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Research on Sliding Mode Control of IC Packaging Equipment Precision Motion Platform with Disturbance Compensation

机译:具有干扰补偿的IC封装设备精密运动平台的滑模控制研究

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the permanent magnet linear synchronous motor (PMLSM) is widely used in the drive motor of high-precision motion platform in IC packaging equipment. Since the servo system of the PMLSM motion platform is susceptible to uncertain factors such as nonlinear effect and external disturbance, traditional PID control methods are difficult to achieve the desired control effects. Aiming to decrease the influence of disturbance and nonlinear friction in PMLM model, a strategy of sliding mode control with RBF neural network and disturbance compensation was proposed. The RBF neural network was used to approximate the system disturbance, which improves the performance of the sliding mode controller. Lyapunov stability analysis to prove the stability of the sliding mode control strategy was provided. The nonlinear friction as disturbance compensation acts into the PMLSM Controller, which reduces the crawling and oscillating phenomenon caused by the friction force and improves the positioning accuracy of the motion platform. A feed-forward controller is added in the control system to improve the response time. The simulation results of MATLAB simulation platform show that control strategy improves the robustness, the response speed and positioning accuracy of the system comparing to the traditional PID control strategy.
机译:永磁直线同步电动机(PMLSM)广泛用于IC封装设备中高精度运动平台的驱动电动机。由于PMLSM运动平台的伺服系统易受诸如非线性效应和外部干扰之类的不确定因素的影响,因此传统的PID控制方法很难实现所需的控制效果。为了减少扰动和非线性摩擦对PMLM模型的影响,提出了一种基于RBF神经网络和扰动补偿的滑模控制策略。 RBF神经网络用于近似系统扰动,从而提高了滑模控制器的性能。 Lyapunov稳定性分析证明了滑模控制策略的稳定性。非线性摩擦作为扰动补偿作用在PMLSM控制器中,从而减少了由摩擦力引起的爬行和振荡现象,并提高了运动平台的定位精度。在控制系统中添加了前馈控制器,以缩短响应时间。 MATLAB仿真平台的仿真结果表明,与传统的PID控制策略相比,控制策略提高了系统的鲁棒性,响应速度和定位精度。

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