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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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Coalition formation problem: a capability-centric analysis and general model

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science China(Information sciences) 2024年第11期67卷 180-193页

作者： Jie CHEN Miao GUO Bin XIN Qing WANG Shengyu LU Yipeng WANG Yulong DING School of Automation Beijing Institute of Technology National Key Lab of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering Tongji University

Coalition formation(CF) refers to reasonably organizing robots and/or humans to form coalitions that can satisfy mission requirements, attracting more and more attention in many fields such as multirobot collaboration and human-robot collaboration. However, the analysis on CF problems remains *** provide a valuable study reference for researchers interested in CF, this paper proposed a capabilitycentric analysis of the CF problem. The key problem elements of CF are firstly extracted by referencing the concepts of the 5W1H method. That is, objects(who) form coalitions(what) to accomplish missions(why) by aggregating capabilities(how) in a specific environment(where-when). Then, a multi-view analysis of these elements and their correlation in terms of capabilities is proposed through various logic diagrams, structure charts, etc. Finally, to facilitate a deeper understanding of capability-centric CF, a general mathematical model is constructed, demonstrating how the different concepts discussed in this analysis contribute to the overall model.

关键词： coalition formation capability aggregation capability metric mission requirement environmental effect

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Time-Varying Formation Tracking control of Heterogeneous Multi-Agent Systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems control theory Autonomous systems Stability

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Transferable adversarial slow feature extraction network for few-shot quality prediction in coal-to-ethylene glycol process

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Chinese Journal of Chemical engineering 2024年第7期71卷 258-271页

作者： Cheng Yang Chao Jiang Guo Yu Jun Li Cuimei Bo School of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing 211816China Department of Intelligent Manufacturing Nanjing Tech UniversityNanjing 210009China

In the coal-to-ethylene glycol(CTEG)process,precisely estimating quality variables is crucial for process monitoring,optimization,and control.A significant challenge in this regard is relying on offline laboratory analysis to obtain these variables,which often incurs substantial monetary costs and significant time *** resulting few-shot learning scenarios present a hurdle to the efficient development of predictive *** address this issue,our study introduces the transferable adversarial slow feature extraction network(TASF-Net),an innovative approach designed specifically for few-shot quality prediction in the CTEG ***-Net uniquely integrates the slowness principle with a deep Bayesian framework,effectively capturing the nonlinear and inertial characteristics of the CTEG ***,the model employs a variable attention mechanism to identify quality-related input variables adaptively at each time step.A key strength of TASF-Net lies in its ability to navigate the complex measurement noise,outliers,and system interference typical in CTEG *** learning strategy using a min-max game is adopted to improve its robustness and ability to model irregular industrial data accurately and ***,an incremental refining transfer learning framework is designed to further improve few-shot prediction performance achieved by transferring knowledge from the pretrained model on the source domain to the target *** effectiveness and superiority of TASF-Net have been empirically validated using a real-world CTEG *** with some state-of-the-art methods,TASF-Net demonstrates exceptional capability in addressing the intricate challenges for few-shot quality prediction in the CTEG process.

关键词： Chemical process Neural networks Slowness principle Transfer learning Prediction

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Mixed H_(∞)/Passive Exponential Synchronization for Delayed Memristive Neural Networks with Switching Event-Triggered control

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Journal of Systems science & Complexity 2024年第1期37卷 294-317页

作者： WU Wenhuang GUO Lulu CHEN Hong Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

This paper is devoted to event-triggered synchronization of delayed memristive neural networks with H∞and passivity *** aim is to guarantee the exponential synchronization and mixed H∞and passivity control for memristive neural networks by using event-triggered ***,a switching system is constructed under the event-triggered control ***,by adopting a piece-wise Lyapunov functional,a sufficient condition is established for the exponential synchronization and mixed H_(∞)and passivity ***,an event-triggered controller design scheme is proposed using matrix decoupling ***,the effectiveness of the designed controller is exemplified by a numerical example.

关键词： Event-triggered control exponential synchronization memristive neural networks time delays.

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EASI: Evolutionary Adversarial Simulator Identification for Sim-to-Real Transfer 38

EASI: Evolutionary Adversarial Simulator Identification for ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dong, Haoyu Fu, Huiqiao Xu, Wentao Zhou, Zhehao Chen, Chunlin Department of Control Science and Intelligent Engineering School of Management and Engineering Nanjing University China

Reinforcement Learning (RL) controllers have demonstrated remarkable performance in complex robot control tasks. However, the presence of reality gap often leads to poor performance when deploying policies trained in simulation directly onto real robots. Previous sim-to-real algorithms like Domain Randomization (DR) requires domain-specific expertise and suffers from issues such as reduced control performance and high training costs. In this work, we introduce Evolutionary Adversarial Simulator Identification (EASI), a novel approach that combines Generative Adversarial Network (GAN) and Evolutionary Strategy (ES) to address sim-to-real challenges. Specifically, we consider the problem of sim-to-real as a search problem, where ES acts as a generator in adversarial competition with a neural network discriminator, aiming to find physical parameter distributions that make the state transitions between simulation and reality as similar as possible. The discriminator serves as the fitness function, guiding the evolution of the physical parameter distributions. EASI features simplicity, low cost, and high fidelity, enabling the construction of a more realistic simulator with minimal requirements for real-world data, thus aiding in transferring simulated-trained policies to the real world. We demonstrate the performance of EASI in both sim-to-sim and sim-to-real tasks, showing superior performance compared to existing sim-to-real algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles

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Autonomous intelligent Systems 2025年第1期5卷 1-20页

作者： Liu, Zhengqin Lei, Jinlong Yi, Peng Hong, Yiguang Department of Control Science and Engineering Tongji University Shanghai201804 China The Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201804 China

Lately, there has been a lot of interest in game-theoretic approaches to the trajectory planning of autonomous vehicles (AVs). But most methods solve the game independently for each AV while lacking coordination mechanisms, and hence result in redundant computation and fail to converge to the same equilibrium, which presents challenges in computational efficiency and safety. Moreover, most studies rely on the strong assumption of knowing the intentions of all other AVs. This paper designs a novel autonomous vehicle trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated trajectory planning by exploiting vehicle-to-everything (V2X) technology. Firstly, the trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define the interaction fairness of the planned trajectories and prove that interaction-fair trajectories correspond to the variational equilibrium (VE) of this game. Subsequently, we propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, in which each CAV optimizes its individual trajectory based on neighboring CAVs’ information shared through V2X, and the roadside unit takes the role of updating multipliers for collision avoidance constraints. The approach can significantly improve computational efficiency through parallel computing among CAVs, and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, we conduct Monte Carlo experiments in multiple situations at an intersection, where the empirical results show the advantages of SVEP, including the fast computation speed, a small communication payload, high scalability, equilibrium concordance, and safety, making it a promising solution for trajectory planning in connected traffic scenarios. To the best of our knowledge, this is the first study to achieve semi-distributed solving of a game with coupled constr

关键词： Autonomous vehicles

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Reinforcement Learning with Neighborhood Search and Self-learned Rules 13

Reinforcement Learning with Neighborhood Search and Self-lea...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Yin, Xiao Zhu, Yuanyang Chen, Chunlin Nanjing University Department of Control Science and Intelligent Engineering Nanjing210093 China

ISBN: (纸本)9798350361674

Reinforcement learning (RL), induced by the exploration-exploitation dilemma, usually requires enough interaction experiences with the environment to improve performance. Rule-based methods utilize the internal expertise of tasks to benefit the learning process of the target task and can be shared with similar goal-setting tasks, which is also one form of knowledge transfer with better interpretability. However, symbolic rules or prior knowledge of the task may not be obtained in advance for most control tasks. In this paper, we propose an effective approach named neighborhood search and self-learned rules (RL-NSSR), where the neighborhood search library and the rules can be learned from the samples of a set of collected trajectories. In the RL- NSSR, first, the expert policy is obtained by training the RL agent in the discretized continuous state-action space task for collecting the trajectories data. Second, we extract the rules using the collected data via ensemble decision tree methods, and select the states of the trajectories with higher cumulative rewards as the neighborhood states to speed up the learning process. Third, we employ both the neighborhood search and the self-learned symbolic rules in a fine-grained way and a rough way, respectively, to guide the exploration strategy for accelerating the learning process. Experiments conducted on several groups of locomotion tasks by the MuJoCo physics engine demonstrate that the proposed RL-NSSR approach can achieve improved control performances for both simple and complex tasks. © 2024 IEEE.

关键词： Self-supervised learning

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of Systems science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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Dynamic Capacitated Vehicle Routing Problem with Stochastic Requests Using Deep Reinforcement Learning

Dynamic Capacitated Vehicle Routing Problem with Stochastic ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Tang, Kaiqiang Fu, Huiqiao Liu, Jiasheng Deng, Guizhou Lu, Yuanyang Chen, Chunlin School of Management and Engineering Nanjing University Department of Control Science and Intelligent Engineering Nanjing210093 China

ISBN: (纸本)9781665410205

With the rapid growth of industries like e-commerce, food delivery, and ride-hailing, research on the Vehicle Routing Problem (VRP) is becoming increasingly relevant. However, most of the research in the field of VRP is based on static delivery tasks. In such tasks, the information about customer and order requests is provided before the delivery vehicle departs from the depot, and it remains constant during delivery. However, in the real world, the delivery tasks are often dynamic, where only some orders are known before the vehicle departs from the depot, and the rest of the orders are disclosed over time [1]. © 2024 IEEE.

关键词： Stochastic systems

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