Adaptive Quantized Iterative Learning Control Using Encoding-Decoding Strategy
研究了利用动态编解码机制传输信号的自适应量化迭代学习控制方法，通过调整量化参数确保系统输出收敛且不饱和，并通过数值和实验验证了有效性。
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.