Iterative Learning Control for Discrete-time Systems with Quantized Measurements

In this paper, the problem of iterative learning control (ILC) for discrete-time systems with quantized output measurements is considered. Here, a logarithmic quantizer is introduced and an ILC scheme is constructed by using output signals with only a finite number of quantization levels. By using sector hound method to deal with the quantization error, a learning condition of ILC that guarantees the convergence of tracking error is derived through rigorous analysis. It is shown that the convergence condition is determined by quantization level, and the tracking error converges to a hound depending on quantization density. Finally, an illustrative example is presented to demonstrate the theoretical results.