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Inverse-adaptive multilayer T-S fuzzy controller for uncertain nonlinear system optimized by differential evolution algorithm
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DOI：10.1007/s00500-020-04782-2.png)
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Abstract
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This paper initiatively proposes a novel inverse-adaptive multilayer T-S fuzzy controller (IFC + AF) optimized with differential evolution (DE) soft computing algorithm available for a class of robust control methods applied in uncertain nonlinear SISO and MISO systems. First, a novel multilayer T-S fuzzy model is created by combined multiple simple T-S fuzzy models with a sum function in the output. Then, the parameters of multilayer T-S fuzzy model are optimally identified using DE algorithm to create offline the inverse nonlinear system regarding uncertain system parameters. Second, an adaptive fuzzy-based sliding-mode surface is innovatively designed to guarantee that the closed-loop system is asymptotically stable using Lyapunov stability principle. Moreover, necessary benchmark tests are investigated in MATLAB/Simulink platform, including the spring-mass-damper SMD system and the fluid level of a double tank with uncertain parameters, in order to illustrate the effectiveness and the feasibility of the proposed IFC + AF control scheme. The IFC + AF control algorithm is adequately investigated with various control coefficients and is strictly compared with the advanced adaptive fuzzy control and the inverse fuzzy control (IFC) approaches. Simulation and experiment results are satisfactorily investigated and demonstrate the feasibility and performance of the proposed IFC + AF control method.
Keywords:
Inverse-adaptive multilayer T-S fuzzy controller (IFC plus AF)
Uncertain nonlinear system
Hybrid adaptive-optimal control
Differential evolution (DE) algorithm
Lyapunov stability principle
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