Feedback control based sampled-data ILC for repetitive position tracking control of DC motors

This paper presents the design and application of a feedback control based sampled-data iterative learning control for systems with initial resetting error, input disturbance and output measurement noise. The feedback controller introduced in the feedforward iterative learning system is to enhance the convergent rate of output error. Theoretical analysis of stability and convergence is rigorously studied. It is shown that the tracking error will converge to a residual set if the feedforward learning gain satisfies a sufficient condition and the sampling period is small enough. Since the learning controller is designed in a sampled-data formulation, it is realized by a digital circuit in an FPGA chip and applied to a repetitive position tracking control of DC motors to demonstrate its feasibility. The experiment results show that the learning performance is improved if the sampled-data iterative learning controller is designed based on a suitable feedback controller (PD controller).