A Kalman Filter-Based Approach for Iterative Learning Control with Probabilistic Quantization

This investigation tackles the complexities of quantized learning control within linear networked architectures perturbed by additive transmission disturbances. The primary aim is to deliver exceptional tracking exactness while alleviating network resource strain. We introduce a sophisticated control tactic rooted in Kalman filter principles, seamlessly embedded within a stochastic quantization paradigm. This quantizer operates through a Bernoulli distribution influenced by quantization variances. Analytical results affirm that this technique, utilizing Kalman-optimized parameters, drives the command deviation sequence to diminish rapidly to zero in a mean-square framework. Empirical comparisons with established methodologies reveal pronounced convergence velocity, precision, and robustness.