机械手臂的多模型预测控制

针对机械手臂的非线性特点,提出了基于隶属度函数的多模型预测控制方法。该方法首先根据机械手臂的特点,选择合适的调度变量,将机械手臂的工作空间划分为若干个工作子空间,在每个子空间内的平衡点处对机械手臂进行线性化处理,得到相应的线性子模型,从而得到机械手臂的多模型表示;其次针对每个线性子模型设计局部预测控制器,使其在相应的子空间内达到控制要求;最后选择梯形隶属度函数与局部预测控制器进行加权求和,获得全局多模型预测控制器,以对机械手臂进行控制。仿真结果表明,当机械手臂的工作条件在大范围内变化时,全局多模型预测控制器的控制性能远优于常规PD控制器,达到了预期的控制目的。

Multiple model predictive control of robot manipulator

A multiple model predictive control method based on membership function was proposed according to nonlinear characteristics of robot manipulator.An appropriate scheduling variable was selected according to the characteristics of the robot manipulator.The operation space of the robot manipulator was divided into several subspaces,the robot manipulator was linearized at equilibrium point in each subspace,and linear sub-models were built in each subspace,and the multiple model presentation of the robot manipulator was developed.Then,local predictive controllers were designed according to each linear sub-model,and make it satisfy with control requirements in the subspace.Finally,the local predictive controllers were combined by trapezoidal membership functions into a global multiple model predictive controller to control the robot manipulator.The simulation results show that the control performance of the global multiple model predictive controller based on the membership functions is superior to conventional PD controller when the robot manipulator was working in a wide operating range,so as to realize the desired control goal.