Iterative learning control for nonlinear nonaffine networked systems with stochastic noise in communication channels

Abstract

This article investigates the convergence analysis of networked iterative learning control for nonlinear nonaffine systems firstly by considering stochastic noise introduced by the network channels. The convergence analysis is under a data-driven framework, which does not rely on any mechanism model information. To deal with the nonlinearity, both the state transition technique and the differential mean value principle are used to formulate the iterative dynamics of system states, tracking errors and input signals using a lifted matrix expression, respectively. In terms of the contraction mapping principle, the tracking error is shown to be iteratively convergent under the sense of mathematical expectation. Since the [Formula: see text]-norm is not used in the analysis, the convergence property of the tracking error is not affected by the operation interval and a good transient performance can be ensured in theory. Simulation studies test the theoretical results.

Quantized iterative learning control of communication-constrained systems with encoding and decoding mechanism

“…The development of communication technology promotes the combination of network and control systems, and networked control systems (NCSs) have become a focus of extensive research and examination (Bahreini et al, 2022; Liu and Chi, 2021; Yang et al, 2020; Yu and Chen, 2023). Compared to traditional control systems, the network control mode offers several advantages, which is easy to be maintained and connected, and has high efficiency.…”