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Privacy-preserving filtering, control and optimization for industrial cyber-physical systems

science China(Information sciences) 2025年第4期68卷 267-283页

作者： Derui DING Qing-Long HAN Xiaohua GE Xian-Ming ZHANG Jun WANG Department of Control Science and Engineering University of Shanghai for Science and Technology School of Engineering Swinburne University of Technology Department of Computer Science City University of Hong Kong

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.

关键词： industrial cyber-physical systems privacy preservation distributed control distributed optimization power systems

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Application of feedforward and recurrent neural networks for model-based control systems

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control Theory and technology 2025年第1期23卷 91-104页

作者： Marek Krok Wojciech P.Hunek Szymon Mielczarek Filip Buchwald Adam Kolender Department of Control Science and Engineering Opole University of TechnologyOpolePoland

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.

关键词： Predictive control Linear-quadratic control Inverse problems Feedforward network Recurrent neural network Optimization

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A dynamic control decision approach for fixed-wing aircraft games via hybrid action reinforcement learning

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science China(Information sciences) 2025年第3期68卷 197-219页

作者： Xing ZHUANG Dongguang LI Hanyu LI Yue WANG Jihong ZHU Science and Technology on Electromechanical Dynamic Control Laboratory Beijing Institute of Technology Department of Precision Instrument Tsinghua University

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.

关键词： intelligent air combat unmanned aerial vehicle game dynamic control reinforcement learning

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Distributed coordination control of multi-agent systems under intermittent sampling and communication: a comprehensive survey

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science China(Information sciences) 2025年第5期68卷 289-331页

作者： Xiaohua GE Qing-Long HAN Xian-Ming ZHANG Derui DING Boda NING School of Engineering Swinburne University of Technology Department of Control Science and Engineering University of Shanghai for Science and Technology Department of Electrical and Electronic Engineering Auckland University of Technology

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.

关键词： multi-agent systems distributed coordination control sampled-data control event-triggered control resilient control denial-of-service attacks communication protocols event-triggered mechanisms

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From Static and Dynamic Perspectives:A Survey on Historical Data Benchmarks of control Performance Monitoring

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 300-316页

作者： Pengyu Song Jie Wang Chunhui Zhao Biao Huang the State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University IEEE the Department of Chemical and Materials Engineering University of Alberta

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.

关键词： control performance monitoring (CPM) data-driven method historical data benchmark (HIS) industrial process performance index static and dynamic analysis

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Adaptive torque estimation-based nonlinear H∞ control of modular robot manipulators with uncertain environments

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Neural Computing and Applications 2025年第3期37卷 1617-1631页

作者： Dong, Bo Wang, Yuge An, Tianjiao Cui, Yiming Zhu, Xinye Department of Control Science and Engineering Changchun University of Technology Changchun130012 China

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 systems generated by device vibrations while facing with unknown external disturbance. To address these challenges, a variational Bayesian (VB)-based extended Kalman filter (VBEKF) is developed as part of the MRM dynamic model to mitigate bias in torque estimation resulting from external interference within HD model joints. Furthermore, leveraging a two-player zero-sum game strategy, a robust adaptive dynamic programming (ADP) method based on single critic neural network (NN) is formulated to solve the Hamilton–Jacobi–Isaacs (HJI) equation in H∞ control problem, ultimately yielding an approximate optimal solution for H∞ control. The Lyapunov theory guarantees that the trajectory tracking error of a closed-loop MRM system remains ultimately uniformly bounded (UUB), even in the presence of unknown environmental disturbances. Ultimately, the experiment results are given to illustrate the advantages and effectiveness of the proposed method. The experimental results show that the proposed method reduces the control torque error by ∼20% compared with the existing control methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Extended Kalman filters

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Model-Free Current Predictive control for PMSMs With Ultra-Local Model Employing Fixed-Time Observer and Extremum-Seeking Method

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IEEE Transactions on Power Electronics 2025年第8期40卷 10682-10693页

作者： Lin, Xinpo Liu, Jianxing Liu, Zhuang Gao, Yabin Peretti, Luca Wu, Ligang Harbin Institute of Technology Department of Control Science and Engineering Weihai264209 China Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China KTH Royal Institute of Technology Division of Electric Power and Energy Systems Stockholm10044 Sweden

Conventional model predictive current control of permanent magnet synchronous machines (PMSMs) relies heavily on a precise mathematical model, which may be challenging to obtain in certain cases. To address this issue, this paper proposes a model-free predictive current controller for PMSMs. Specifically, the dynamic model of the motor currents is represented using two ultra-local models, which express the derivatives of a controlled output as the sum of an amplified control input and an unknown offset term. All system parameters, nonlinear terms, and unmodeled dynamics are encapsulated into two offset terms. Online estimation of these two offset terms is performed using a second-order fixed-time convergence observer without requiring exact knowledge of the system parameters in advance. In contrast to both the linear extended state observer and the super-twisting observer, the fixed-time observer enhances convergence speed while concurrently sustaining minimal chatting. To get rid of the need for model rated parameters, an extremum-seeking approach is utilized to tune the control gains in the ultra-local model, where the amplitude of the disturbance item in extremum-seeking approach is adaptively attenuated, resulting in effective reduction of the fluctuations of both the control gains and PMSM's currents in steady state. Subsequently, a predictive controller is designed using this ultra-local model. Finally, the effectiveness and advantages of the proposed control scheme are confirmed through experimental results. © 1986-2012 IEEE.

关键词： Permanent magnets

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Comparison of the feedback linearization plus LQR controller and the PID controller for greenhouse indoor climate

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International Journal of Agricultural and Biological Engineering 2025年第1期18卷 74-82页

作者： Kidist Ameha Mengesha Gang-Gyoo Jin Yung-Deug Son Department of Electrical Power and Control Engineering Adama Science and Technology UniversityAdama 888Ethiopia Department of Mechanical Facility Control Engineering Korea University of Technology and EducationChungnam 31253Republic of Korea

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.

关键词： greenhouse climate control feedback linearization plus LQR control PID control genetic algorithm

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Research on the cascade modulation method for star position simulation in a weak starlight simulator

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Optics Express 2025年第3期33卷 5627-5639页

作者： Wu, Linghao Zhang, Yu Li, Yihuan Sheng, Lan Xia, Yuxin Zhang, Siqi Gu, Siwen Department of Measurement and Control Technology and Instrumentation Changzhou Institute of Technology Changzhou213032 China Changchun University of Science and Technology Changchun130022 China

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 correction, this paper introduces a design approach for a cascaded architecture tailored to faint starlight simulators. Through the implementation of an isometric mapping technique that maps the dimming LCOS onto the surface of the imaging LCOS, the dimensionality of gray-scale adjustment for the imaging LCOS is augmented, thereby enhancing the accuracy of simulated stellar position correction. Initially, a stellar position cascaded modulation architecture is meticulously constructed to investigate the pivotal factors that impact the gray-scale resolution of the imaging LCOS within the framework of the cascaded modulation architecture. Subsequently, the optimization protocol for the concentrator system embedded within the cascaded modulation architecture is formulated. Leveraging the simulated annealing algorithm, the multi-parameter freeform surface of the concentrator system undergoes synchronous optimization. Finally, the simulation outcomes derived from the cascaded modulation architecture both prior to and subsequent to optimization clearly demonstrate that upon the implementation of the novel architecture, the accuracy of simulated stellar position correction experiences a notable enhancement of 62%. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Liquid crystals

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Constrained Networked Predictive control for Nonlinear Systems Using a High-Order Fully Actuated System Approach

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 478-480页

作者： Yi Huang Guo-Ping Liu Yi Yu Wenshan Hu the School of Electrical Engineering and Automation Wuhan University IEEE the Center for Control Science and Technology Southern University of Science and Technology the Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University

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.

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