伺服系统转动惯量辨识及控制器PI参数优化

Servo System Inertia Identification and Controller PI Parameter Optimization

在机器人运行时,为了使伺服电机在最优性能下达到目标速度、在工作过程中有着更强的抗扰动能力,并避免出现震荡、谐振的状况,从而造成机器人运行时动态稳定性严重降低。提出一种基于非线性动态学习因子的粒子群优化算法,对普通粒子群优化算法进行改进。该算法以伺服系统控制模型中的速度控制器为核心,实时辨识负载转动惯量值,使伺服系统内部控制参数根据实际工况调节;运用该辨识值,通过计算得到速度控制PI参数值,并实时修正速度控制器PI参数值。MATLAB/SIMULINK仿真结果表明,与传统的粒子群优化算法相比,无论在电机启动过程中、还是负载扰动下,该方法都具有更快的响应速度、更高的控制精度以及更强的抗干扰能力。

During the operation of the robot,in order to make the servo motor achieve the target speed under the optimal performance,and have stronger anti-disturbance ability in the working process,and to avoid the problem of vibration and resonance,resulting in a serious reduction in the dynamic stability of the robot.The control model of servo motor is analyzed,and a particle swarm optimization algorithm based on nonlinear dynamic learning factor is proposed.The algorithm takes the speed controller in the servo system control model as the core,and can identify the load's moment of inertia in real time,so that the internal control parameters of the servo system can be adjusted according to the actual conditions.By using the identification value,the PI parameter value of the speed control is obtained through calculation,and the PI parameter value of the speed controller is corrected in real time.The results of MATLAB/SIMULINK simulation show that compared with the traditional particle swarm optimization algorithm,this method has faster response speed,higher control accuracy and stronger anti-interference ability,whether in the motor starting process or under the load disturbance.