Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory

Xiong, Z; Hong, Y; Chen, C; Zhang, J; Zhong, M

doi:10.1109/ACC.2016.7525090

Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory

Lookup NU author(s): Dr Zhihua Xiong, Dr Jie Zhang ORCiD

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Abstract

© 2016 American Automatic Control Council (AACC). The convergence analysis of two-dimensional based integrated predictive iterative learning control (2D-IPILC) is presented for batch process in presence of output noises. In the 2D-IPILC method, iterative learning control (ILC) in the batch domain is integrated reasonably with real-time model predictive control (MPC) in the time domain. Based on the 2D system theory, system response of output tracking error is described explicitly by using the state-transition matrix of the 2D system for the first time. Then influence of output noise to the tracking error can be revealed clearly. By using this description of model response, convergence properties of the 2D-IPILC can be also analyzed theoretically. The sufficient convergence conditions of output tracking error in the proposed algorithm are derived for a class of linear systems. Simulation results have demonstrated the effectiveness of the proposed method.