Annual Reviews in Control最新文献

Guidance and control (G&C) technologies play a central role in the development and operation of vehicular systems. The emergence of computational guidance and control (CG&C) and highly efficient numerical algorithms has opened up the great potential for solving complex constrained G&C problems onboard, enabling higher level of autonomous vehicle operations. In particular, convex-optimization-based G&C has matured significantly over the years and many advances continue to be made, allowing the generation of optimal G&C solutions in real-time for many vehicular systems in aerospace, automotive, and other domains. In this paper, we review recent major advances in convex optimization and convexification techniques for G&C of vehicular systems, focusing primarily on three important application fields: (1) Space vehicles for powered descent guidance, small body landing, rendezvous and proximity operations, orbital transfer, spacecraft reorientation, space robotics and manipulation, spacecraft formation flying, and station keeping; (2) Air vehicles including hypersonic/entry vehicles, missiles and projectiles, launch/ascent vehicles, and low-speed air vehicles; and (3) Motion control and powertrain control of ground vehicles. Throughout the paper, we draw figures that illustrate the basic mission concepts and objectives, introduce key equations that characterize the feature of each class of problems and approaches, and present tables that summarize similarities and distinctions among the problems, ideas, and methods. Where available, we provide comparative analyses and reveal correlations between different applications and technical approaches. Finally, we identify open challenges and issues, discuss potential opportunities, and make suggestions for future research directions.

Chaos control remains a crucial area of study in nonlinear dynamics due to its ability to enhance system stability and efficiency in various applications. This review thoroughly examines modern chaos control techniques and offers new insights and methods for stabilizing inherently unpredictable systems. It discusses recent advancements in chaos control, focusing on theoretical breakthroughs and practical applications. Various methods for controlling chaos are explored, including the OGY method, Delayed Feedback Control (DFC), Proportional–Integral–Derivative (PID) control, Sliding Mode Control (SMC), and some unconventional techniques, evaluating their effectiveness in different chaotic systems. By analyzing the literature, this review highlights the potential of chaos control techniques to enhance system predictability and reliability, opening up promising paths for future research.

Operating electrolyzers for producing green hydrogen is a critical emerging issue because of either the broader use of hydrogen for several scopes or the short life span and efficiency of these components. Digital Twin offers a new opportunity to effectively face these problems by improving online control and providing fault detection, diagnosis, and prediction services. Since the Digital Twin is, in fact, a virtual mirror of a real system continuously updated by information received from the field, it allows it to swiftly react to small signals of departure from standard or optimal conditions. Although Digital Twins are widely applied in different fields, comprehensive guidance on developing and designing a Digital Twin in the literature is still lacking. This manuscript aims to provide a comprehensive guide on how to build the Digital Twin of a PEM-Electrolyzer. In particular, the architecture of the Digital Twin is initially presented, then all its components are analyzed, showing the steps to be performed to build a Digital Twin for operating PEM-Electrolyser system.

The use of robots has exceeded the standard focus of manufacturing and production. Over the last decades, special robotic systems have been developed in various extreme environments, such as in the maintenance, repair or even decommissioning of large-scale, strategic facilities, important to any nation’s infrastructure, including power, space, mining, etc. The deployment areas for these robots, like nuclear fuel handling systems, are generally hazardous or unreachable for human beings. The control techniques therein will play an indispensable role in the overall performance of a robotic system as they need to answer enhanced requirements for performance, robustness, and long-term reliability, driven by the fundamental demand for safe operation in complex and hazardous environments. This also needs an understanding of the enhanced industrial standards and requirements for the research, development, design and use of control systems in such environments. The control systems need to be designed specifically capable of tackling different practical control challenges caused by extreme environmental factors. This special section is designed and motivated to bridge the gap between the research community and application engineers, and to help connect control theory, control applications and industrial requirements/regulations.

In this tutorial we study a safety analog of the classical zero-sum differential game with positive definite penalties on the state and the two inputs. Consider a nonlinear system affine in two inputs, which are called “offender” and “defender.” Let the inputs have the opposing objectives in relation to an infinite-time cost which, in addition to penalizing the inputs of both agents, incorporates a safety index of the system (a barrier function), with the defender aiming to maximize the system safety and the offender aiming to minimize it. If there is a pair of (offender, defender) non-Nash feedback policies of the $L_{g}h$ form with a safe outcome, namely, where the defender maintains safety while the offender fails to violate safety, then there exists an inverse optimal pair of policies that attain a Nash equilibrium relative to the safety minimax objective. In the tutorial we study both deterministic and stochastic offenders. The deterministic offender applies its feedback through its deterministic input value, while the stochastic offender applies its feedback through its incremental covariance. In addition to Nash policies for a minimax offender–defender formulation, we provide feedback laws for the defender, in the scenario where the offender action is unrestricted by optimality, and where the defender ensures input-to-state safety in the deterministic and stochastic senses. This tutorial is derived from our recent article on inverse optimal safety filters, by setting the nominal control to zero and declaring the disturbance to be the offender agent.

Among several illustrative examples, one is particularly interesting and unconventional. We consider a safety game played on a unicycle vehicle between its two inputs: the angular velocity and the linear velocity, as the opposing players. We consider two scenarios. In the first, the angular velocity, acting as an offender, attempts to run the vehicle into an obstacle by steering, while the linear velocity, acting as a defender, drives the vehicle forward or in reverse to prevent the vehicle being run into the obstacle. In the second scenario, the linear velocity acts as an offender and angular velocity acts as a defender (in the deterministic case by varying the heading rate; in the stochastic case by varying the variance of a white noise driving the heading rate). A “wind” towards the obstacle advantages the offender in both scenarios. The input policies derived are optimal in the sense of their opposite objectives, under the best possible policy of the opponent, under meaningful costs on their actions. The linear velocity input prevails, whether acting in the role of a defender, in which case the collision with the obstacle is prevented, or in the role of an offender, in which case the collision with the obstacle is achieved.

In the digital transformation era, and particularly in Industry 5.0, humans play an active role in industrial cyber–physical systems (CPS) since they are the most flexible piece in such automated systems. However, their integration is not easy and constitutes a relevant challenge, presenting different requirements according to the activities they execute and the related integration levels, i.e., Human-in-the-Loop (HitL) and Human-in-the-Mesh (HitM). Besides the use of human-centric design approaches, the use of digital technologies, namely Internet of Things, Artificial Intelligence, virtual and augmented reality and collaborative robotics, can contribute to empower humans to perform their operations in a faster and more efficient manner. This paper discusses how emergent digital technologies can enhance a more symbiotic integration of humans in industrial CPS, contributing with the analysis of different aspects and concerns that must be considered to properly enable the HitL and HitM integration levels in CPS. Four experimental case studies are presented to demonstrate the feasibility of using digital technologies to enhance the human-CPS integration, covering HitL and HitM levels. Furthermore, some challenges related to the human-integration factors affected by the digital technologies in such environments are briefly discussed and pointed out as research directions.

This paper presents a unified framework for exponential stability analysis of linear stationary systems with irrational transfer functions in the space of an arbitrary number of unknown parameters. Systems described by irrational transfer functions may be of infinite dimension, typically having an infinite number of poles and/or zeros, rendering their stability analysis more challenging as compared to their finite-dimensional counterparts. The analysis covers a wide class of distributed parameter systems, time delayed systems, or even fractional systems. First, it is proven that, under mild hypotheses, new poles may appear to the right of a vertical axis of abscissa $\gamma$ (imaginary axis, when $\gamma =0$) through a continuous variation of parameters only if existing poles to the left of $\gamma$ cross the vertical axis. Hence, by determining parametric values for which the crossing occurs, known as stability crossing sets (SCS), the entire parametric space is separated into regions within which the number of right-half poles (including multiplicities) is invariant. Based on the aforementioned result, a constraint satisfaction problem is formulated and a robust estimation algorithm, from interval arithmetics that uses contraction and bisection, is used to solve it. Applications are provided for determining the SCS of (i) a controlled parabolic 1D partial differential equation, namely the heat equation, in finite and semi-infinite media, (ii) time-delay rational systems with distributed and retarded type delays, (iii) fractional systems, providing stability results even for incommensurate differentiation orders.

The monitoring process for complex infrastructure requires collecting various data sources with varying time scales, resolutions, and levels of abstraction. These data sources include data from human inspections, historical failure records, cost data, high-fidelity physics-based simulations, and online health monitoring. Such heterogeneity presents significant challenges in implementing a diagnostic and prognostic framework for decision-making regarding maintenance (and other life cycle actions). The core challenge lies in the effective integration of physical information and data-driven models, aiming to synergize their strengths to overcome individual limitations. One possible solution is to propose an approach that considers the strengths and limitations of each data source, as well as their compatibility with each other. The flexibility and efficacy of contemporary learning approaches can be used with more systematic and informative physics-based models that draw on domain expertise. This represents an inherent desire to base all inferences on both our engineering knowledge and monitoring data that is at our disposal. In this context, the article reviews recent advances in this field, particularly in physics-based and deep learning techniques. It looks at new theories and models developed in the last five years, especially those used in system health monitoring, predicting damage, and planning maintenance. These new methods are proving to be more accurate and efficient than older, more traditional techniques. However, there are still challenges to be addressed. These include the need for high-quality data, finding the right balance between accuracy and the time it takes to compute, and effectively combining physical models with data-driven models. The paper calls for further research into methods that can handle large amounts of complex data and consider uncertainties in both the models and the data. Finally, it highlights the need to explore how these models can be adapted for different systems and used in real-time applications.