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

School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Information 2019, 10(12), 377; https://doi.org/10.3390/info10120377
Received: 26 October 2019 / Revised: 22 November 2019 / Accepted: 27 November 2019 / Published: 29 November 2019
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.
Keywords: adaptive inverse control; fuzzy c-regression model; fuzzy c mean; least mean square; back propagation adaptive inverse control; fuzzy c-regression model; fuzzy c mean; least mean square; back propagation
MDPI and ACS Style

Zhong, S.J. Adaptive Inverse Controller Design Based on the Fuzzy C-Regression Model (FCRM) and Back Propagation (BP) Algorithm. Information 2019, 10, 377.

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