基于阻尼最小二乘法的神经网络预测偏差补偿自校正控制器

SELF-TUNING CONTROLLER FOR NEURAL NETWORK PREDICTIVE DEVIATION COMPENSATION BASED ON DAMPED LEAST SQUARE

  • 摘要: 本文提出一种神经网络预测偏差补偿自校正控制器,用线性模型的预测控制去控制非线性系统,其预测偏差用神经网络进行补偿.线性模型的辨识和神经网络的学习均采用阻尼最小二乘法.仿真结果表明,用这种控制器能有效地控制非线性系统,并具有超调小,鲁棒性好的特点.

     

    Abstract: In this paper, we propose a kind of neural network predictive deviation compensation self-tuning controller, using linear model to control nonlinear system. The predictive deviation's compensation is completed by neural network. The damped least square method was applied in both identification of the linear model and study of the neural network. The simulation results show that this kind of controller can control nonlinear system effectively and has small overshoot and good stability.

     

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