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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Shijie Cui Peng Zeng Zhongfeng Wang Chunhe Song State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Connecting large-scale distributed generation to a distribution network is complex as it is difficult to set the current protection limit within a traditional distribution network. This paper proposes a distributed protection and control scheme based on reinforcement learning for a distribution network connected to a distributed generation system. This research introduces the idea of transforming this distribution network protection problem into a multi-agent reinforcement learning problem. The concrete construction method of the reinforcement learning environment and deep Q-learning network is described. The algorithm training process is presented and the algorithm training and verification environment is built.

关键词： Training Uncertainty Power supplies Distribution networks Reinforcement learning Power system stability Distributed power generation

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Adaptive Current Protection Technology for Distribution Network with Distributed Power Sources Based on Local Information

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Mobile Information Systems 2021年 2021卷

作者： Cui, Shijie Zeng, Peng Wang, Zhongfeng Zuo, Yue State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China State Grid Huludao Power Supply Company Huludao125000 China

Because the distributed power supply is connected to the distribution network, its structure has also changed the original traditional method. The original single power supply method was originally used. The network provided is a radial network and it is connected to the distributed power supply. Later, it became a structure with small- and medium-sized power supplies inside, and its network power supply became dual or multiple power supplies. Such a structural change affected the coordination of power distribution protection devices, which would impose current on the distribution network. Protection has a negative impact. This paper aims to study the adaptive current protection technology of distribution network with distributed power based on local information. On the basis of analyzing the characteristics of distributed power, the classification of distributed power, and the operation mode of distributed power, according to the access of IIDG, as well as different influences of relay protection of distribution network, an improved protection scheme with adaptive current quick-break protection as the main protection is proposed, so that different fault characteristics of the distribution network have different setting values, and then simulation experiments are carried out on this scheme. The experimental results show that the setting value of three-phase short-circuit fault proposed in this paper can well protect the protection range, which is larger than the traditional protection setting and self-adaptive setting. © 2021 Shijie Cui et al.

关键词： Electric power system protection

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Position Sensorless control of Permanent Magnet Synchronous Motor Based on Adaptive Filtering Sliding Mode Observer

Position Sensorless Control of Permanent Magnet Synchronous ...

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Chinese control and Decision Conference, CCDC

作者： Anna Wang Yingjie Xiong Tao Zhang College Of Information Science and Engineering Northeastern University Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665478977

In the position sensorless control strategy of permanent magnet synchronous motor (PMSM), the back-EMF method based on sliding mode observer (SMO) is favored because of its robustness. In this paper, a SMO based on the piecewise sigmoid function is designed. Based on the back-EMF observation error, the gain adaptation of the SMO is realized, which effectively reduces the high-frequency chattering problem of the conventional SMO. The low-pass filter of the conventional SMO is improved, and an adaptive filter based on the back-EMF low-pass filter is proposed. According to the back-EMF observation error, the cut-off frequency of the low-pass filter is changed in real-time, and the estimation of rotor position is improved. After Matlab/Simulink simulation verification, the control strategy is feasible and achieves a better control effect.

关键词： Adaptation models Low-pass filters Adaptive filters Rotors Sensorless control Observers Permanent magnet motors

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Real-Time Energy Management of Microgrid System Based on Policy-Based Reinforcement Learning

SSRN

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SSRN 2022年

作者： Liu, Ding Zang, Chuanzhi Zeng, Peng Li, Wanting Wang, Xin Liu, Yuqi Xu, Shuqing Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang University of Technology Shenyang110870 China

Electric power grids are changing from traditional power systems to modern smart integrated power systems. The microgrid (MG) plays a vital role in this process through the integration of distributed renewable energy resources (RESs) and energy storage systems (ESSs). However, wind and solar radiation are characterized by intermittence, randomness and fluctuation. These uncertainties cannot be ignored in microgrids with high penetration of RES and random load demand, which challenges the effective and economic management of MGs. To achieve real-time economically optimal scheduling of microgrids, we proposed a real-time energy management strategy. Unlike traditional model-based approaches, this strategy is learning-based and does not need the knowledge of system uncertainties. The energy management problem of a microgrid is formulated as a Markov decision process (MDP) solved using policybased deep reinforcement learning (DRL). In the proposed RL-based approach, two different neural networks are used. One takes the real-time system state of the microgrid as inputs and makes the decision to obtain the optimal control policy © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Background Flow Sensing for Autonomous Underwater Vehicles Using Model Reduction with Dynamic Mode Decomposition

Background Flow Sensing for Autonomous Underwater Vehicles U...

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IEEE/ASME (AIM) International Conference on Advanced intelligent Mechatronics

作者： Fengying Dang Sanjida Nasreen Feitian Zhang Bioinspired Robotics and Intelligent Control Laboratory at the Department of Electrical and Computer Engineering George Mason University Fairfax VA USA

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

Autonomous underwater vehicles (AUVs) have received increasing attention among scientific and engineering societies for their superior performances and promising future in marine environments. A key challenge to marine autonomy is the sensing of the background flow environment, which is important for navigation and motion control of AUVs. While existing flow estimation studies typically focus on (quasi-)steady flow fields for AUVs of well-defined shapes, this paper proposes a novel flow sensing algorithm that applies to any dynamic flows for arbitrary AUV shapes. The proposed flow sensing method assimilates distributed pressure measurements through coalescing recursive Bayesian estimation and dynamic mode decomposition (DMD)-based model reduction. To demonstrate the effectiveness of the proposed distributed flow sensing algorithm, both simulation and experimental results are presented on three testing prototypes of different shapes including a Joukowski foil, a triangle, and a rectangle.

关键词： Sensors Pressure measurement Estimation Heuristic algorithms Reduced order systems Vehicle dynamics Bayes methods

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Research on Distributed intelligent Protection Strategy of distribution network based on Reinforcement Learning

Research on Distributed Intelligent Protection Strategy of d...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Shijie Cui Peng Zeng Ende Hu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China State Grid Shaoxing Power Supply Company Shaoxing China

ISBN: (纸本)9781665428781

In the context of building a new power system with new energy as the main part, the operation mode of distribution network is more complex, and the traditional distribution network protection strategy can hardly meet the demand, so this paper proposes a distributed intelligent protection strategy for distribution network based on reinforcement learning. This paper analyzes the impact of new energy resources access on distribution network relay protection, and presents an intelligent protection algorithm model based on deep reinforcement learning. The structure of intelligent protection device is designed, and the distribution network distributed protection scheme based on intelligent protection device is proposed. Finally, the algorithm training process is given, and the experimental environment is built.

关键词： Training Uncertainty Power supplies Protective relaying Distribution networks Reinforcement learning Power system stability

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Practical fractional-order variable-gain super-twisting control with application to wafer stages of photolithography systems

arXiv

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arXiv 2021年

作者： Kuang, Zhian Sun, Liting Gao, Huijun Tomizuka, Masayoshi Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Mechanical Control System Lab Mechanical Engineering Department University of California BerkeleyCA94720 United States

In this paper, a practical fractional-order variable-gain super-twisting algorithm (PFVSTA) is proposed to improve the tracking performance of wafer stages for semiconductor manufacturing. Based on the sliding mode control (SMC), the proposed PFVSTA enhances the tracking performance from three aspects: 1) alleviating the chattering phenomenon via super-twisting algorithm and a novel fractional-order sliding surface (FSS) design, 2) improving the dynamics of states on the sliding surface with fast response and small overshoots via the designed novel FSS and 3) compensating for disturbances via variable-gain control law. Based on practical conditions, this paper analyzes the stability of the controller and illustrates the theoretical principle to compensate for the uncertainties caused by accelerations. Moreover, numerical simulations prove the effectiveness of the proposed sliding surface and control scheme, and they are in agreement with the theoretical analysis. Finally, practice-based comparative experiments are conducted. The results show that the proposed PFVSTA can achieve much better tracking performance than the conventional methods from various perspectives. Copyright © 2021, The Authors. All rights reserved.

关键词： Sliding mode control

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Event-triggered state estimation for T-S fuzzy affine systems based on piecewise Lyapunov-Krasovskii functionals

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control Theory and Technology 2019年第1期17卷 99-111页

作者： Meng WANG Jianbin QIU Gang FENG Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong China State Key Laboratory of Robotics and Systems & Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin Heilongjiang 150080 China

This paper investigates the problem of event-triggered H∞state estimation for Takagi-Sugeno (T-S) fuzzy affine systems. The objective is to design an event-triggered scheme and an observer such that the resulting estimation error system is asymptotically stable with a prescribed H∞performance and at the same time unnecessary output measurement transmission can be reduced. First, an event-triggered scheme is proposed to determine whether the sampled measurements should be transmitted or not. The output measurements, which trigger the condition, are supposed to suffer a network-induced time-varying and bounded delay before arriving at the observer. Then, by adopting the input delay method, the estimation error system can be reformulated as a piecewise delay system. Based on the piecewise Lyapunov-Krasovskii functional and the Finsler's lemma, the event-triggered H∞observer design method is developed. Moreover, an algorithm is proposed to co-design the observer gains and the event-triggering parameters to guarantee that the estimation error system is asymptotically stable with a given disturbance attenuation level and the signal transmission rate is reduced as much as possible. Simulation studies are given to show the effectiveness of the proposed method.

关键词： Takagi-Sugeno (T-S) fuzzy affine systems event-triggered scheme, piecewise Lyapunov-Krasovskii functional, state estimation

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Adaptive Bipartite Time-Varying Formation control for Multi-Agent Systems on Directed Graph

Adaptive Bipartite Time-Varying Formation Control for Multi-...

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第三十九届中国控制会议

作者： Chenhang Yan Wei Zhang Xiaohang Li Housheng Su Laboratory of Intelligent Control and Robotics Shanghai University of Engineering Science School of Artificial Intelligence and Automation Huazhong University of Science and Technology

Distributed bipartite time-varying formation control for multi-agent systems with a leader of unknown input on directed graphs is investigated. An adaptive non-smooth protocol proposed in this paper only utilizes the local output feedback information among neighboring agents and thus can avoid using eigenvalue information of the Laplacian matrix of the graph. It is shown that if the directed interaction network containing a spanning tree with the leader as the root is structurally balanced,then the bipartite time-varying formation tracking can be achieved with a leader of bounded input via the proposed scheme. A convergence analysis of the proposed protocol for multi-agent systems is reflected by the Lyapunov method. Finally, a validly numerical simulation is illustrated to show the performance of the proposed scheme.

关键词： time-varying formation multi-agent system bipartite graph distributed control cooperative control

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An Easy-implemented Optimization Method of Trajectory Planning Based on Polynomial Interpolation

An Easy-implemented Optimization Method of Trajectory Planni...

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第40届中国控制会议

作者： Guangrong Chen Ming Ma Sheng Guo Bowen Hou Junzheng Wang Robotics Research Center Beijing Jiaotong University Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Firstly,the forward kinematics and inverse kinematics of a five axis manipulator are ***,an easyimplemented optimization method of trajectory planning based on seventh order polynomial interpolation is *** proposed optimization method can capture the time optimal trajectory,while the actuators specifications including speed,acceleration of motor can be guaranteed as ***,simulation results validate the effectiveness and efficiency of proposed optimization *** research provides an insight for the application of trajectory optimization on the controller with low computing capability and real-time requirement.

关键词： Trajectory planning Optimization method Polynomial interpolation Manipulator

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