复合舵机的神经网络自校正控制

针对复合舵机的时变和非线性特性,提出了神经网络自校正控制方法.用改进的非线性自回归滑动平均模型(NARMA)对复合舵机进行动态建模,采用多层感知器神经网络辨识舵机非线性模型,由广义逆思想设计出控制器,并根据被控对象与辨识模型间误差在线调整网络权值,进而修正控制律,实现了复合舵机的自校正控制.仿真结果表明,在模型有时变及非线性因素的情况下,此方法仍能得到较好的控制效果.

Artificial-neural-network adaptive control of compound actuator

To solve time-varying and nonlinear problem of the compound actuator,the neural-network adaptive control principle was proposed.Establish the dynamic model of compound actuator using nonlinear auto-regressive moving average model,multi-layer perception neural network was used to identify the nonlinear model.The controller was based on the theory of generalized inverse and the network weights were deduced with respect to the error between the object and the identified model.The self-turning control was realized in combination with control-algorithm modify online.The simulation results show that this control scheme has good effect on the control of compound actuator under the model having time-varying and nonlinear character.