Encoding–Decoding-Based Quantized Learning Control Using Spherical Polar Coordinates
研究了离散时间系统在量化迭代学习控制下的性能，提出一种结合球极坐标量化器的编解码机制，实现无误差跟踪，并在永磁同步电机测试中验证。
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.