Encoding-Decoding Mechanism-Based Finite-Level Quantized Iterative Learning Control With Random Data Dropouts

Abstract

Learning control is investigated to solve the tracking problem for linear systems via unreliable networks with random data dropouts. By using an encoding-decoding mechanism-based finite-level uniform quantizer, the communication burden is remarkably reduced while retaining a precise tracking performance. An intermittent update principle is adopted in both the encoding-decoding mechanism and the learning control algorithm to handle the effects of data dropouts. A special case, in which no consecutive dropouts exist along the iteration axis, is provided first and then extended to a general case, in which bounded consecutive data dropouts are allowed. A precise analysis of the maximum range of the finite-level quantizer and the associated asymptotical convergence is conducted. Illustrative simulations demonstrate the validity of the proposed framework.