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摘要: 基于模糊树模型, 结合神经网络中的逆向学习和专门化学习, 提出了自适应直接逆控制方法. 首先离线辨识对象的逆模型作为初始的控制器, 然后与对象串联, 用最小均方差 (Least mean square, LMS) 算法在线调节控制器中的线性参数. 本方法辨识得到的逆模型控制器可以减少需要的模糊规则数目, 同时达到较好的跟踪控制效果. 仿真结果表明了方法的有效性.Abstract: An adaptive direct-inverse control approach is proposed based on the fuzzy tree model and inverse learning and special learning that are used in neural networks broadly. In this approach, the inverse model of the plant is identified as the initial controller firstly. Then the inverse model is connected to the plant in series and the linear parameters of the controller are tuned by the least mean square algorithm in on-line manner. The controller based on the inverse model obtained by the proposed approach can reduce the fuzzy rules and acquire satisfying tracing control effect. Simulation results have shown the effeteness of the proposed approach.
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Key words:
- Fuzzy tree model /
- T-S fuzzy model /
- adaptive inverse control /
- tracing control
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