Nonlinear system control using adaptive neural fuzzy networks based on a modified differential evolution

  • Authors:

  • Cheng-Hung Chen;Cheng-Jian Lin;Chin-Teng Lin

  • Affiliations:

  • Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu, Taiwan;Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taiping City, Taiwan;Department of Computer Science and the Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu, Taiwan and Brain Research Center, University System of Taiwan, Hsi ...

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
  • Year:
  • 2009

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Abstract

This study presents an adaptive neural fuzzy network (ANFN) controller based on a modified differential evolution (MODE) for solving control problems. The proposed ANFN controller adopts a functional link neural network as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN controller is a nonlinear combination of input variables. The proposed MODE learning algorithm adopts an evolutionary learning method to optimize the controller parameters. For design optimization, a new criterion is introduced. A hardware-in-the loop control technique is developed and applied to the designed ANFN controller using the MODE learning algorithm. The proposed ANFN controller with the MODE learning algorithm (ANFN-MODE) is used in two practical applications-the planetary-train-type inverted pendulum system and the magnetic levitation system. The experiment is developed in a real-time visual simulation environment. Experimental results of this study have demonstrated the robustness and effectiveness of the proposed ANFN-MODE controller.