【摘要】 由于内在的滞回非线性,超磁致伸缩驱动器(GMA)会在开环系统中引起定位误差,在闭环系统中造成系统不稳定。为了克服这个问题,将动态递归神经网络(DRNN)前馈和PD反馈控制器相结合,提出了一种实时滞回补偿控制策略,以期实现GMA的精密位移跟踪控制。DRNN控制器是根据GMA的滞回特性构造的,通过反馈误差学习方案在线学习GMA的逆滞回模型。仿真结果表明该控制策略能适应GMA滞回特性随机械负载、输入信号的变化,在线建立GMA的滞回逆模型,从而消除滞回非线性的影响,实现GMA的精密控制。更多还原

【Abstract】 Due to the inherent hysteretic nonlinearity,the giant magnetostrictive actuator(GMA)can cause position error in the open-loop systems,and cause instability in the closed-loop systems.To remedy this problem,a real-time hysteretic compensation control strategy combining a dynamic recurrent neural network(DRNN)feedforward controller and a proportional derivative(PD)feedback controller was proposed to implement the precision position tracking control of the GMA.The DRNN controller was constructed based on the hysteretic characteristics of the GMA,and on-line learned the inverse hysteresis model of the GMA by the feedback-error learning scheme.Simulation results show that the proposed control strategy can adapt to the changes of hysteretic characteristics of the GMA under different mechanical loads or input signals,on-line obtain inverse hysteresis model of the GMA,thus eliminate the hysteretic impact and achieve the precision control of the GMA.更多还原