舵面电动加载系统的自适应CMAC复合控制

Adaptive CMAC hybrid control for rudder electric loading systems

针对无人机舵面电动加载系统具有非线性及多余力矩的特点,提出了一种自适应CMAC(Cerebellar Model Articulation Controller)神经网络与自适应神经元控制器并联构成复合控制结构.该控制策略以系统的指令输入和实际输出作为CMAC的激励信号,以系统的当前控制误差作为CMAC的训练信号.提出了利用误差在线自适应调整学习率的方法,消除了常规前馈型CMAC的过学习和不稳定现象.建立了无人机舵面电动加载系统的数学模型,给出了具体的控制结构和算法.仿真结果表明:该方法有效抑制了加载系统的多余力矩,增强了系统的稳定性,明显改善了舵面电动加载系统的动态性能.

Aiming at the nonlinearity and the surplus torque in rudder electric loading systems of unmanned aerial vehicle（UAV）,a self-adaptive cerebellar model articulation controller（CMAC） was proposed,which was parallel to an adaptive neuron controller.This hybrid control strategy adopted the desired value and the actual output as the incentive signals of CMAC,and put the current system error as the training signal of CMAC.The method was proposed by using the error to adjust the learning rate on line,which eliminated the excess self-learning phenomena.The mathematical model of rudder electric loading systems for UAV was established and the detailed control structure was put forward.Simulation results show that the proposed hybrid controller can effectively eliminate the surplus torque,enhance the control stability of the systems and fairly improve the dynamic loading performances of the systems,which is highly suitable for real-time control of nonlinear systems.