Abstract
An iterative learning control scheme is described for linear discrete-time systems. A weighted least-squares criterion of learning error is optimized to obtain a unique control gain for a case when the number of sampling is relatively small. It is then shown that algorithmic convergence can be readily guaranteed, because the present learning rule consists of a steady-state Kalman filter. By paying attention to the sparse system structure for the system's impulse response model, we further derive a suboptimal iterative learning control for a practical case when the number of sampling is large.
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Watanabe, K., Fukuda, T. & Tzafestas, S.G. An interative learning control scheme using the weighted least-squares method. J Intell Robot Syst 4, 267–284 (1991). https://doi.org/10.1007/BF00303227
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DOI: https://doi.org/10.1007/BF00303227