Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection b...
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Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.
In this paper,a new study concerning the usage of artificial neural networks in the control application is *** is shown,that the data gathered during proper operation of a given control plant can be used in the learni...
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In this paper,a new study concerning the usage of artificial neural networks in the control application is *** is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control ***,the instances driven by neural networks have the ability to outperform the original analytically driven *** different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical *** addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.
Autonomous decision-making is crucial for aircraft to achieve quick victories in diverse scenarios. Based on a 6-degree-of-freedom aircraft model, this paper proposes a decoupled guidance and control theory for autono...
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Autonomous decision-making is crucial for aircraft to achieve quick victories in diverse scenarios. Based on a 6-degree-of-freedom aircraft model, this paper proposes a decoupled guidance and control theory for autonomous aircraft maneuvering, distinguishing between close and long-range engagements. We introduce a method for heading attitude control to enhance stability during close-range interactions and a speed-based adaptive grid model for precise waypoint control in mid-to-long-range engagements. The paper transforms dynamic aircraft interactions into a Markov decision process and presents a hybrid discrete and continuous action reinforcement learning approach. This unified learning framework offers enhanced generalization and learning speed for dynamic aircraft adversarial processes. Experimental results indicate that in a symmetric environment, our approach rapidly achieves Nash equilibrium, securing over a 10% advantage. In unmanned aerial aircraft game control with higher maneuverability, the probability of gaining a situational advantage increases by more than 40%. Compared to similar methods, our approach demonstrates superior effectiveness in decision optimization and adversarial success ***, we validate the algorithm's robustness and adaptability in an asymmetric environment, showcasing its promising application potential in collaborative control of aircraft clusters.
Sampling and communication are both crucial for coordination in multi-agent systems(MASs), with sampling capturing raw data from the environment for control decision making, and communication ensuring the data is shar...
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Sampling and communication are both crucial for coordination in multi-agent systems(MASs), with sampling capturing raw data from the environment for control decision making, and communication ensuring the data is shared effectively for synchronized and informed control decisions across agents. However, practical MASs often operate in environments where continuous and synchronous data samplings and exchanges are impractical, necessitating strategies that can handle intermittent sampling and communication constraints. This paper provides a comprehensive survey of recent advances in distributed coordination control of MASs under intermittent sampling and communication, focusing on both foundational principles and state-of-the-art techniques. After introducing fundamentals, such as communication topologies,agent dynamics, control laws, and typical coordination objectives, the distinctions between sampling and communication are elaborated, exploring deterministic versus random, synchronous versus asynchronous, and instantaneous versus sequential scenarios. A detailed review of emerging trends and techniques is then presented, covering time-triggered, event-triggered,communication-protocol-based, and denial-of-service-resilient coordination control. These techniques are analyzed across various attack models, including those based on data loss, sampled data, time constraints, and topology switching. By synthesizing these developments, this survey aims to equip researchers and practitioners with a clearer understanding of current challenges and methodologies, concluding with insights into promising future directions.
In recent decades, control performance monitoring(CPM) has experienced remarkable progress in research and industrial applications. While CPM research has been investigated using various benchmarks, the historical dat...
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In recent decades, control performance monitoring(CPM) has experienced remarkable progress in research and industrial applications. While CPM research has been investigated using various benchmarks, the historical data benchmark(HIS) has garnered the most attention due to its practicality and effectiveness. However, existing CPM reviews usually focus on the theoretical benchmark, and there is a lack of an in-depth review that thoroughly explores HIS-based methods. In this article, a comprehensive overview of HIS-based CPM is provided. First, we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo: static and dynamic properties. The static property portrays time-independent variability in system output, and the dynamic property describes temporal behavior driven by closed-loop feedback. Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two ***, two mainstream solutions for CPM methods are summarized, including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior. Furthermore, this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.
Modular robot manipulators (MRMs) based on harmonic drive (HD) transmissions perform various target tasks in unknown environments, and the main challenge is to overcome the uncertain noise and controller errors of sys...
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In order to surmount the challenge wherein the gray-scale resolution of liquid crystal on silicon (LCOS) imaging within a faint starlight simulator acts as a limiting factor for the precision of stellar position corre...
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Greenhouse environmental control systems can improve the growth and quality of the plants within greenhouses by keeping a constant *** climate is a multi-input multi-output system that is significantly affected by cli...
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Greenhouse environmental control systems can improve the growth and quality of the plants within greenhouses by keeping a constant *** climate is a multi-input multi-output system that is significantly affected by climate factors like temperature,relative humidity,and carbon dioxide *** to the nonlinearity and existence of coupling among climate factors,the designed controller should provide good control *** study proposed both the feedback linearization plus linear quadratic regulator(LQR)controller and the proportional-integral-derivative(PID)controller for indoor air temperature and humidity control of a greenhouse *** nonlinear greenhouse model was transformed into its equivalent linear form using input-output feedback ***,a proportional-integral type LQR controller was designed for the linear form to achieve the overall nonlinear feedback control *** addition,the practical PID controller was designed and its gains were tuned using a genetic algorithm by considering the integral of absolute error and control deviation,and the integral of squared error and control deviation.A set of simulation works done on the nonlinear model illustrates the effectiveness of the two control *** control methods,feedback linearization plus LQR and PID,demonstrated effective performance in both setpoint tracking and disturbance *** feedback linearization plus LQR controller exhibited superior disturbance rejection capabilities,characterized by reduced perturbation peaks and faster recovery ***,the PID controller demonstrated superior setpoint tracking performance with minimal overshoot.
Dear Editor,In this letter, a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated (HOFA) systems with noises. The method can effe...
Dear Editor,In this letter, a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated (HOFA) systems with noises. The method can effectively deal with nonlinearities, constraints, and noises in the system, optimize the performance metric, and present an upper bound on the stable output of the system.
The attitude tracking control with unwinding-free performance for rigid spacecraft is studied in this article. A full-state feedback control law based on a hyperbolic sine function is developed such that the resulted ...
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