A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems

Applied Sciences

YungC. Shin¹  Bin Zhang¹ 

[1] School of Mechanical Engineering, Purdue University, West Lafayette, IN 47906, USA;

关键词: Takagi-Sugeno model;    data-driven system identification;    Neuro-fuzzy model;    optimal control;    flexible robot;   

DOI  :  10.3390/app11010062

来源: DOAJ

【 摘 要 】

A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy models can closely approximate the original unknown nonlinear system with a reduced number of linearizations. Based on T-S fuzzy models, optimal controllers are designed and implemented for a nonlinear two-link flexible joint robot, which demonstrates the possibility of implementing the well-established model-based optimal control method onto unknown nonlinear dynamic systems.

【 授权许可】

Unknown