering. For a class of Riesz-spectral systems, it is shown that the spectral factorization problem can be solved by symmetric extraction of poles and zeros, which leads to a tractable computational method in order to calculate the optimal output injection in the Kalman filter problem. Then the class of Sturm–Liouville operators is considered on the space of square integrable functions on a finite interval. According to the properties of such unbounded operators on that space, a set of interpolation Hilbert spaces is considered in a second time. Properties of Sturm–Liouville operators on these spaces are exhibited, together with properties of the $C_0-$semigroups that are generated by these operators. In addition, a characterization of approximate observability by means of point measurement operators is established for such systems. For the aforementioned Sturm–Liouville systems with pointwise measurement, the assumptions needed for applying the symmetric extraction method are shown to be satisfied, which entails that these systems are well-adapted for Kalman filtering with a pointwise measurement observation operator which is bounded on a well-chosen Hilbert state space. The great advantage of considering a new state space is pushed forward by this optimal state estimation problem, which would not make sense in the space of square integrable functions, notably in terms of Riccati equation. The main results are applied to the Kalman filtering of a diffusion system with mixed boundary conditions and pointwise measurement.

Modern Building Automation Systems (BASs), as the brain that enable the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as the cloud, to enable low-cost remote management, optimized automation via outsourced cloud analytics, and increased building-grid integrations. As smart buildings move towards open communication technologies, providing access to BASs through the building's intranet, or even remotely through the Internet, has become a common practice. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols (i.e., BACnet, KNX, LonWorks, and Modbus) are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. The impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed.