| Summary: | 碩士 === 國立虎尾科技大學 === 自動化工程系碩士班 === 106 === In the field of precision mechanical control, how to overcome the influence of friction force is a key issue. In order to reduce the influence of friction force, this paper developed a precision adaptive fuzzy neural sliding-mode tracking control with friction compensation. In this study, the DNLRX inverse friction force model was used as feedforward controller, and four kinds of feedback controllers: the integral sliding-mode controller, sliding-mode controller, fuzzy sliding-mode controller and neural network sliding-mode controller were used to estimate and compensate the total uncertainty of friction and disturbances.
In the experiments, the different controllers were compared with each other for no friction compensation and friction compensation to verify the effect of friction compensation. The experimental results show that the designed controller is effective and feasible, and through the comparison of the experimental results, it can be known that the performance of friction compensation using different feedback controllers is better than without friction compensation and it can reduce the tracking error effectively. Meanwhile, the neural network sliding-mode feedback controller plus DNLRX feed-forward control has the best control performance.
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