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Neural Network State Learning Based Adaptive Terminal ILC for Tracking Iteration-varying Target Points 被引量:2
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作者 Yu Liu Rong-Hu Chi Zhong-Sheng Hou 《International Journal of Automation and computing》 EI CSCD 2015年第3期266-272,共7页
Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial con... Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance. 展开更多
关键词 Adaptive terminal iterative learning control neural network initial state learning iteration-varying terminal desired points ini
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Ballistic learning control: formulation, analysis and convergence
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作者 Jianxin XU Deqing HUANG Wei WANG 《控制理论与应用(英文版)》 EI CSCD 2013年第3期325-335,共11页
In this paper, we formulate and explore the characteristics of iterative learning in ballistic control problems. The iterative learning control (ILC) theory provides a suitable framework for derivations and analysis... In this paper, we formulate and explore the characteristics of iterative learning in ballistic control problems. The iterative learning control (ILC) theory provides a suitable framework for derivations and analysis of ballistic control under learning process. To overcome the obstacles caused by uncertain gradient and redundant control input, we incorporate extra trials into iterative learning. With the help of trial results, proper control and updating direction can be determined. Then, iterative learning can be applied to ballistic control problem. Several initial state learning algorithms are studied for initial speed control, force control, as well as combined speed and angle control. In the end, shooting angle learning in the basketball shot process is simulated to verify the effectiveness of iterative learning methods in ballistic control problems. 展开更多
关键词 Ballistic control Iterative learning control Initial state learning CONVERGENCE
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