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Kernel-Based State-Space Kriging for Predictive Control

IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1263-1275页

作者： A.Daniel Carnerero Daniel R.Ramirez Daniel Limon Teodoro Alamo the Department of System Engineering and Automation University of SevilleSevilla 41020Spain the School of Engineering Tokyo Institute of TechnologyTokyo 152-8552Japan Department of System Engineering and Automation University of SevilleSevilla 41020Spain

In this paper, we extend the state-space kriging(SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions as linear combinations of past outputs. To model the nonlinear dynamics of the system, we propose the kernel-based state-space kriging(K-SSK), a new version of the SSK where kernel functions are used instead of resorting to considerations about the locality of the data. Also, a Kalman filter can be used to improve the predictions at each time step in the case of noisy measurements. A constrained tracking nonlinear model predictive control(NMPC) scheme using the black-box input-output model obtained by means of the K-SSK prediction method is proposed. Finally, a simulation example and a real experiment are provided in order to assess the performance of the proposed controller.

关键词： Data-driven methods model identification Kernel methods predictive control

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Relation-Aware Learning for Multi-Task Multi-Agent Cooperative Games

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IEEE Transactions on Games 2024年第2期17卷 1-12页

作者： Yu, Yang Yang, Likun Guo, Zhourui Ren, Yongjian Yin, Qiyue Zhang, Junge Huang, Kaiqi Center for Research on Intelligent System and Engineering Institute of Automation Chinese Academy of Sciences Beijing China Center for Research on Intelligent System and Engineering and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

Collaboration among multiple tasks is advantageous for enhancing learning efficiency in multi-agent reinforcement learning. To guide agents in cooperating with different teammates in multiple tasks, contemporary approaches encourage agents to exploit common cooperative patterns or identify the learning priorities of multiple tasks. Despite the progress made by these methods, they all assume that all cooperative tasks to be learned are related and desire similar agent policies. This is rarely the case in multi-agent cooperation, where minor changes in team composition can lead to significant variations in cooperation, resulting in distinct cooperative strategies compete for limited learning resources. In this paper, to tackle the challenge posed by multi-task learning in potentially competing cooperative tasks, we propose a novel framework called Relation-Aware Learning (RAL). RAL incorporates a relation awareness module in both task representation and task optimization, aiding in reasoning about task relationships and mitigating negative transfers among dissimilar tasks. To assess the performance of RAL, we conduct a comparative analysis with baseline methods in a multi-task StarCraft environment. The results demonstrate the superiority of RAL in multi-task cooperative scenarios, particularly in scenarios involving multiple conflicting tasks. Index Terms—Cooperation games, multi-task learning, reinforcement learning. IEEE

关键词： Reinforcement learning

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A novel stiffness optimization model of space telescopic boom based on locking mechanism

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Frontiers of Mechanical engineering 2024年第3期19卷 41-59页

作者： Kun XU Xinghan ZHUANG Zhou SU Qiuhong LIN Shouzhi REN Hang XIAO Xilun DING School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China Beijing Institute of Spacecraft System Engineering Beijing 100094China

The deployable telescopic boom,whose mass and stiffness play crucial roles,is extensively used in the design of space-deployable ***,the most existing optimal design that neglects the influence of the locking mechanisms in boom joints cannot raise the whole stiffness while reducing the boom *** tackle this challenge,a novel optimization model,which utilizes the arrangement of the locking mechanisms to achieve synchronous improvement of the stiffness and mass,is *** proposed optimization model incorporates a novel joint stiffness model developed based on an equivalent parallel mechanism that enables the consideration of multiple internal stiffness factors of the locking mechanisms and tubes,resulting in more accurate representations of the joint stiffness *** analysis shows that the proposed stiffness model achieves more than at least 11% improved accuracy compared with existing ***,case verification shows that the proposed optimization model can improve stiffness while effectively reducing mass,and it is applied in boom optimization design.

关键词： deployable telescopic boom locking mechanism stiffness model clearance error optimization model

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Parallel system Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control

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CSEE Journal of Power and Energy systems 2024年第1期10卷 13-28页

作者： Tianyun Zhang Jun Zhang Feiyue Wang Peidong Xu Tianlu Gao Haoran Zhang Ruiqi Si School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072HubeiChina State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence ***,there is,currently,nearly no standard technical framework for objective and quantitative intelligence *** this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation *** this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning(AutoRL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment *** results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.

关键词： AI quantitative intelligence assessment and self-evolution automated reinforcement learning Bayesian optimization corrective control parallel system

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Controllability of heterogeneous networked sampled-data systems

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Science China(Information Sciences) 2025年第3期68卷 302-318页

作者： Zixuan YANG Lin WANG Xiaofan WANG Guanrong CHEN Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Mechatronic Engineering and Automation Shanghai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Electrical Engineering City University of Hong Kong

This study investigates the controllability of a general heterogeneous networked sampled-data system(HNSS) consisting of nonidentical node systems, where the inner coupling between any pair of nodes can be described by a unique *** signals on control and transmission channels are sampled and held by zero-order holders, and the control sampling period of each node can be different. Necessary and sufficient controllability conditions are developed for the general HNSS, using the Smith normal form and matrix equations, respectively. The HNSS in specific topology or dynamic settings is discussed subsequently with easier-to-verify conditions derived. These heterogeneous factors have been determined to independently or jointly affect the controllability of networked sampled-data systems. Notably, heterogeneous sampling periods have the potential to enhance the overall controllability, but not for systems with some special dynamics. When the node dynamics are heterogeneous,the overall system can be controllable even if it is topologically uncontrollable. In addition, in several typical heterogeneous sampled-data multi-agent systems, pathological sampling of single-node systems will necessarily cause overall uncontrollability.

关键词： network controllability sampled-data system heterogeneous system inner coupling multi-agent system

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IEEE Transactions on Aerospace and Electronic systems 2025年第3期61卷 7597-7609页

作者： Wu, Wenjing Fu, Hanjing Cui, Kaixin Liu, Zhigang Yang, Dong Shi, Dawei Beijing Institute of Technology MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

Data limitation caused by the difficulty of data acquisition, the high cost of data collection, and the changes of working conditions is a serious obstacle to the accurate lithium-ion battery state of health (SOH) estimation for spacecrafts system. To achieve accurate few-shot lithium-ion battery SOH estimation and enhance the adaptability of learning methods across varying working conditions, a domain similarity model agnostic meta-learning method is proposed. First, we design a dynamic–static feature extraction method to adequately exploit information from the limited battery data and link the obtained features by gray correlation coefficients to collect sufficient information. Then, by calculating the domain similarity between the training tasks through the maximum mean discrepancy algorithm, the training tasks are ranked to reduce the retraining time. Finally, an long short term memory (LSTM) model is added to the meta-learning framework to capture the long-term dependency relationships between SOH and time series of the voltage, and the ranked tasks are embedded in the meta-training process to improve adaptability in different working conditions. The effectiveness of the proposed method is validated by NASA and MIT datasets, and comparative experimental results illustrate that the proposed method average error is reduced by 73%, and the running speed is increased by 20% compared with traditional MAML in few-shot scenarios. © 1965-2011 IEEE.

关键词： State of charge

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Study on plasma expansion model of primary discharge on spacecraft solar array

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Plasma Science and Technology 2024年第11期26卷 87-95页

作者： Dejie WEI Jianwen WU Liying ZHU School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191People’s Republic of China Institute of Spacecraft System Engineering CAST Beijing 100094People’s Republic of China

In the space plasma environment, primary discharge may occur on the solar array and evolve into a destructive sustained arc, which threatens the safe operation of the spacecraft. Based on the plasma expansion fluid theory, a new multicomponent plasma expansion model is proposed in this study, which takes into account the effects of ion species, ion number, initial discharge current, and Low Earth Orbit(LEO) plasma environment. The expansion simulation of single-component and multicomponent ions is carried out respectively, and the variations of plasma number density, expansion distance, and speed during the expansion process are *** with the experimental results, the evolution of propagation distance and speed is closed and the error is within a reasonable range, which verifies the validity and rationality of the model. The propagation characteristics of the primary discharge on the solar array surface and the influence of the initial value on the maximum propagation distance and the propagation current peaks are investigated. This study can provide important theoretical support for the propagation and evolution of the primary discharge and the key behavior of the transition to secondary discharge on spacecraft solar array.

关键词： primary discharge plasma expansion propagation characteristics spacecraft solar array

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A trajectory-level robot skill learning method based on fuzzy systems

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Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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Spiking Reinforcement Learning Enhanced by Bioinspired Event Source of Multi-dendrite Spiking Neuron and Dynamic Thresholds

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 618-629页

作者： Xingyue Liang Qiaoyun Wu Yun Zhou Chunyu Tan Hongfu Yin Changyin Sun the School of Artificial Intelligence Anhui University Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education IEEE the Institute of Artificial Intelligence Hefei Comprehensive National Science Center the School of Automation Southeast University the Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education

Deep reinforcement learning(DRL) achieves success through the representational capabilities of deep neural networks(DNNs). Compared to DNNs, spiking neural networks(SNNs),known for their binary spike information processing, exhibit more biological characteristics. However, the challenge of using SNNs to simulate more biologically characteristic neuronal dynamics to optimize decision-making tasks remains, directly related to the information integration and transmission in *** by the advanced computational power of dendrites in biological neurons, we propose a multi-dendrite spiking neuron(MDSN) model based on Multi-compartment spiking neurons(MCN), expanding dendrite types from two to multiple and deriving the analytical solution of somatic membrane *** apply the MDSN to deep distributional reinforcement learning to enhance its performance in executing complex decisionmaking tasks. The proposed model can effectively and adaptively integrate and transmit meaningful information from different sources. Our model uses a bioinspired event-enhanced dendrite structure to emphasize features. Meanwhile, by utilizing dynamic membrane potential thresholds, it adaptively maintains the homeostasis of MDSN. Extensive experiments on Atari games show that the proposed model outperforms some state-of-the-art spiking distributional RL models by a significant margin.

关键词： Deep reinforcement learning multi-compartment spiking neurons spiking neural network

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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