Adaptive Inverse Controller Design Based on the Fuzzy C-Regression Model (FCRM) and Back Propagation (BP) Algorithm

Establishing an accurate inverse model is a key problem in the design of adaptive inverse controllers. Most real objects have nonlinear characteristics, so mathematical expression of an inverse model cannot be obtained in most situation. A Takagi–Sugeno(T-S)fuzzy model can approximate real objects w...

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Vydáno v:Information (Basel) Ročník 10; číslo 12; s. 377
Hlavní autor: Jian Zhong, Shi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.12.2019
Témata:
Adaptive control
Algorithms
Back propagation
Clustering
Control stability
Control systems design
Control theory
Controllers
Fuzzy sets
Inverse problems
Lagrange multiplier
Mathematical models
Neural networks
Nonlinear systems
Process parameters
Regression models
Systems stability
ISSN:2078-2489, 2078-2489
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Shrnutí:Establishing an accurate inverse model is a key problem in the design of adaptive inverse controllers. Most real objects have nonlinear characteristics, so mathematical expression of an inverse model cannot be obtained in most situation. A Takagi–Sugeno(T-S)fuzzy model can approximate real objects with high precision, and is often applied in the modeling of nonlinear systems. Since the consequent parameters of T-S fuzzy models are linear expressions, this paper firstly uses a fuzzy c-regression model (FCRM) clustering algorithm to establish inverse fuzzy model. As the least mean square (LMS) algorithm is only used to adjust consequent parameters of the T-S fuzzy model in the process of parameter adjustment, the premise parameters are fixed and unchanged in the process of adjustment. In this paper, the back propagation (BP) algorithm is applied to adjust the premise and consequent parameters of the T-S fuzzy model, simultaneously online. The simulation results show that the error between the system output controlled by proposed adaptive inverse controller and the desired output is smaller, also the system stability can be maintained when the system output has disturbances.
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ISSN:2078-2489
2078-2489
DOI:10.3390/info10120377