Robust Quantized H∞ ILC Design for Uncertain Systems with Communication Constraints

Abstract

This paper considers the problem of H∞ iterative learning control design for network-based uncertain systems with communication constraints, including data quantization and random data dropouts. It is assumed that the control input update signals are quantized before they are transmitted to the iterative learning controller and a logarithmic quantizer is used to decode the data with a number of quantization levels. Then, the data dropout is modeled by the conventional Bernoulli random variable to describe the successful transmission or miss. The 2-D dynamic of such ILC process is established by a stochastic Roesser model. By using sector bound method to deal with the quantization error, a sufficient asymptotically stability condition with an H∞ disturbance attenuation level for the 2-D system is given and then the ILC design can be developed based on the condition. The effectiveness of the proposed method is illustrated by application to an injection molding process.

DOI: http://dx.doi.org/10.5755/j01.itc.47.3.18451