ANN-based PID controller for an electro-hydraulic servo system
ANN-based PID controller for an electro-hydraulic servo system
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DOI：
10.1109/ical.2008.4636112
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发表时间：
2008-09
期刊：
影响因子：
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通讯作者：
Jianjun Yao;Li-quan Wang;Caidong Wang;Zhonglin Zhang;Peng Jia
中科院分区：
文献类型：
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作者：
Jianjun Yao;Li-quan Wang;Caidong Wang;Zhonglin Zhang;Peng Jia
为实现电液伺服系统的高精度跟踪控制，提出了一种基于神经网络的PID控制器控制方案。采用了PID控制器作为反馈控制器，保证了系统的稳定性。采用小脑模型关节控制器(CMAC)神经网络作为前馈补偿器辨识逆系统动力学模型。CMAC和PID控制器是并联的。该并联控制器的输出可归结为总的控制动作。提出了一种非线性跟踪微分器(NTD)，用于产生高质量的差分信号以用于PID控制器。该控制算法的主要任务是通过CMAC学习算法使总控制量与CMAC输出之间的误差最小。因此，控制动作由CMAC形成。数值仿真结果表明，与常规的PID控制策略相比，该控制策略具有良好的系统性能，包括高精度的轨迹跟踪能力和抗扰动能力。
A control scheme of ANN-based PID controller is developed here to reach high precision tracking control for an electro-hydraulic servo system. The PID controller is used as a feedback controller to guarantee the system stability. The cerebellar model articulation controller (CMAC) neural network is used as a feed-forward compensator to identify the inverse system dynamics model. The CMAC and the PID controller are connected in parallel. The outputs of this paralleled controller are summed up as the total control action. A nonlinear tracking differentiator (NTD) is presented to yield high quality differential signals for the PID controller. The main task of this control algorithm is to make the error between the total control action and the output of the CMAC minimize by the CMAC learning algorithm. Thus the control action is formed by the CMAC. Numerical simulation results show comparing with conventional PID control strategy this proposed control scheme has an excellent system performance including high precision trajectory tracking ability and rejection of disturbance.