Multi-lagged-input information enhancing quantized iterative learning control

Abstract

Quantization is a significant technique in network control to save limited bandwidth. In this work, two new multi-lagged-input-based quantized iterative learning control (MLI-QILC) methods are proposed by using output quantization and error quantization, respectively. The multi-lagged-input iterative dynamic linearization method (MLI-IDL) is introduced to build a linear data model of nonlinear systems using additional control inputs in lagged time instants and multiple parameters where the condition of nonzero input change is not required any longer. The MLI-QILC is proposed by designing two new objective functions utilizing the quantized data of the system outputs and tracking errors, respectively. With rigorous analysis, it is shown that the proposed MLI-QILC with output quantization guarantees that the tracking error converges to a bound which is related to the quantization density and the bound of the desired trajectory. Furthermore, an asymptotic convergence can be achieved for the proposed MLI-QILC method with error quantization. The theoretical results are verified by simulations.

Parameter-Optimal-Gain-Arguable Iterative Learning Control for Linear Time-Invariant Systems with Quantized Error

“…Later on, Xu and Shen improved such theoretical results by proposing an ILC law using the quantized error information for linear and nonlinear systems; the tracking error of this scheme is zero-error convergence [35]. Two new multi-lagged input-based quantized iterative learning control methods are proposed using the output quantization and the error quantization in the paper [36]. Later, by using the lifting representation, some schemes for the convergence conditions are given for the different data quantization in papers [37][38][39], but when the convergence conditions are guaranteed, the tracking error of ILC with system output quantized signal and control input quantized signal converges to a bound.…”