Abstract
A novel CMAC hybrid control strategy with fast convergence and high precision is proposed for the electric loading system of UAVs in order to solve disturbance of the surplus torque. This CMAC control strategy employs a nonuniform memory quantization scheme that defines its computing structure. A weighted Gaussian neighborhood-based activation process is subsequently implemented to facilitate the learning and computation of this novel CMAC structure. The hybrid controller is compound of CMAC and PD algorithm. The feedforward control is realized by CMAC while the feedback control is performed by PD controller. The mathematical model of an electric loading system for UAVs is established and the detailed control structure is put forward. Simulation results show that the proposed controller has a faster convergence rate and higher precision compared with the conventional CMAC, and can effectively eliminate the surplus torque and fairly improve the dynamic loading performance of the system.
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