A new neural network-based adaptive ILC for nonlinear discrete-time systems with dead zone scheme |
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Authors: | Ronghu Chi Zhongsheng Hou |
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Institution: | (1) Institute of Autonomous Navigation and Intelligent Control, School of Automation and Electrical Engineering, Qingdao University of Science and Technology, Qingdao, 266042, China;(2) Advanced Control Systems Laboratory, School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China |
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Abstract: | By introducing a dead-zone scheme, a new neural network based adaptive iterative learning control (ILC) (NN-AILC) scheme is
presented for nonlinear discrete-time systems, where the NN weights are time-varying. The most distinct contribution of the
proposed NN-AILC is the relaxation of the identical conditions of initial state and reference trajectory, which are common
requirements in traditional ILC problems. Convergence analysis indicates that the tracking error converges to a bounded ball,
whose size is determined by the dead-zone nonlinearity. Computer simulations verify the theoretical results.
This research is supported by General Program (60774022) and State Key Program (60834001) of National Natural Science Foundation
of China, and Doctoral Foundation of Qingdao University of Science & Technology (0022324). |
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Keywords: | Adaptive control iterative learning control neural network non-identical initial condition non-identical trajectory |
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