Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller based on Neural Network with System Error Set as Input

Model-Free Adaptive Control (MFAC) is a new data-driven control method, which depends only on the input/output (I/O) measurement data rather than the mathematical model information of the actual controlled system. The SISO MFAC based on the compact-form dynamic linearization (SISO-CFMFAC) is a promising approach to control the SISO nonlinear systems. However, the parameters in the SISO-CFMFAC should be tuned carefully before being put into use. Unfortunately, so far the parameter tuning of SISO-CFMFAC is still a laborious, time-consuming and cost-consuming work. In this paper, a novel parameter self-tuning approach of SISO-CFMFAC based on back propagation Neural Network with System Error set as input (SISO-CFMFAC-NNSE) is proposed, and then verified by using a typical time-varying nonlinear SISO system. Results show that the proposed controller named SISO-CFMFAC-NNSE can achieve better control stability and accuracy than the existing controller of SISO-CFMFAC.