神经网络+迭代学习解析.pdf

International Journal of Automation and Computing 12(3), June 2015, 266-272 DOI: 10.1007/s11633-015-0891-0 Neural Network State Learning Based Adaptive Terminal ILC for Tracking Iteration-varying Target Points Yu Liu1 Rong-Hu Chi1 Zhong-Sheng Hou2 1School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266042, China 2 Advanced Control Systems Lab, School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China Abstract: 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 fas