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Data-driven output regulation control for constrained linear systems

Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Predefined-Time Distributed Fault-Tolerant control for Nonlinear Multiagent systems Suffering From Nonaffine Faults

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3300-3311页

作者： Cheng, Shuai Xin, Bin Chen, Jie Deng, Fang Qingdao University School of Automation Qingdao266071 China Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing100081 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Department of Control Science and Engineering Shanghai200092 China

This article investigates the distributed predefined-time (PT) fault-tolerant control for nonlinear multiagent systems (NNMSs) with nonaffine faults. A novel distributed PT control scheme is proposed based on a new PT theorem. The switching functions are introduced into distributed control signals to avoid the singularity problem. The second-order filter technology is designed to solve "explosion of complexity" issues. The newly designed PT compensation signals are utilized to eliminate filter errors. The proposed distributed PT controller based on local information can guarantee that all signals of the closed-loop system are PT bounded, and consensus errors of NNMSs can be enforced to a small region around the origin within a predefined time. Compared with the finite/fixed time, the predefined time is an exact value given in advance, which remains constant regardless of initial states and control parameters. Finally, two comparative simulations are provided to demonstrate the presented strategy. © 2013 IEEE.

关键词： Adaptive control systems

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Observer-based event-triggered formation tracking control for second-order multi-agent systems in constrained region

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Science China(Information Sciences) 2025年第2期68卷 151-169页

作者： Fenglan SUN Zhonghua XU Wei ZHU ■ KURTHS Key Lab of Intelligent Analysis and Decision on Complex Systems School of ScienceChongqing University of Posts and Telecommunications Key Lab of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of AutomationChongqing University of Posts and Telecommunications Department of Complexity Science Potsdam Institute for Climate Impact Research Institute of Physics Humboldt University of Berlin

In this paper, an event-triggered time-varying formation tracking control for a class of second-order nonlinear multiagent systems(MAS) operating within a constrained region is investigated. To mitigate the negative effects of external unknown disturbance, a novel disturbance observer with performance guarantees is proposed, enabling precise disturbance *** the artificial potential field(APF) method, a repulsive potential function is introduced to prevent inter-agent collisions as well as collisions with environmental obstacles. To reduce continuous communication and frequent system updates, a sliding mode technique is incorporated into the formation tracking controller, utilizing an event-triggered mechanism. The controller is also applicable to the formation control of MAS in switching-constrained regions. The achievement of the specified timevarying geometric formation is rigorously demonstrated through the Lyapunov framework. Numerical simulations are presented to validate the effectiveness of the theoretical results.

关键词： formation tracking control region constraints repulsive potential function event-triggered mechanism

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Deep Reinforcement Learning-Assisted Robust Cubature Kalman Filter for Power System Dynamic State Estimation With Multi-Rate Measurements

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IEEE Transactions on automation Science and Engineering 2025年 22卷 14346-14357页

作者： Gu, Haoli Liu, Shichao Chen, Bo Wang, Rusheng Yu, Li Kaynak, Okyay Zhejiang University of Technology Department of Automation Hangzhou310023 China Zhejiang Key Laboratory of Intelligent Perception and Control for Complex Systems Hangzhou310023 China Ningbo University of Technology Ningbo315201 China Bogazici University Istanbul34342 Turkey

The coexistence of high-frequency phasor measurement units (PMUs) and conventional SCADA systems raises the challenge of heterogenous-source and multi-rate measurements which significantly degrades the performance of power system dynamic state estimation. In this work, a deep reinforcement learning (DRL) assisted robust cubature Kalman filtering (CKF) scheme is proposed to handle measurements from hybrid sources and with different time scales. In specific, a multi-rate measurement function reconstruction approach is designed with an independent discretization mechanism to lift the present limitation of requiring an integer multiple relationship of the sampling rates from multiple sources in most of existing works. Embedded with this discretization mechanism, a deep reinforcement learning assisted two-parameter linear exponential smoothing method is proposed to reconstruct the slow measurement model with online adjustable estimation parameters. A generalized correntropy loss criterion is also included in the robust CKF to counter the non-Gaussian noise and the noise distribution variation caused by the reconstruction. Comparisons results demonstrate that the proposed DRL-based robust CKF method can achieve better accuracy and robustness under various operating scenarios. © 2004-2012 IEEE.

关键词： Deep reinforcement learning

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Stability and bifurcation analysis of delayed tri-trophic food chain model in poisoned environment with fear effect and additional food

Stability and bifurcation analysis of delayed tri-trophic fo...

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作者： Ramasamy, Sivasamy Banjerdpongchai, David Park, PooGyeon Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Republic of

One of the most important goals of theoretical ecologists is to find a strategy for controlling the chaos in ecological models to maintain healthy ecosystems. We investigate the influence of fear and the supply of additional food to predators in a delayed tri-trophic food chain model, where interference among the species is determined by Holling type-II functional response. The proposed model incorporates the fact that the growth rates of prey and middle predators are decreased due to the fear of middle and special predators, respectively. The additional food is supplied to both predators by either nature or external agencies. Furthermore, the poisoned prey in the predator's environment is taken into account. In the proposed model, we use two time delays: one for the growth term of the middle predator and another for the gestation delay of the special predator. We determine the conditions for the existence of ecologically feasible equilibrium points and their local and global stability. In addition, we establish the conditions for the existence of Hopf bifurcation around interior equilibrium to seek periodic behavior in nondelayed and delayed models. Numerical investigations are performed to justify the proposed theoretical findings through phase portraits, time series of solutions, and bifurcation diagrams. We observe that the chaotic dynamics of a tri-trophic food chain model can be controlled by the proper choice of the fear effect and by supplying additional food parameters. As a result, our findings provide more ecological insights into the dynamics of the delayed tri-trophic food chain model with the fear effect and additional food supply. © 2023 John Wiley & Sons, Ltd.

关键词： Hopf bifurcation

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Conditional Disturbance Compensation-Based Fault-Tolerant Group Consensus control for MASs: An Event-Based Switching Method

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IEEE Transactions on automation Science and Engineering 2025年 22卷 13828-13841页

作者： Liu, Pei-Ming Guo, Xiang-Gui Wang, Jian-Liang Che, Wei-Wei Wu, Zheng-Guang University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering Beijing100083 China University of Science and Technology Beijing Shunde Innovation School Foshan528000 China Beihang University Hangzhou Innovation Institute Autonomous Intelligent Systems Department Hangzhou310051 China Northeastern University College of Information Science and Engineering Shenyang266071 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou310027 China

With respect to the group consensus control for multi-agent systems (MASs) with disturbances and actuator nonidentical and unknown direction faults (NUDFs), this paper proposes a novel fault-tolerant control scheme by incorporating an event-based switching function and a saturation function. The event-based switching function is implemented to directly switch to the desired working mode, thereby reducing invalid switching after a reverse fault. Concurrently, the saturation function, acting at the software level, can not only prevent the instantaneous impact of excessive reverse input caused by a reverse fault, but also eliminate input peaking and state chattering after switching the working mode. It is worth mentioning that the proposed switching-based fault-tolerant control method can avoid the control chocks and excessive control gains as in the existing methods by using Nussbaum functions and thus improve the transient performance. Additionally, for disturbances, the designed conditional disturbance compensation mechanism ensures a smaller steady-state error, and less energy consumption by utilizing beneficial disturbances and compensating for harmful ones. Besides, the conditional compensation mechanism also reduces the saturation error. Finally, the effectiveness of the proposed control scheme are verified through a simulation example of uncrewed aerial vehicles (UAVs). © 2004-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Privacy-preserving distributed estimation for interconnected dynamic systems

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Automatica 2025年 177卷

作者： Zhang, Yuchen Chen, Bo Wang, Jianzheng Yu, Li Department of Automation Zhejiang University of Technology Hangzhou310023 China Zhejiang Key Laboratory of Intelligent Perception and Control for Complex Systems Hangzhou310023 China

This paper investigates the problem of privacy protection in distributed estimation for interconnected dynamic systems. The exchange of information between subsystems during weighted sum aggregation poses significant privacy risks to distributed estimation. To address these concerns, we propose a noise contamination mechanism for private matrix–vector multiplication and private weighted sum aggregation. This mechanism decomposes the matrix into two column-rank-deficient matrices and injects noises derived from their null spaces to protect the confidentiality of the vector during the matrix–vector multiplication. Several conditions are established to ensure that the mechanism achieves complete confidentiality. Subsequently, we design a double-layer encryption scheme for private weighted sum aggregation with hidden weights, incorporating private matrix–vector multiplication and the Paillier cryptosystem. Furthermore, we develop privacy-preserving distributed estimators for interconnected dynamic systems using the proposed aggregation schemes. Our analysis demonstrates that both subsystems and their distributed estimators achieve effective privacy protection. Finally, an illustrative example is provided to validate the effectiveness of the proposed methods. © 2025 Elsevier Ltd

关键词： Differential privacy

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Certificated Actor-Critic: Hierarchical Reinforcement Learning with control Barrier Functions for Safe Navigation

arXiv

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arXiv 2025年

作者： Xie, Junjun Zhao, Shuhao Hu, Liang Gao, Huijun Department of Automation School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

control Barrier Functions (CBFs) have emerged as a prominent approach to designing safe navigation systems of robots. Despite their popularity, current CBF-based methods exhibit some limitations: optimization-based safe control techniques tend to be either myopic or computationally intensive, and they rely on simplified system models;conversely, the learning-based methods suffer from the lack of quantitative indication in terms of navigation performance and safety. In this paper, we present a new model-free reinforcement learning algorithm called Certificated Actor-Critic (CAC), which introduces a hierarchical reinforcement learning framework and well-defined reward functions derived from CBFs. We carry out theoretical analysis and proof of our algorithm, and propose several improvements in algorithm implementation. Our analysis is validated by two simulation experiments, showing the effectiveness of our proposed CAC algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Capturing Aerodynamic Characteristics of ATTAS Aircraft with Evolving intelligent System

arXiv

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arXiv 2025年

作者： Soylu, Aydoğan Kumbasar, Tufan Control and Automation Engineering Department Istanbul Technical University Istanbul Turkey AI and Intelligent Systems Laboratory Istanbul Technical University Istanbul Turkey

Accurate modeling of aerodynamic coefficients is crucial for understanding and optimizing the performance of modern aircraft systems. This paper presents the novel deployment of an Evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN) for modeling the aerodynamic coefficients of the ATTAS aircraft to express the aerodynamic characteristics. eT2QFNN can represent the nonlinear aircraft model by creating multiple linear submodels with its rule-based structure through an incremental learning strategy rather than a traditional batch learning approach. Moreover, it enhances robustness to uncertainties and data noise through its quantum membership functions, as well as its automatic rule-learning and parameter-tuning capabilities. During the estimation of the aerodynamic coefficients via the flight data of the ATTAS, two different studies are conducted in the training phase: one with a large amount of data and the other with a limited amount of data. The results show that the modeling performance of the eT2QFNN is superior in comparison to baseline counterparts. Furthermore, eT2QFNN estimated the aerodynamic model with fewer rules compared to Type-1 fuzzy counterparts. In addition, by applying the Delta method to the proposed approach, the stability and control derivatives of the aircraft are analyzed. The results prove the superiority of the proposed eT2QFNN in representing aerodynamic coefficients. © 2025, CC BY-NC-SA.

关键词： Fuzzy rules

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Capturing Aerodynamic Characteristics of ATTAS Aircraft with Evolving intelligent System

Capturing Aerodynamic Characteristics of ATTAS Aircraft with...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Aydoğan Soylu Tufan Kumbasar Control and Automation Engineering Department Istanbul Technical University Istanbul Turkey AI and Intelligent Systems Laboratory Istanbul Technical University Istanbul Turkey

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

关键词： Training Uncertainty Atmospheric modeling Aerodynamics Fuzzy neural networks Stability analysis Robustness Aircraft Robots Optimization

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