[[abstract]]This thesis proposed a fuzzy differentiable cerebellar model articilation controller (FDCMAC). Its main method is to combine fuzzy logical controller (FLC) and differentiable cerebellar model articulation controller (DCMAC). FLC usually uses a fuzzy knowledge base to characterize its control logic for a given system to control. As compared with conventional controllers such as PID controller, FLC can provid better robustness and adaptation in practical control. Its fuzzy knowledge base is created by trial and error. It has steady state error, so it may not guarantee precise control. DCMAC is a table look-up neuron-computing technique. It performs well in terms of its fast learning speed and local generalization capability for approximating nonlinear function. Compared with the FLC, this new controller shortens the design process of fuzzy knowledge base by less trial and error, and improves performance of the control system. According to simulated results, this controller can significantly reduce the tracking error and effectively elevate the accuracy in control process. At last, the experiment results for linear piezoelectric ceramic motor (LPCM) drive system with proposed controller has performed to demonstrate a high performance and robust control system.
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