Different‐factor compact‐form model‐free adaptive control with neural networks for MIMO nonlinear systems

Abstract

AbstractIn this paper, a different‐factor structure‐based compact‐form model‐free adaptive control method with neural networks (DF‐CFMFAC‐NN) is proposed for a class of general multiple‐input and multiple‐output (MIMO) nonlinear systems. Its novelty lies in that it is a pure data‐driven control method using merely input/output data without any model information involved. Moreover, by virtue of the different‐factor structure, it addresses the problem in the prototype CFMFAC that mainly deals with a class of MIMO systems with similar channel characteristics under a fixed topology. Aiming at different characteristics between control channels, widely existing in MIMO systems, DF‐CFMFAC‐NN with learning parameters shows powerful tracking ability and flexible design for each control channel, and learning parameters are auto‐tuned by back propagation neural networks (BPNNs) online based on their self‐learning and self‐adapting properties. In the tuning process, a one‐step‐ahead partial derivative is directly derived by the dynamic linearization technique utilized in DF‐CFMFAC, which greatly improves the prediction accuracy of parameters. The convergence of tracking error and the stability of the tuning process are guaranteed by rigorous theoretical analysis. A coal mill system is provided to demonstrate its effectiveness and practicability, demonstrating it to be a promising control method for MIMO nonlinear systems.