Encoding–Decoding-Based Quantized Learning Control Using Spherical Polar Coordinates

Abstract

This study investigates the performance of discrete-time systems under quantized iterative learning control. An encoding–decoding mechanism is combined with a spherical polar coordinate-based quantizer to process the signals transmitted through a control network, which introduces a quantization operation to the encoding process. A scenario involving encoding and decoding of the system output is explored before discussing the general scenario involving encoding and decoding of both the system output and control input. Unlike existing schemes, the two scenarios require no additional scaling parameter in the encoder and decoder. The radius of the support sphere is designed to vary over the iterations, and the learning control scheme is based on the output of the decoder. The results indicate that the control method enables error-free tracking performance of a system. The theoretical conclusions are verified in tests of a permanent magnet synchronous motor.