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Value Iteration-Based Cooperative Adaptive Optimal control for Multi-Player Differential Games With Incomplete Information

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 690-697页

作者： Yun Zhang Lulu Zhang Yunze Cai the department of automation Shanghai Jiao Tong UniversityShanghai 200240 the Key Laboratory of System control and Information Processing Ministry of Education of ChinaShanghai 200240China

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence *** players are divided into two groups in the learning process and adapt their policies *** method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based ***,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control *** efficacy of our method is illustrated by three examples.

关键词： Adaptive dynamic programming incomplete information multi-player differential game value iteration

Fixed-Time Anti-Disturbance Average-Tracking for Multi-Agent Systems Without Velocity Measurements

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Journal of Systems Science & Complexity 2024年第3期37卷 985-1002页

作者： LI Yuling LIU Chenglin ZHANG Ya CHEN Yangyang Key Laboratory of Advanced Process control for Light Industry(Ministry of Education) Institute of AutomationJiangnan UniversityWuci 214122China School of automation Southeast UniversityNanjing 210096China

In this paper,the authors tackle a fixed-time average-tracking problem for multi-agent systems which do not require velocity measurements and are affected by external mismatched and matched *** objective requires the considered agents with second-order dynamics to follow the average value of several time-varying reference signals,each of which is acquired by only one *** of all,the average value is estimated by using a novel distributed average estimator in a fixed ***,an original observer is created to provide estimated values in a fixed time for external mismatched and matched disturbances as well as unmeasured velocities of ***,the authors propose a novel fixed-time anti-disturbance average-tracking protocol with the aid of the observer and a sliding mode *** the end,the feasibility of theoretical analysis is illustrated by some simulation experiments.

关键词： Average-tracking control fixed-time algorithm fixed-time observer mismatched distur-bances multi-agent systems velocity measurements

A LESO Based Backstepping controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of automation China University of Geosciences Hubei Key Laboratory of Advanced control and Intelligent automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education department of Electrical and Computer Engineering University of Alberta The Tokyo Institute of Technology

In the process of coal mine drilling, controlling the rotary speed is important as it determines the efficiency and safety of drilling. In this paper, a linear extended state observer(LESO) based backstepping controller for rotary speed is proposed, which can overcome the impact of changes in coal seam hardness on rotary speed. Firstly, the influence of coal seam hardness on the drilling rig's rotary system is considered for the first time, which is reflected in the numerical variation of load torque, and a dynamic model for the design of rotary speed controller is established. Then an LESO is designed to observe the load torque, and feedforward compensation is carried out to overcome the influence of coal seam hardness. Based on the model of the compensated system, a backstepping method is used to design a controller to achieve tracking control of the rotary speed. Finally, the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments, the steady-state error of the rotary speed in field is 1 r/min, and the overshoot is reduced to 5.8%. This greatly improves the stability and security, which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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

Anti-Occlusion Object Tracking Algorithm Based on Filter Prediction

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 400-413页

作者：陈坤赵旭董春玉邸子超陈宗枝 department of automation Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Aerospace control Technology Institute Shanghai 201109China

Visual object tracking is an important issue that has received long-term attention in computer *** ability to effectively handle occlusion,especially severe occlusion,is an important aspect of evaluating the performance of object tracking algorithms in long-term tracking,and is of great significance to improving the robustness of object tracking ***,most object tracking algorithms lack a processing mechanism specifically for *** the case of occlusion,due to the lack of target information,it is necessary to predict the target position based on the motion *** filtering and particle filtering can effectively predict the target motion state based on the historical motion information.A single object tracking method,called probabilistic discriminative model prediction(PrDiMP),is based on the spatial attention mechanism in complex scenes and *** order to improve the performance of PrDiMP,Kalman filtering,particle filtering and linear filtering are ***,for the occlusion situation,Kalman filtering and particle filtering are respectively introduced to predict the object position,thereby replacing the detection result of the original tracking algorithm and stopping recursion of target ***,for detection-jump problem of similar objects in complex scenes,a linear filtering window is *** evaluation results on the three datasets,including GOT-10k,UAV123 and LaSOT,and the visualization results on several videos,show that our algorithms have improved tracking performance under occlusion and the detection-jump is effectively suppressed.

关键词： single object tracking occlusion Kalman filtering particle filtering linear filtering spatial attention mechanism

Autonomous navigation method of satellite constellation based on adaptive forgetting factors

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Chinese Journal of Aeronautics 2024年第7期37卷 317-332页

作者： Dong WANG Jing YANG Kai XIONG School of automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Space Intelligent control Laboratory Beijing Institute of Control EngineeringBeijing 100094China

To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors(UKF-AFF)is *** process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting *** adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more *** intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency *** that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed *** analysis of the proposed method is also *** effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison *** estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.

关键词： Constellation autonomous navigation Unscented Kalman filter Adaptive forgetting factor Model uncertainty Stability analysis

Achieving Given Precision Within Prescribed Time yet With Guaranteed Transient Behavior via Output Based Event-Triggered control

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2059-2067页

作者： Zeqiang Li Yujuan Wang Yongduan Song International Joint Laboratory on Safety and control of Autonomous Unmanned Systems School of AutomationChongqing UniversityChongqing 400044China IEEE

It is interesting yet nontrivial to achieve given control precision within user-assignable time for uncertain nonlinear *** underlying problem becomes even more challenging if the transient behavior also needs to be accommodated and only system output is available for *** key design innovations are proposed to circumvent the aforementioned technical difficulties,including the employment of state estimation filters with event-triggered mechanism,the construction of a novel performance scaling function and an error *** contrast to most existing performance based works where the stability is contingent on initial conditions and the maximum allowable steady-state tracking precision can only be guaranteed at some unknown(theoretically infinite)time,in this work the output of the system is ensured to synchronize with the desired trajectory with arbitrarily pre-assignable convergence rate and arbitrarily pre-specified precision within prescribed time,using output only with lower cost of sensing and *** addition,all the closed-loop signals are ensured to be globally uniformly bounded under the proposed control *** merits of the designed control scheme are confirmed by numerical simulation on a ship model.

关键词： Adaptive control nonlinear systems output feedback prescribed performance

Camera,LiDAR,and IMU Based Multi-Sensor Fusion SLAM:A Survey

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Tsinghua Science and Technology 2024年第2期29卷 415-429页

作者： Jun Zhu Hongyi Li Tao Zhang department of automation Tsinghua UniversityBeijing 100084China

In recent years,Simultaneous Localization And Mapping(SLAM)technology has prevailed in a wide range of applications,such as autonomous driving,intelligent robots,Augmented Reality(AR),and Virtual Reality(VR).Multi-sensor fusion using the most popular three types of sensors(e.g.,visual sensor,LiDAR sensor,and IMU)is becoming ubiquitous in SLAM,in part because of the complementary sensing capabilities and the inevitable shortages(e.g.,low precision and long-term drift)of the stand-alone sensor in challenging *** this article,we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related ***,a brief introduction of the state estimator formation in SLAM is ***,the state-of-the-art algorithms of different multi-sensor fusion algorithms are *** we analyze the deficiencies associated with the reviewed approaches and formulate some future research *** paper can be considered as a brief guide to newcomers and a comprehensive reference for experienced researchers and engineers to explore new interesting orientations.

关键词： multi-sensor fusion Simultaneous Localization And Mapping(SLAM) navigation localization

Deterministic learning-based neural output-feedback control for a class of nonlinear sampled-data systems

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Science China(Information Sciences) 2024年第9期67卷 164-183页

作者： Yu ZENG Fukai ZHANG Tianrui CHEN Cong WANG School of automation Science and Engineering South China University of Technology School of control Science and Engineering Shandong University

This study investigates the deterministic learning(DL)-based output-feedback neural control for a class of nonlinear sampled-data systems with prescribed performance(PP). Specifically, first, a sampleddata observer is employed to estimate the unavailable system states for the Euler discretization model of the transformed system dynamics. Then, based on the observations and backstepping method, a discrete neural network(NN) controller is constructed to ensure system stability and achieve the desired tracking performance. The noncausal problem encountered during the controller deduction process is resolved using a command filter. Moreover, the regression characteristics of the NN input signals are demonstrated with the observed states. This ensures that the radial basis function NN, based on DL theory, meets the partial persistent excitation condition. Subsequently, a class of discrete linear time-varying systems is proven to be exponentially stable, achieving partial convergence of neural weights to their optimal/actual values. Consequently, accurate modeling of unknown closed-loop dynamics is achieved along the system trajectory from the output-feedback control. Finally, a knowledge-based controller is developed using the modeling *** controller not only enhances the control performance but also ensures the PP of the tracking error. The effectiveness of the scheme is illustrated through simulation results.

关键词： deterministic learning neural networks adaptive neural control predefined performance nonlinear sampled-data systems