An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems |
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Authors: | Ye Yang Chen Chen Jiangang Lu |
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Affiliation: | 1.State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.Y.); (C.C.);2.Zhejiang Laboratory, Hangzhou 311121, China |
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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 by 21.69% and 11.21%, respectively, proving it to be applicable for SISO discrete-time nonlinear systems. |
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Keywords: | SISO discrete-time nonlinear systems full-form model-free adaptive controller fuzzy neural networks long short-term memory neural networks three-tank system |
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