Annual Reviews in Control最新文献

Over the past decade, the advancement of digital technology has significantly enhanced operations management in complex cyber-physical systems (CPSs), especially in the production and manufacturing sectors. In such systems, the physical and cyber spaces are generally connected through sensors, networking, and control actions. With the surge in available real-time data, automation and intelligence have become increasingly prevalent. However, full automation and sophisticated intelligence often remain challenging to achieve in real-world CPSs. Currently, many practical tasks in CPSs are best tackled through the integration of human cognitive skills with autonomous systems, highlighting the indispensable role that humans play in these settings. In this study, we present a framework for real-time decision-making and control in complex cyber-physical-human systems. The framework consists of three main modules: intelligent data processing, intelligent decision-making and control, and human-computer interaction. It is designed to provide a practical and implementable framework for supporting real-time decision-making and control in cyber-physical-human system applications. To demonstrate the applicability of the framework, we build a comprehensive decision support tool to manage several important real-time decision-making and control tasks at a container terminal. The tool is seamlessly integrated into the main operating system of the container terminal and aids decision-makers in making optimal decisions and generating appropriate control actions. The effectiveness of the tool is confirmed by observed improvements in several key operational efficiency indicators at the container terminal.

This article provides an overview of model predictive control (MPC) frameworks for dynamic operation of nonlinear constrained systems. Dynamic operation is often an integral part of the control objective, ranging from tracking of reference signals to the general economic operation of a plant under online changing time-varying operating conditions. We focus on the particular challenges that arise when dealing with such more general control goals and present methods that have emerged in the literature to address these issues. The goal of this article is to present an overview of the state-of-the-art techniques, providing a diverse toolkit to apply and further develop MPC formulations that can handle the challenges intrinsic to dynamic operation. We also critically assess the applicability of the different research directions, discussing limitations and opportunities for further research.

This paper presents an optimization-based perspective for incorporating disturbance decoupling constraints into controller synthesis, which paves the way for utilizing numerical optimization tools. We consider the constraints arising from the following sets of static state feedback: (i) The set of all disturbance decoupling controllers; (ii) The set of all disturbance decoupling and stabilizing controllers. To inner approximate these sets by means of matrix equations or inequalities, we provide a unifying review of the relevant results of the geometric control theory. The approximations build on the characterization of controlled invariant subspaces in terms of the solvability of a linear matrix equation (LME) involving the state feedback. The set (i) is inner approximated through the LME associated with any element of an upper semilattice generated by controlled invariant subspaces. The set (ii) is inner approximated through a bilinear matrix inequality (BMI) and the LME associated with any element of a different upper semilattice generated by internally stabilizable controlled invariant subspaces. However, the resulting inner approximations depend on the subspaces chosen from the semilattices. It is shown that a specific (internally stabilizable) self-bounded controlled invariant subspace, which is the best choice regarding eigenvalue assignment, yields the largest inner approximation for both of the sets among (internally stabilizable) self-bounded controlled invariant subspaces. The inner approximations exactly characterize the controller sets under particular structural conditions. We have been driven by two primary motivations in investigating inner approximations for the sets above: (i) Enable the formulation of a variety of equality (and inequality) constrained optimization problems, where cost functions, such as a norm of the state feedback, can be minimized over a large subset of the set of all disturbance decoupling (and stabilizing) controllers; (ii) Introduce the disturbance decoupling constraints to members of the control systems community who might not be quite familiar with the elegant geometric state-space theory, similar to the authors themselves. This can add another dimension to research endeavors in resilient control of networked multi-agent systems.

The use of wireless communication within the civil nuclear industry can bring many benefits over wired solutions, such as reducing lifecycle costs and enabling new applications in asset and process management. This paper will discuss aspects of wireless communication in industrial control systems, i.e. termed wireless control systems, of the civil nuclear industry. In this respect, we will review previous use of wireless communication in the nuclear industry, and provide the results of a recent feasibility study of wireless communication for an industrial, civil nuclear control system. The studied use case was of an advanced nuclear modular reactor, the Stable Salt Reactor (SSR), and the augmentation of one of its control systems, the refuelling control system, with wireless communication. Hence, in contrast to previous work on wireless control systems, this paper here will focus on the complex and rigorous processes required for regulated safety which have to be followed to allow for wireless control to be implemented in the nuclear civil sector. The following analysis and design procedure was followed: (a) the decision process for choosing the refuelling control system, (b) the review for a suitable communication protocol and technology, the analysis for placement of wireless transceivers for sensors and actuators, (c) the analysis for wireless communication integrity, (d) the basic analysis and guidelines for control system robustness under packet loss, (e) the discussion of possible self-powering options and (f) the safety analysis of the control system under communication failure. Our initial hypothesis is that wireless control systems in Nuclear Applications can improve asset integrity. Control systems can be made more robust and secure to external influences by securely communicating control responses and asset information within a Nuclear Plant. Safety is also improved by reducing the number of operator interactions required for servicing connections, as failures are reduced overall. The removal of power/data harnesses from in-reactor applications can enable faster deployment and replacement of instrumentation for new builds, existing plants and decommissioning.

In recent years, formal methods have been extensively used in the design of autonomous systems. By employing mathematically rigorous techniques, formal methods can provide fully automated reasoning processes with provable safety guarantees for complex dynamic systems with intricate interactions between continuous dynamics and discrete logics. This paper provides a comprehensive review of formal controller synthesis techniques for safety-critical autonomous systems. Specifically, we categorize the formal control synthesis problem based on diverse system models, encompassing deterministic, non-deterministic, and stochastic, and various formal safety-critical specifications involving logic, real-time, and real-valued domains. The review covers fundamental formal control synthesis techniques, including abstraction-based approaches and abstraction-free methods. We explore the integration of data-driven synthesis approaches in formal control synthesis. Furthermore, we review formal techniques tailored for multi-agent systems (MAS), with a specific focus on various approaches to address the scalability challenges in large-scale systems. Finally, we discuss some recent trends and highlight research challenges in this area.

This tutorial paper presents recent work of the authors that extends the theory of Control Barrier Functions (CBFs) to address practical challenges in the synthesis of safe controllers for autonomous systems and robots. We present novel CBFs and methods that handle safety constraints (i) with time and input constraints under disturbances, (ii) with high-relative degree under disturbances and input constraints, and (iii) that are affected by adversarial inputs and sampled-data effects. We then present novel CBFs and adaptation methods that prevent loss of validity of the CBF, as well as methods to tune the parameters of the CBF online to reduce conservatism in the system response. We also address the pointwise-only optimal character of CBF-induced control inputs by introducing a CBF formulation that accounts for future trajectories, as well as implementation challenges such as how to preserve safety when using output feedback control and zero-order-hold control. Finally we consider how to synthesize non-smooth CBFs when discontinuous inputs and multiple constraints are present.

Modern autonomous systems, such as flying, legged, and wheeled robots, are generally characterized by high-dimensional nonlinear dynamics, which presents challenges for model-based safety-critical control design. Motivated by the success of reduced-order models in robotics, this paper presents a tutorial on constructive safety-critical control via reduced-order models and control barrier functions (CBFs). To this end, we provide a unified formulation of techniques in the literature that share a common foundation of constructing CBFs for complex systems from CBFs for much simpler systems. Such ideas are illustrated through formal results, simple numerical examples, and case studies of real-world systems to which these techniques have been experimentally applied.

A current trend in research on multi-agent control systems is to consider high-level task specifications that go beyond traditional control objectives and take into account the heterogeneity of each agent in the system, i.e., the different capabilities of the agents in terms of actuation, sensing, communication and computation. This article provides an overview of our work on the problem of control of heterogeneous multi-agent systems under both spatial and temporal constraints as well as our perspective on the challenges and open problems associated with the consideration of such spatiotemporal constraints. Initially, we review a set of control strategies introduced by the authors addressing the satisfaction of cooperative tasks such as formation control as well as individual objectives such as reference tracking. The satisfaction of those objectives is ensured using prescribed performance control. Building upon these approaches we then review recent results on control under high-level spatiotemporal objectives expressed in Signal Temporal Logic, a formal language that allows to express complex spatial tasks that must be satisfied within pre-defined deadlines. Theoretical results considering multi-agent systems with various capabilities under spatiotemporal constraints are presented.