基于BOWA小脑模型的高精度稳定 电动加载系统

 随着电动加载系统的不断发展,对控制精度、动态特性和稳定性提出了更高的要求,常规的小脑模型(CMAC)和PD控制相结合的复合控制策略难以满足加载指标要求。针对无人机舵机电动加载系统的控制需求,提出了一种基于平衡学习、最优权值和自适应学习率的新型小脑模型(BOWA-CMAC)复合控制策略,它在保留小脑模型算法正常学习过程的同时,避免了算法的过学习现象,保证了系统的稳定,同时提高了跟踪精度和动态特性。仿真和实验结果表明,BOWA-CMAC复合控制策略具有很强的鲁棒性,抑制了加载系统的多余力矩,保证了系统的稳定性,有效提高了系统的跟踪精度和动态特性,非常适合于实时控制。

High Precision and Stable Electric Loading System Based on BOWA-CMAC

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.