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《Pattern Recognition and Artificial Intelligence》 2019-04
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Data Driven Optimal Stabilization Control and Simulation Based on Reinforcement Learning

LU Chaolun;LI Yongqiang;FENG Yuanjing;College of Information Engineering,Zhejiang University of Technology;  
Q-learning algorithm is used to solve the optimal stabilization control problem while only the data, rather than the model of the plant, is available. Due to the continuity of state space and control space, Q-learning can only be implemented in an approximate manner. Therefore, the proposed approximate Q-learning algorithm can obtain only one suboptimal controller. Although the obtained controller is suboptimal, the simulation shows that the closed-loop domain of attraction of the proposed algorithm is broader and the cost function is also smaller than the linear quadratic regulator and deep deterministic policy gradient method for the strongly nonlinear plant.
【Fund】: 國家自然科學基金項目(No.61703369);; 浙江省重點研發計劃項目(No.2017C03039);; 溫州市重大科技專項項目(No.ZS2017007)資助~~
【CateGory Index】: O232
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【Citations】
Chinese Journal Full-text Database 1 Hits
1 XU Jian-Xin1 HOU Zhong-Sheng21. Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore 2. Advanced Control Systems Laboratory, School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, P.R. China;Notes on Data-driven System Approaches[J];自動化學報;2009-06
【Co-citations】
Chinese Journal Full-text Database 10 Hits
1 Yuan-Qing Xia;Yu-Long Gao;Li-Ping Yan;Meng-Yin Fu;School of Automation,Beijing Institute of Technology;;Recent Progress in Networked Control Systems——A Survey[J];International Journal of Automation and Computing;2015-04
2 HOU Zhong-Sheng;DONG Hang-Rui;JIN Shang-Tai;Advanced Control Systems Laboratory,School of Electronic and Information Engineering,Beijing Jiaotong University;;Model-free Adaptive Control with Coordinates Compensation for Automatic Car Parking Systems[J];自動化學報;2015-04
3 LI Xiang-yang;College of Automation Science and Engineering,South China University of Technology;;Iterative learning control based on equivalent control[J];系統工程與電子技術;2014-07
4 WANG Lu 1 LI Ning 1 LI Shao-Yuan 1 1.Department of Automation,Shanghai Jiao Tong University,Key Laboratory of System Control and Information Processing,Ministry of Education of China,Shanghai 200240,China;Performance Monitoring of the Data-driven Subspace Predictive Control Systems Based on Historical Objective Function Benchmark[J];自動化學報;2013-05
5 BO Ying-chun1,LI Lai-hong2,MA Shan-peng3,XIA Bo-kai1(1.College of Information and Control Engineering in China University of Petroleum,Qingdao 266580,China; 2.Hekou Power Supply Corporation,Shengli Oilfield,Dongying 257200,China; 3.Shandong Shtar Science & Technology Group Company Limited,Dongying 257062,China);Application of neural dynamic programming to dissolved oxygen control[J];中國石油大學學報(自然科學版);2013-01
6 RUAN Xiao-e 1,PARK Kwang-hyun 2,BIEN Z Zenn 3(1.School of Mathematics and Statistics,Xi’an Jiaotong University,Xi’an Shaanxi 710049,China;2.School of Robotics,Kwangwoon University,Seoul 447-1,Korea;3.School of Electrical and Computer Engineering,Ulsan National Institute of Science and Technology,Ulsan 689-798,Korea);Retrospective review of some iterative learning control techniques with a comment on prospective long-term learning[J];控制理論與應用;2012-08
7 CHI Rong-hu1,HOU Zhong-sheng2,WANG Dan-wei3,JIN Shang-tai2(1.School of Automation and Electrical Engineering,Qingdao University of Science and Technology,Qingdao Shandong 266042,China;2.Advanced Control Systems Lab,School of Electronics and Information Engineering,Beijing Jiaotong University,Beijing 100044,China;3.EXQUISITUS,Centre for E-City,School of Electrical and Electronic Engineering,Nanyang Technological University,Singapore 639798);An optimal terminal iterative learning control approach for nonlinear discrete-time systems[J];控制理論與應用;2012-08
8 JIN Shang-tai1,HOU Zhong-sheng1,CHI Rong-hu2,LIU Xiang-bin1(1.Advanced Control Systems Laboratory,School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China;2.School of Automation and Electrical Engineering,Qingdao University of Science and Technology,Qingdao Shandong 266042,China);Data-driven model-free adaptive iterative learning control for a class of discrete-time nonlinear systems[J];控制理論與應用;2012-08
9 XIONG Fu-qiang,GUI Wei-hua,YANG Chun-hua(School of Information Science and Engineering,Central South University,Changsha 410083,China.);Integrated prediction model of iron concentration in goethite method to remove iron process[J];控制與決策;2012-03
10 Lin Yue-song Chen Lin Guo Bao-feng(Institute of Information and Control,Hangzhou Dianzi University,Hangzhou 310018,China);A Data-driven Fusion and Its Application to Acoustic Vehicle Classification[J];電子與信息學報;2011-09
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