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Influence Enhanced Sparse Coordination Graphs for Multi-Agent Reinforcement Learning

Neural Networks 2025年 188卷 107454页

作者： Zhang, Xiwen Chen, Jie Gan, Ming-Gang Chen, Haoxiang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

In contemporary Multi-Agent Reinforcement Learning (MARL), effectively enhancing the expressive capacity of value functions has been a persistent research focus. Many studies have employed value decomposition methods;however, due to the neglect of inter-agent collaboration, these methods fall short of achieving optimal performance. Subsequent research introduced coordination graphs into value decomposition methods;nevertheless, these approaches often rely on simplistic rules to evaluate inter-agent collaboration and fail to adequately describe the collaborative relationships of agents in complex environments. Consequently, we propose Influence Enhanced Sparse Coordination Graphs (IESCG) as a solution to provide insights into the aforementioned problem. In this study, we propose influence networks as quantitative descriptions of the importance of collaboration among agents, utilizing them as crucial basis for constructing the topology of Sparse Time-Varying Coordination Graphs. Additionally, we propose Recurrent Payoff Function Networks (RPFN) to incorporate temporal information while providing necessary input to influence networks. Furthermore, Sparse Graph Advantage Selection Coefficients (SGASC) are introduced to stabilize the overall value function across different time steps, ensuring training stability. Experimental investigations conducted on the StarCraft II micromanagement and MACO benchmark indicate that our algorithm not only accelerates convergence and improves winning probabilities but also exhibits more pronounced advantages in complex scenarios. © 2025

关键词： Markov processes

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Finite-time sliding mode attitude control for a reentry vehicle with blended aerodynamic surfaces and a reaction control system

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Chinese Journal of Aeronautics 2014年第4期27卷 964-976页

作者： Geng Jie Sheng Yongzhi Liu Xiangdong Key Laboratory for Intelligent Control&Decision of Complex Systems Beijing Institute of Technology School of Automation Beijing Institute of Technology

This paper proposes a finite-time robust flight controller, targeting for a reentry vehicle with blended aerodynamic surfaces and a reaction control system（RCS）. Firstly, a novel finite-time attitude controller is pointed out with the introduction of a nonsingular finite-time sliding mode manifold. The attitude tracking errors are mathematically proved to converge to zero within finite time which can be estimated. In order to improve the performance, a second-order finite-time sliding mode controller is further developed to effectively alleviate chattering without any deterioration of robustness and accuracy. Moreover, an optimization control allocation algorithm, using linear programming and a pulse-width pulse-frequency（PWPF） modulator, is designed to allocate torque commands for all the aerodynamic surface deflections and on–off switching-states of RCS *** are provided for the reentry vehicle considering uncertain parameters and external disturbances for practical purposes, and the results demonstrate the effectiveness and robustness of the attitude control system.

关键词： Chattering alleviation control allocation Finite-time convergence Flight control systems Second-order sliding mode Singularity elimination Sliding mode control

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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Adaptive Robust Dead-Zone Compensation control of Electro-Hydraulic Servo systems with Load Disturbance Rejection

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Journal of systems Science & complexity 2015年第2期28卷 341-359页

作者： HE Yudong WANG Junzheng HAO Renjian Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

A backstepping method based adaptive robust dead-zone compensation controller is pro- posed for the electro-hydraulic servo systems （EHSSs） with unknown dead-zone and uncertain system parameters. Variable load is seen as a sum of a constant and a variable part. The constant part is regarded as a parameter of the system to be estimated real time. The variable part together with the friction are seen as disturbance so that a robust term in the controller can be adopted to reject them. Compared with the traditional dead-zone compensation method, a dead-zone compensator is incor- porated in the EH$S without constructing a dead-zone inverse. Combining backstepping method, an adaptive robust controller （ARC） with dead-zone compensation is formed. An easy-to-use ARC tuning method is also proposed after a further analysis of the ARC structure. Simulations show that the proposed method has a splendid tracking performance, all the uncertain parameters can be estimated, and the disturbance has been rejected while the dead-zone term is well estimated and compensated.

关键词： Adaptive robust control dead-zone servo systems tuning method. compensation disturbance rejection electro-hydraulic

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Optimal fusion estimation for stochastic systems with cross-correlated sensor noises

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Science China(Information Sciences) 2017年第12期60卷 57-70页

作者： Liping YAN Yuanqing XIA Mengyin FU School of Automation Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Institute of Technology

This paper is concerned with the optimal fusion of sensors with cross-correlated sensor *** taking linear transformations to the measurements and the related parameters, new measurement models are established, where the sensor noises are decoupled. The centralized fusion with raw data, the centralized fusion with transformed data, and a distributed fusion estimation algorithm are introduced, which are shown to be equivalent to each other in estimation precision, and therefore are globally optimal in the sense of linear minimum mean square error(LMMSE). It is shown that the centralized fusion with transformed data needs lower communication requirements compared to the centralized fusion using raw data directly, and the distributed fusion algorithm has the best flexibility and robustness and proper communication requirements and computation complexity among the three algorithms(less communication and computation complexity compared to the existed distributed Kalman filtering fusion algorithms). An example is shown to illustrate the effectiveness of the proposed algorithms.

关键词： optimal estimation distributed fusion Kalman filter cross-correlated noises linear transformation

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Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

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Journal of systems Engineering and Electronics 2013年第4期24卷 655-665页

作者： Fang Deng Jie Chen Chen Chen School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems

An adaptive unscented Kalman filter （AUKF） and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter （UKF） when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.

关键词： parameter estimation state estimation unscented Kalman filter (UKF) strong tracking filter wavelet transform.

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A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets

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Chinese Journal of Aeronautics 2014年第3期27卷 622-633页

作者： Zhang Xing Chen Jie Xin Bin Peng Zhihong School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

The problem of generating optimal paths for curvature-constrained unmanned aerial vehicles （UAVs） performing surveillance of multiple ground targets is addressed in this paper. UAVs are modeled as Dubins vehicles so that the constraints of UAVs＇ minimal turning radius can be taken into account. In view of the effective surveillance range of the sensors equipped on UAVs, the problem is formulated as a Dubins traveling salesman problem with neighborhood （DTSPN）. Considering its prohibitively high computational complexity, the Dubins paths in the sense of terminal heading relaxation are introduced to simplify the calculation of the Dubins distance, and a boundary-based encoding scheme is proposed to determine the visiting point of every target neighborhood. Then, an evolutionary algorithm is used to derive the optimal Dubins tour. To further enhance the quality of the solutions, a local search strategy based on approximate gradient is employed to improve the visiting points of target neighborhoods. Finally, by a minor modification to the individual encoding, the algorithm is easily extended to deal with other two more sophisticated DTSPN variants （multi-UAV scenario and multiple groups of targets scenario）. The performance of the algorithm is demonstrated through comparative experiments with other two state-of-the-art DTSPN algorithms identified in literature. Numerical simulations exhibit that the algorithm proposed in this paper can find high-quality solutions to the DTSPN with lower computational cost and produce significantly improved performance over the other algorithms.

关键词： Approximate gradient Dubins traveling salesmanproblem with neighborhood Local search Memetic algorithm Unmanned aerial vehicles

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Further results on cloud control systems

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Science China(Information Sciences) 2016年第7期59卷 232-236页

作者： Yuanqing XIA Yongming QIN Di-Hua ZHAI Senchun CHAI School of Automation Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Institute of Technology

This paper is devoted to further investigating the cloud control systems(CCSs). The benefits and challenges of CCSs are provided. Both new research results of ours and some typical work made by other researchers are p... 详细信息

关键词： cloud control systems cloud computing cyber-physical systems networked control systems big data

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Online adaptive Q-learning method for fully cooperative linear quadratic dynamic games

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Science China(Information Sciences) 2019年第12期62卷 164-177页

作者： Xinxing LI Zhihong PENG Lei JIAO Lele XI Junqi CAI School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

A model-based offline policy iteration(PI) algorithm and a model-free online Q-learning algorithm are proposed for solving fully cooperative linear quadratic dynamic games. The PI-based adaptive Q-learning method can learn the feedback Nash equilibrium online using the state samples generated by behavior policies, without sending inquiries to the system model. Unlike the existing Q-learning methods, this novel Q-learning algorithm executes both policy evaluation and policy improvement in an adaptive *** prove the convergence of the offline PI algorithm by proving its equivalence to Newton's method while solving the game algebraic Riccati equation(GARE). Furthermore, we prove that the proposed Q-learning method will converge to the Nash equilibrium under a small learning rate if the method satisfies certain persistence of excitation conditions, which can be easily met by suitable behavior policies. Our simulation results demonstrate the good performance of the proposed online adaptive Q-learning algorithm.

关键词： adaptive dynamic programming reinforcement learning Q-learning fully cooperative linear quadratic dynamic games policy iteration off-policy

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The Separate Meter in Separate Meter Out control System Using Dual Servo Valves Based on Indirect Adaptive Robust Dynamic Surface control

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Journal of systems Science & complexity 2019年第2期32卷 557-576页

作者： CHEN Guangrong WANG Junzheng WANG Shoukun ZHAO Jiangbo SHEN Wei The Key Laboratory of Drive and Control of Servo Motion Systems Beijing Institute of Technology The Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Due to the demand for energy efficiency in electro-hydraulic systems, the separate meter in and separate meter out(SMISMO) control system attracts vast attention. In this paper, the SMISMO control system was configured with two servo valves to control the meter in and meter out *** designing two of the proposed indirect adaptive robust dynamic surface controllers(IARDSC)for the working-side and off-side system and setting the coupled items as estimated parameters, the SMISMO control system was decoupled into two subsystems completely. Here, indirect adaptive robust control(IARC) was employed to address the internal parameter uncertainties and external *** surface control(DSC) was utilized in the backstepping design procedure of IARC to deal with the inherent ‘explosion of terms' problem. As thus, the proposed IARDSC could simplify the design procedure, decrease the computational cost, and achieve an improved control performance in practical use. Finally, experimental results validated the effectiveness of proposed IARDSC and showed that the proposed SMISMO control system could provide a possibility to save more energy.

关键词： Dynamic surface control indirect adaptive robust control parameter estimation SMISMO stability analysis

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