基于灰预测模糊PID的随动系统负载模拟器力矩控制研究

为了抑制多余力矩的幅值，提高随动系统负载模拟器加载力矩的控制精度，提出了一种基于灰预测模糊PID的力矩控制器。由灰模型根据力矩传感器测量数值序列的变化趋势，预测加载力矩的未来数值，并以此预测值作为力矩控制器的运算依据。力矩控制器在大误差时采用Bang-Bang控制，小误差时采用模糊PID控制；同时引入伸缩因子，根据误差大小动态调整输入变量的论域，以增强模糊控制器的控制能力。仿真分析和实验结果表明，与传统的PID控制相比，所提出的控制策略能够将多余力矩的幅值进一步削弱接近1/2，可以用于随动系统的动态力矩加载控制。

Research on Torque Control of Servo System Load Simulator Based on Grey Prediction Fuzzy-PID Controller

In order to restrain the extra torque and improve the loading precision of servo system load simulator, a fuzzy-PID torque controller based on grey prediction was proposed. According to the variation trend of the data measured by sensors, the grey prediction model predicted the future values of loading torque circularly, and took those prediction values as the operation basis of the torque controller. When the error was large, the controller performed a switch control; while the error was small, the controller performed a fuzzy-PID control. Meanwhile, a flexible factor depending on error was introduced to adjust the universe of the input variable dynamically and enhance the control ability of the fuzzy controller. The simulation and experiment results show that, compared with the traditional PID control, the proposed control strategy can reduce the extra torque by about one half, and therefore, it can be used to control the dynamical loading of servo systems.