Observer based switching ILC for consensus of nonlinear nonaffine multi-agent systems |
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| Authors: | Ronghu Chi Yangchun Wei Rongrong Wang Zhongsheng Hou |
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| Institution: | 1. School of Automation & Electronics Engineering, Qingdao University of Science & Technology, Qingdao 266061, PR China;2. School of Automation, Nanjing University of Science & Technology, Nanjing 210000, PR China;3. School of Automation, Qingdao University, Qingdao 266042, PR China;1. School of Electrical Engineering & Automation, Henan Polytechnic University, Jiaozuo, China;2. School of Automation, Qingdao University, Qingdao, China;1. School of Automation, Guangdong University of Technology, Guangzhou, Guangdong 510006, China;2. Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou, Guangdong 510006, China;3. School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China;1. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, 116024, China;2. School of Control Science and Engineering, Dalian University of Technology, Dalian, 116024, China |
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| Abstract: | This study considers the main challenges of presenting an iterative observer under a data-driven framework for nonlinear nonaffine multi-agent systems (MASs) that can estimate nonrepetitive uncertainties of initial states and disturbances by using the information from previous iterations. Consequently, an observer-based iterative learning control is proposed for the accurate consensus tracking. First, the dynamic effect of nonrepetitive initial states is transformed as a total disturbance of the linear data model which is developed to describe I/O iteration-dynamic relationship of nonlinear nonaffine MASs. Second, the measurement noises are considered as the main uncertainty of system output. Then, we present an iterative disturbance observer to estimate the total uncertainty caused by the nonrepetitive initial shifts and measurement noises together. Next, we further propose an observer-based switching iterative learning control (OBSILC) using the iterative disturbance observer to compensate the total uncertainty and an iterative parameter estimator to estimate unknown gradient parameters. The proposed OBSILC consists of two learning control algorithms and the only difference between the two is that an iteration-decrement factor is introduced in one of them to further reduce the effect of the total uncertainty. These two algorithms are switched to each other according to a preset error threshold. Theoretical results are demonstrated by the simulation study. The proposed OBSILC can reduce the influence of nonrepetitive initial values and measurement noises in the iterative learning control for MASs by only using I/O data. |
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