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arXiv 2022年

作者： Liu, Yuqi Gao, Yinfeng Zhang, Qichao Ding, Dawei Zhao, Dongbin The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing1000839 China

Multi-task intersection navigation including the unprotected turning left, turning right, and going straight in dense traffic is still a challenging task for autonomous driving. For the human driver, the negotiation skill with other interactive vehicles is the key to guarantee safety and efficiency. However, it is hard to balance the safety and efficiency of the autonomous vehicle for multi-task intersection navigation. In this paper, we formulate a multi-task safe reinforcement learning with social attention to improve the safety and efficiency when interacting with other traffic participants. Specifically, the social attention module is used to focus on the states of negotiation vehicles. In addition, a safety layer is added to the multi-task reinforcement learning framework to guarantee safe negotiation. We compare the experiments in the simulator SUMO with abundant traffic flows and CARLA with high-fidelity vehicle models, which both show that the proposed algorithm can improve safety with consistent traffic efficiency for multi-task intersection navigation. Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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PV Maximum Power-point Tracking Using Modified Particle Swarm Optimization under Partial Shading Conditions

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Chinese Journal of electrical engineering 2020年第4期6卷 106-121页

作者： Al-wesabi Ibrahim M.B.Shafik Min Ding Mohammad Abu Sarhan Zhijian Fang Ahmed.G.Alareqi Tariq Al’moqri Ayman.M.Al-Rassas School of Automation China University of GeoscienceWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China School of Oil and Natural Gas Engineering China University of GeosciencesWuhan 430074China School of Mathematics and Physics China University of GeoscienceWuhan 430074China School of Petroleum Engineering China University of PetroleumEast ChinaQingdao 266580China

A novel maximum power-point tracking approach is proposed based on studies investigating the output characteristics of photovoltaic(PV)systems under partial shading *** existence of partially shaded conditions leads to the presence of several peaks on PV curves,which decrease the efficiency of conventional ***,the proposed algorithm,which is based on the modified particle-swarm optimization(MPSO)technique,increases the output power of PV systems under such abnormal conditions and has a better performance compared to other *** proposed method is examined under several scenarios for partial shading condition and non-uniform irradiation levels using Matlab,and to investigate its effectiveness adequately,the results of the proposed method are compared with those of the neural network *** experimental results show that the proposed method can decrease the interference of the local maximum power-point to cause the PV system to operate at a global maximum *** efficiency of the MPSO is achieved with the least number of steady-state oscillations under partial shading conditions compared with the neural network method.

关键词： Photovoltaic(PV) maximum power point tracking(MPPT) step-up converter artificial neural networks(ANNs) particle-swarm optimization(PSO)

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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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第34届中国控制与决策会议

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

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 *** this paper,a SMO based on the piecewise sigmoid function is *** 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 *** low-pass filter of the conventional SMO is improved,and an adaptive filter based on the back-EMF low-pass filter is *** 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 *** Matlab/Simulink simulation verification,the control strategy is feasible and achieves a better control effect.

关键词： Sensorless control SMO Adaptive gain and Adaptive filter

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Research on the Stability of Aircraft With Nose Wheel Autonomous Taxiing Configuration

Research on the Stability of Aircraft With Nose Wheel Autono...

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2022 CSAA/IET International Conference on Aircraft Utility systems, AUS 2022

作者： Huang, Haixiang Zhang, Hao Liu, Xiaochao Shang, Yaoxing Beihang University School of Automation Science and Electrical Engineering Beijing China Ningbo Institute of Technology Beihang University Ningbo China Science and Technology on Aircraft Control Laboratory Beihang University Beijing China Research Institute for Frontier Science Beihang University Beijing China Key Laboratory of Advanced Aircraft Utility Systems Beihang University Ministry of Industry and Information Technology China Beijing Tianma Intelligent Control Technology Co. Ltd. Beijing China General Research Institute of Coal Science Beijing China

ISBN: (数字)9781839537769

ISBN: (纸本)9781839537769

The traditional main engine-driven aircraft ground taxiing method has the problems of low efficiency, heavy pollution and high fuel consumption. However, today's aircraft is facing the urgent need for efficient ground maneuvering, it is urgent to further optimize the ground taxiing method of the aircraft. In response to this demand, a taxiing strategy of autonomously dragging the nose wheels of the aircraft is proposed. However, the ground dynamics research on the nose-wheel drive taxiing configuration aircraft is still insufficient. Therefore, in this paper, the ground dynamics modeling and simplification of the nose wheel autonomous drag configuration aircraft are carried out;the stability boundary of the system under different nose wheel rotation angles and rotation angle change rates during ground taxiing is analyzed and calculated;The traditional configuration aircraft with thrust to achieve the function of ground taxiing is compared and analyzed. It provides a theoretical basis for the efficient and autonomous taxiing stability control of aircraft on the ground. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Wheels

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An adaptive federated control strategy for participant selection in multi-client collaboration 1

An adaptive federated control strategy for participant selec...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Chen, Yizhu Du, Xiaoming Wang, Xiao Zhang, Jun Jason Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (纸本)9781665433372

The federated ecology provides a new paradigm for breaking the isolated data island problem and fully activating the potential of big data and artificial intelligence, especially in multi-client collaboration tasks. Participant selection strategy in multi-client collaboration is the main limitation for increasing the convergence speed and lowing the communication costs. However, in the face of the unbalanced and non-IID data distributions, the performance of federated optimization algorithms will also decrease. To solve the above problems, we propose an adaptive federated control strategy for participant selection in multi-client collaboration based on the Mann-Kendall test, named FedMK. By adaptively selecting weak participants for training rather than random selection, FedMK can speed up model convergence and reduce communication costs. Experiment results show that our method outperforms the baseline method in non-IID scenarios, and the number of communication rounds on CIFAR-10 and synthetic datasets reduced by more than 15% and 10 %, 9 respectively. © 2021 IEEE.

关键词： Stream flow

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Modeling, Analysis and Simulation of the Effect of Temperature on Magnetostrictive Actuators

Modeling, Analysis and Simulation of the Effect of Temperatu...

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International Conference on Fluid Power and Mechatronics (FPM)

作者： Xinyuan Zhang Shaoping Wang Yu Niu Xiao Wu Xingjian Wang Alex I. Gavrilov School of Automation Science and Electrical Engineering Beihang University Beijing China Tianmushan Laboratory Hangzhou China Ningbo Institute of Technology Beihang University Ningbo China Research Institute of Aero-Engine Beihang University Beijing China Department of Automatic Control Systems Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9798350304947

ISBN: (纸本)9798350304954

The increased demand for active control of engines has made the study of high-frequency response actuators increasingly important, and actuators based on magnetostrictive materials are promising for a wide range of applications. In this paper, the magnetic field distribution and various thermal losses including Joule heat loss, eddy current loss and hysteresis loss inside the giant magnetostrictive actuator (GMA) are analyzed, and the mathematical model of the current-driven GMA operation is derived, where the new J-A model considering the temperature effect is used to describe the hysteresis nonlinearity of the material. This paper simulates the actuator model under the action of multiple physical fields in COMSOL, focusing on the variation of the output displacement of the actuator when the thermal field and thermal expansion are integrated, and provides the theoretical basis for the design of the GMA control algorithm in the future.

关键词： Actuators Analytical models Thermal expansion Temperature distribution Mechatronics Magnetostriction Magnetic hysteresis

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Achieving Social Optimum in Non-convex Cooperative Aggregative Games: A Distributed Stochastic Annealing Approach

arXiv

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arXiv 2022年

作者： Wang, Yinghui Geng, Xiaoxue Chen, Guanpu Zhao, Wenxiao School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

This paper designs a distributed stochastic anneal-ing algorithm for non-convex cooperative aggregative games, whose agents' cost functions not only depend on agents' own decision variables but also rely on the sum of agents' decision vari-ables. To seek the the social optimum of cooperative aggregative games, a distributed stochastic annealing algorithm is proposed, where the local cost functions are non-convex and the communi-cation topology between agents is time varying. The weak conver-gence to the social optimum of the algorithm is further analyzed. A numerical example is given to illustrate the effectiveness of the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Target Capturing in an Ellipsoidal Region with a Swarm of Quadrotor UAVs

Target Capturing in an Ellipsoidal Region with a Swarm of Qu...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： M. Alp Merzi Veysel Gazi Giuseppe Fedele Luigi D’ Alfonso Antonio Bono Dept. Electrical and Electronics Eng. Faculty of Engineering Marmara University Istanbul Turkey Dept. Control and Automation Eng. Faculty of Electrical and Electronics Engineering Yildiz Technical University Istanbul Turkey Dept. of Informatics Modeling Electronics and Systems Eng. University of Calabria Rende Italy

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405942

In a recent paper [1], we developed a target capturing strategy for swarms composed of double integrator agents. In this paper, we build on the results in [1] and focus on the implementation of the methodology for a swarm composed of quadrotor UAVs. We utilize both MATLAB and ROS environments under realistic conditions for that purpose. In ROS, we utilize Ultra-Violet LED blinkers based Ultra-Violet Direction And Ranging (UVDAR) system for relative localization. The sensing and localization errors serve as realistic errors for the assumptions in [1] for the uncertainties of the velocity and acceleration of the target. We verify in a realistic scenario that the agents converge to a user-defined ellipsoidal ring around the target despite the uncertainties and the more realistic agent dynamics.

关键词： Location awareness Uncertainty Ultraviolet sources Light emitting diodes Distance measurement Sensors Aircraft

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Robust Optimization for Quantum Reinforcement Learning control using Partial Observations

arXiv

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arXiv 2022年

作者： Jiang, Chen Pan, Yu Wu, Zheng-Guang Gao, Qing Dong, Daoyi State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and quantum approximate optimization algorithm. It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for QAOA with noisy control Hamiltonian. This robust control optimization model can be trained to compensate the uncertainties in practical quantum computing. © 2022, CC BY.

关键词： Robust control

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Differentially Private Bipartite Consensus over Signed Networks with Time-Varying Noises

arXiv

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arXiv 2022年

作者： Wang, Jimin Ke, Jieming Zhang, Ji-Feng The School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China The Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Tthe School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

This paper investigates the differentially private bipartite consensus algorithm over signed networks. The proposed algorithm protects each agent’s sensitive information by adding noise with time-varying variances to the cooperative-competitive interactive information. In order to achieve privacy protection, the variance of the added noise is allowed to be increased, and substantially different from the existing works. In addition, the variance of the added noise can be either decaying or constant. By using time-varying step-sizes based on the stochastic approximation method, we show that the algorithm converges in mean-square and almost-surely even with an increasing privacy noise. We further develop a method to design the step-size and the noise parameter, affording the algorithm to achieve asymptotically unbiased bipartite consensus with the desired accuracy and the predefined differential privacy level. Moreover, we give the mean-square and almost-sure convergence rate of the algorithm, and the privacy level with different forms of the privacy noises. We also reveal the algorithm’s trade-off between the convergence rate and the privacy level. Finally, a numerical example verifies the theoretical results and demonstrates the algorithm’s superiority against existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Multi agent systems

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