An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems

Abstract

This study focuses on the full-form model-free adaptive controller (FFMFAC) for SISO discrete-time nonlinear systems, and proposes enhanced FFMFAC. The proposed technique design incorporates long short-term memory neural networks (LSTMs) and fuzzy neural networks (FNNs). To be more precise, LSTMs are utilized to adjust vital parameters of the FFMFAC online. Additionally, due to the high nonlinear approximation capabilities of FNNs, pseudo gradient (PG) values of the controller are estimated online. EFFMFAC is characterized by utilizing the measured I/O data for the online training of all introduced neural networks and does not involve offline training and specific models of the controlled system. Finally, the rationality and superiority are verified by two simulations and a supporting ablation analysis. Five individual performance indices are given, and the experimental findings show that EFFMFAC outperforms all other methods. Especially compared with the FFMFAC, EFFMFAC reduces the RMSE by 21.69% and 11.21%, respectively, proving it to be applicable for SISO discrete-time nonlinear systems.

Controlling Water Level by Using Modified Model Free Adaptive Controller

“…On the other hand, Data-Driven Control (DDC) [8] guarantees the stability of the controlled systems, robustness, and convergence under specific assumptions. This method depends on offline and online input and output data of the controlled systems, instead of depending on accurate mathematical models [9]. The Model-Free Adaptive Controller (MFAC) is a DDC method [10], which is proposed to control unknown nonlinear systems.…”

“…This controller is successfully used for many industrial control systems [11]. One of these systems is the water level system as suggested by Yang et al [9] and Kadri et al [12].…”