Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential evolution algorithm

This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally id...

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Bibliographic Details
Published in:	Applied intelligence (Dordrecht, Netherlands) Netherlands), 2021, Vol.51 (1), p.527-548
Main Authors:	Van Kien, Cao, Anh, Ho Pham Huy, Son, Nguyen Ngoc
Format:	Article
Language:	eng
Subjects:	Adaptive algorithms Adaptive control Artificial Intelligence Computer Science Control algorithms Control stability Controllers Error analysis Evolutionary algorithms Evolutionary computation Feedback control Fuzzy control Least mean squares Least mean squares algorithm Machines Manufacturing Mechanical Engineering Multilayers Nonlinear control Nonlinear systems Optimization Parameter identification Parameter uncertainty Performance indices Processes Robust control SISO (control systems) Sliding mode Soft computing
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Summary:	This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally identified by the differential evolution (DE) algorithm to create offline the inverse nonlinear plant with uncertain coefficients. Then, the adaptive fuzzy-based sliding mode surface is applied to ensure that the closed-loop system is asymptotically stable in which the stability is satisfied using Lyapunov stability concept. The control quality of the proposed AIMFC algorithm is compared with the three recent advanced control algorithms applied in the Spring-Mass-Damper (SMD) benchmark system. Simulation and experiment results with different control parameters show that the proposed algorithm is better than the inverse fuzzy controller and the conventional adaptive fuzzy controller comparatively applied in both SMD system and the coupled-liquid tank system with the performance index using the least mean squares (LMS) error, which is investigated to demonstrate the efficiency and the robustness of the proposed AIMFC control approach.
ISSN:	0924-669X 1573-7497