Adaptive Quantized Iterative Learning Control Using Encoding-Decoding Strategy

Abstract

This study investigates the utilization of a dynamic encoding-decoding mechanism for transferred signals to explore adaptive quantized iterative learning control. Encoding-decoding pairs for error and output are designed to adjust the quantization parameters dynamically. A uniform quantizer with a finite quantization level is employed on the system measurement side, with distinct lower bounds specified for the quantizer under two encoding-decoding pairs. Zoom-out and zoom-in strategies are incorporated into the encoder and decoder, respectively, enabling adaptation of the quantizer. These two adaptive quantization mechanisms ensure convergence of the system output toward the desired reference without saturating the quantizer under any initial input. The proposed scheme relaxes the constraints on the initial input signals, simplifies the expression for the quantizer saturation bound, and concurrently reduces the magnitude of the saturation bound itself. Finally, a numerical and an experimental examples are presented to validate the proposed learning control scheme.