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Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition |
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| Authors: | Ronghu Chi Zhongsheng Hou Jianxin Xu |
| Affiliation: | a Advanced Control Systems Lab, School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, PR China b School of Automation and Electronics Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China c Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119260, Singapore |
| Abstract: | In this work we present a discrete-time adaptive iterative learning control (AILC) scheme to deal with systems with time-varying parametric uncertainties. Using the analogy between the discrete-time axis and the iterative learning axis, the new adaptive ILC can incorporate a Recursive Least Squares (RLS) algorithm, hence the learning gain can be tuned iteratively along the learning axis and pointwisely along the time axis. When the initial states are random and the reference trajectory is iteration-varying, the new AILC can achieve the pointwise convergence over a finite time interval asymptotically along the iterative learning axis. |
| Keywords: | Iterative learning control Adaptive tuning Time-varying parameters Random initial condition Iteration-varying trajectories |
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