Abstract: The rapid development of electric loading systems brings out higher demands for control precision, dynamic char-acteristics and stability, which makes it diffficult for compound control strategy with conventional cerebellar model articulation controller (CMAC) and PD to meet the loading requirements. Therefore, on the basis of an electric loading system of unmanned aerial vehicle, a novel hybrid control strategy CMAC is proposed with improvements of the balance learning method, optimal weight and adaptive leaning rate (BOWA-CMAC). It not only retains the normal learning process of the hybrid controller, avoids the excessive self-learning phenomenon and assures the stability, but also improves the tracking precision and dynamic characteristics. The simulation and experimental results demonstrate that the proposed controller has good robustness and can effectively eliminate the surplus torque, assure the stability of the system with high tracking precision and dynamic characteristics in real time control.

Key words: electric loading, BOWA-CMAC, balance learning, optimal weight, learning rate, stability