自适应模糊神经控制器的电动舵机控制

Electromechanical Actuator Control with Modified Fuzzy Neural Network PID Controller

针对电动舵机采用PID控制器响应速度慢、超调大的问题,提出了采用人工神经网络和模糊控制来自整定比例因素的控制策略,建立了电动舵机的模型,分析了自适应神经网络控制器原理,并且结合模糊逻辑控制器,构成模糊神经网络来自动调整比例因素,设计了由两个输入变量和一个输出变量的电动舵机控制器。所提的控制算法应用于电动舵机系统,并通过建模仿真和原理样机的弹簧钢悬臂梁加载实验验证,采用该算法电动舵机系统的响应时间达到25 ms,几乎无超调,且舵机系统的位置指令跟踪和力矩干扰抑制能力较强,具备在常规弹药弹道修正中应用的可能性。

According to the electromechanical actuator adopting the normal proportional- integral- derivative （ PID ） controller for has （EMA） long response time and high overshoot problem, the control strategy combining fuzzy control with artificial neural network to self adjusting the scale factors was proposed. The model of the EMA was established, the princi- ple of the adaptive neural network controller was analyzed, and combined with the fuzzy logic controller, a fuzzy neural net- work was constructed to automatically adjust the scale factors. The electromechanical actuator＇s adaptive fuzzy neural net- work controller was designed by two input variables and one output. The control strategy was applied to electromeehanical actuator control system. Through the simulation and the prototype loading experiment with the spring steel cantilever beam, It realizes the EMA＇s response time of 25 ms, almost no overshoot, and also good performance at position command tracking and strong abi/ity of torque disturbance restraint. The controller is possible for applying in trajectory correction of the conventional ammunition.