Quantized Iterative Learning Control for Nonlinear Switched Discrete- Time Systems with Actuator Saturation

A quantized iterative learning control (ILC) scheme is investigated for nonlinear switched discrete-time systems with actuator saturation in repetitive operations. The logarithmic quantizers are utilized to quantize the tracking error signals, and the sector bound method is applied for analysis and mitigation of quantization influence. By using mathematical induction method, it is strictly proved that the tracking error of each subsystem will converge to zero with the increase of iteration. A numerical example is given to illustrate the validity of the quantized ILC algorithm.