A new fuzzy controller for stabilization control of double inverted pendulum systems is presented based on the fusion function. By introducing comprehensive error and comprehensive error rate, reducing the number of input variables, so as to solve the problem of explosion of fuzzy rules. The total number of fuzzy rules can be reduced significantly and the fuzzy rules can be easily established. Simulation results show that the proposed fuzzy controller can stabilize completely a double inverted pendulum system within 5.0 s for different initial direction of the two pendulums angle and has strong anti-interference ability, robustness and good features.
Abstract.A new type of adaptive neural network fuzzy controller based on the stability for the double inverted pendulum control problem is introduced. The method uses a fusion function to reduce the dimension of the system, reducing the number of input variables to solve the fuzzy rule explosion problem. In order to optimize and amend the front-part and later-part parameter of TakagiSugeno fuzzy model, a mixed algorithm of backward propagation (BP) and least square method (LSE) algorithm are used. Using the collected original input and output data to establish adaptive neural network fuzzy inference system (ANFIS), and to control the double inverted pendulum system. Simulation results show that the controller is better than the other controller.
Abstract.A new method about the sliding mode variable structure based on fuzzy reaching law is proposed and it can be applied to double inverted pendulum system. Not only can this method adopt fuzzy reaching law control the double inverted pendulum effectively, also it can significantly weak chattering and improve the speedingness and robustness of the system. Simulation results show the way is possible and effective. The method of using fuzzy reaching law system is better than traditional variable structures.
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