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

摘要

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. Note to Practitioners-Networked control structure has been widely used in practical applications because of its distinct flexibility, facilitation, and robustness of the entire control framework. All of the involved units in networked systems are usually located in different sites and communicate with each other through wired/wireless networks. An overload of these networks can result in packet congestion and data dropouts among other severe problems. As a result, the reduction of communication burden is of great significance for control through networks, for which quantization is an effective method. In consideration of device cost, it is preferred to apply finite-level uniform quantizer that is cheap and abundant. However, generally, this quantizer cannot guarantee an acceptable tracking performance because of its limited quantization ability. An encoding-decoding mechanism is thus proposed in this article to realize high tracking accuracy while using a simple uniform quantizer, where a learning control methodology is provided to improve tracking performance gradually. In addition, random data dropouts are accommodated in the proposed framework.