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静电悬浮控制系统中存在建模不准确及对象扰动,传统控制器只能在动态控制精度和扰动消除性能之间折衷;为了克服其对控制器精度的影响,研究了带扰动消除的自适应逆控制算法。以非线性自回归动态神经网络进行正模型、逆模型以及扰动消除控制器的实时辨识,利用基于遗传算法的改进粒子群算法进行神经网络的更新,以提高自适应收敛速度和精度。设计了基于DSP与PC的仿真环境,分别部署静电悬浮虚拟被控对象和自适应逆控制算法,实现对控制算法的实时验证。结果表明所设计的控制结构与算法可以实现对静电悬浮的稳定控制与扰动消除。利用PC和相应的I/O接口,以及所部署的实时控制算法可以实现快速控制原型,为控制器的工程实现提供基础。
In the electrostatic levitation control system, modeling inaccuracies and object disturbances exist. The traditional controller can only compromise between the dynamic control accuracy and the disturbance elimination performance. In order to overcome its influence on the accuracy of the controller, the adaptive inverse with disturbance elimination Control algorithm. Nonlinear autoregressive dynamic neural network is used to carry out real-time identification of positive model, inverse model and disturbance elimination controller. The improved particle swarm optimization algorithm based on genetic algorithm is used to update the neural network to improve the speed and precision of adaptive convergence. The simulation environment based on DSP and PC is designed. The electrostatic suspended virtual controlled object and adaptive inverse control algorithm are respectively deployed to realize the real-time verification of the control algorithm. The results show that the designed control structures and algorithms can achieve stable control and disturbance elimination of electrostatic levitation. The PC and corresponding I / O interface, as well as the deployed real-time control algorithm, can realize the rapid control prototype and provide the basis for the engineering of the controller.