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第41届中国控制会议

作者： Yisheng Mei Xin Chen 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Aiming at the problem of how to avoid the joint limit and singular position in continuous trajectory planning of the S-R-S redundant manipulator,a new inverse kinematics optimization algorithm is proposed in this ***,according to the special geometry of S-R-S manipulator,we take the arm angle as the redundant parameter to obtain the joint angle expressions under the given tip pose and arm *** we discuss the feasible arm angles under joint limit in nonsingular and singular cases,and give a complete method to obtain the effective arm ***,we introduce the concept of virtual force to represent the degree that the joint is far from the limit,and define the optimization objective function with the virtual force as the weight to avoid joint limit,the complex relationship between the arm angle and joint angles is linearized by the difference method to simplify the objective function to quadratic function,combine with the feasible arm angle range,we realize singularity avoidance and joint limit avoidance at the same *** simulation result shows that compared with the traditional algorithm,the algorithm proposed in this paper has better joint limit avoidance ability on the premise of avoiding singularity which reflects the superiority of the algorithm and can be used for the motion planning of the manipulator in practical engineering.

关键词： Arm angle Singular position avoidance Joint limit avoidance Inverse kinematic optimization

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Finite-time H∞ state estimation for genetic regulatory networks with observations of two hidden-Markov modes 41

Finite-time H∞ state estimation for genetic regulatory netw...

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第41届中国控制会议

作者： You Wu Xiongbo Wan 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper investigates the problem of finite-time H state estimation for discrete time-delayed genetic regulatory networks（GRNs） with observations of two hidden-Markov modes.A new GRN model with two Markov jump delays is proposed,of which the mode observations are governed by two hidden-Markov *** sufficient conditions are derived,which guarantee the finite-time boundedness of the estimation error dynamics with prescribed H disturbance attenuation *** state estimator parameters are designed by solving several matrix inequalities.A numerical example is given to illustrate the validity of our results.

关键词： Genetic regulatory networks finite-time H_∞ state estimation hidden Markov model Markov jump delays

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Non-convex Optimization Based On Deep Koopman Operator 38

Non-convex Optimization Based On Deep Koopman Operator

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38th Youth Academic Annual Conference of Chinese Association of automation, YAC 2023

作者： Guo, Jun Jia, Yubin Yong, Panxiao Zhou, Jun Liu, Zhimin Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350303636

Non-convex optimization problems are prevalent in various fields, and finding the global optimal solution for such problems remains challenging. This paper proposes an optimization method based on deep Koopman operator, which leverages the power of deep neural networks to provide a data-driven architecture for the Koopman function. The Koopman operator is used to linearly represent non-convex problems, and the proposed method utilizes model predictive control to optimize the resulting linear systems. Simulation results show that this method can solve the non-convex optimization problem effectively, quickly and accurately, while obtaining the global optimal solution. © 2023 IEEE.

关键词： Koopman operator model predictive control Non-convex

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Multi-Agent Cooperative Localization via Extraction and Matching of Image Feature 41

Multi-Agent Cooperative Localization via Extraction and Matc...

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第41届中国控制会议

作者： Xiong Lei Yulin Xu Chao Zhai 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In multi-agent system(MAS),it is of great significance to study the problem of cooperative *** this work,we propose a vision-based cooperative localization scheme for multi-agent systems equipped with RGB-D ***,we formulate the cooperative localization problem of ***,the relative pose between agents with overlapping field of view in MAS can be solved by using an improved feature extraction and matching algorithm,which requires the index function to screen the unimportant feature points in the image to reduce the computation *** system does not require all agents to have a common area of view,and we can calculate the relative pose between agents whose field of view does not overlap at all by accumulating the ***,experiments are carried out to validate the effectiveness and feasibility of this method.

关键词： Multi-agent system Cooperative localization Feature extraction and matching Index function

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A Spiral Coverage Path Planning Algorithm for Nonomnidirectional Robots

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Journal of Field Robotics 2025年

作者： Hou, Taogang Li, Jiaxin Pei, Xuan Wang, Hao Liu, Tianhui Institute of Advanced Control Systems School of Automation and Intelligence Beijing Jiaotong University Beijing Beijing China Institute of Robotics School of Mechanical Engineering and Automation Beihang University Beijing Beijing China

The limited steering capabilities of nonomnidirectional robots introduce significant complexity into complete coverage tasks, often leading to increased path overlap or incomplete coverage of certain areas. Although recent research has made progress in optimizing coverage path planning, redundant coverage or omissions are still prone to occur in the target area to be covered. To address these persistent challenges, we propose a novel spiral coverage method. This approach not only conforms to the kinematic constraints of nonomnidirectional robots but also enhances coverage efficiency by dividing the target area into center and boundary regions and devising tailored coverage strategies for each. This method effectively reduces path redundancy and improves overall area coverage. Furthermore, we introduce a comprehensive metric that combines total path length and area coverage ratio to evaluate coverage efficiency, overcoming the limitations and computational complexity associated with existing metrics. For scenarios where maximizing the area coverage ratio is critical, we have developed a high-coverage-rate turning strategy that ensures 100% coverage. Through simulation tests in six representative areas and actual experiments on airport runways, our method shows an improvement of 0.238%–14.538% in coverage efficiency compared with parallel coverage method and 60.548%–76.339% compared with deep reinforcement learning-based method. Additionally, implementing high-coverage-rate turning strategies improves the area coverage ratio by 2.021%–6.732%. In field experiments, our method reduces execution time by 1.61% compared with parallel coverage method. These results show that our method has a significant effect in improving coverage efficiency and achieving complete coverage goals. © 2025 Wiley Periodicals LLC.

关键词： Robot programming

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Multi-agent Weapon Target Assignment and Cooperative Path Planning Based on QS-RRT Algorithm 41

Multi-agent Weapon Target Assignment and Cooperative Path Pl...

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第41届中国控制会议

作者： Qingze Li Yongzhao Hua Haoran Zhai Qingdong Li Xiwang Dong School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang University Institute of Artificial Intelligence Beihang University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,based on the typical combat scenario where multiple UAVs strike multiple targets,the model of weapon firepower allocation is firstly modeled to obtain the corresponding objective function,and then the improved distributed auction algorithm is used to solve the objective function to obtain the multi-target allocation *** layer first analyze the traditional RRT algorithm,and gets the quick and smooth convergence RRT(QS-RRT) on the basis of improved algorithm,the algorithm on the basis of adding the synergy of constraints,and makes the collaborative planning path compared with non-cooperative path planning,the planning path length in the keep basically remain unchanged,on the basis of our UAV for the same target can arriving together,The difference of arrival time is greatly reduced,and the subsequent coordinated strike mission can be completed more efficiently in the combat scenario.

关键词： Weapon target assignment Auction algorithm Cooperative path planning QS-RRT algorithm

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Resilient control of Networked control Systems With Hidden DoS Attacks and Unknown Observation Probability

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IEEE Transactions on control of Network Systems 2025年第2期12卷 1313-1323页

作者： Wang, Yuan Yan, Huaicheng Park, Ju H. Zhang, Hao Shen, Hao East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education Shanghai200237 China Weifang University College of Machinery and Automation Weifang261061 China Yeungnam University Department of Electrical Engineering Gyeongsan38541 Korea Republic of Tongji University Department of Control Science and Engineering Shanghai200092 China Anhui University of Technology School of Electrical and Information Engineering Ma'anshan243002 China

This paper systematically studies the problem of resilient controller design for networked control systems under imperfect communication environments. The denial of service (DoS) attacks in the case of an imperfect network are remodeled. The semi-Markov chain is introduced in this paper to provide a suitable description of random attacks. On this basis, the double-layer stochastic process is constructed, which consists of an attack sequence and an observation one. Second, since the attack modes are inaccessible, a controller is constructed based on the observation sequence by virtue of the emission probability to stabilize the system under random attacks. Due to the complexity of the network, the probability that an observer can accurately detect an attack when it occurs is considered unknown. Based on the introduced double-layer stochastic process, a mode-associated Lyapunov function is constructed to facilitate the stability analysis. Building on this foundation, sufficient conditions that can ensure the δ-error mean square stability of the closed-loop system are derived. In the end, a simulation of the unmanned ground vehicle is presented, showcasing the feasibility and validity of the proposed control scheme. © 2025 IEEE.

关键词： Stochastic systems

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Optimizing Parameter Design with Frequency and Clock Drift Constraints in Microgrids∗ 63

Optimizing Parameter Design with Frequency and Clock Drift C...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Yu, Chang Lu, Xiaoqing Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China Huazhong University of Science and Technology Ministry of Education Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China Wuhan University School of Electrical Engineering and Automation Wuhan430000 China

ISBN: (纸本)9798350316339

Microgrids subjected to secondary cooperative control encounter significant challenges, including operational constraints and clock drifts, adversely affecting their stability and efficiency. This paper provides conditions that assure optimal microgrid performance in both transient and steady-state scenarios, focusing on the effects of clock drifts and fluctuations in load. Furthermore, we introduce a novel approach for designing secondary control parameters, specifically engineered to minimize steady-state discrepancies attributable to clock drifts while ensuring adherence to standards for transient operations. Comprehensive experimental validations corroborate the effectiveness of our proposed solutions. © 2024 IEEE.

关键词： Microgrids

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Distributed Communication-Sliding Mirror-Descent Algorithm for Nonsmooth Resource Allocation Problem

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Journal of Systems Science & Complexity 2022年第4期35卷 1244-1261页

作者： WANG Yinghui TU Zhipeng QIN Huashu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Key Lab of Systems and Control Academy of Mathematics and Systems ScienceBeijing100190China University of Chinese Academy of Sciences Beijing100190China

This paper considers a distributed nonsmooth resource allocation problem of minimizing a global convex function formed by a sum of local nonsmooth convex functions with coupled constraints.A distributed communication-efficient mirror-descent algorithm,which can reduce communication rounds between agents over the network,is designed for the distributed resource allocation *** employing communication-sliding methods,agents can find aε-solution in O(1/ε)communication rounds while maintaining O(1/ε^(2))subgradient evaluations for nonsmooth convex functions.A numerical example is also given to illustrate the effectiveness of the proposed algorithm.

关键词： Distributed resource allocation problem mirror-descent algorithm nonsmooth functions

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Data-Driven Frequency Regulation Method for Multi-Terminal HVDC Grids 38

Data-Driven Frequency Regulation Method for Multi-Terminal H...

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38th Youth Academic Annual Conference of Chinese Association of automation, YAC 2023

作者： Yong, Panxiao Jia, Yubin Guo, Jun Zhou, Jun Liu, Zhimin Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350303636

High voltage direct current (HVDC) lines allow low-loss transmission over long distances. The multi-terminal HVDC network consists of multiple terminals connected by HVDC lines, and each area shares the frequency reserve. Through secondary frequency control, the frequency of AC and DC areas can be regulated back to the set point when the system frequency has deviated. In this paper, a data-driven model predictive control (MPC) strategy is proposed. The Kooman operator is used to obtain the high-dimensional linear model of a nonlinear system, and the Deep Learning method is used to approximate the infinite-dimensional Koopman operator. The nominal MPC is used for optimization and control, and the optimal problem is solved for the Koopman linearized system. The optimal control is applied to the original nonlinear system. Simulation results show the effectiveness of the proposed method. © 2023 IEEE.

关键词： data-driven Koopman operator model predictive control Multi-terminal HVDC

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