电液伺服系统RBF神经网络滑模控制

针对阀控液压缸位置伺服系统非线性导致模型参数确定困难及干扰问题,在分析三阶位置控制的电液控制系统原理及模型的基础上,引入神经网络的RBF径向基控制模型和自适应滑模算法,同时考虑了非1负反馈参数,建立了基于RBF神经网络滑模控制的电液伺服控制系统数学模型。通过选取合适的Lyapunov函数,分析了系统稳定性,解决了参数未定及挠动情况下的电液伺服系统控制器设计问题。仿真结果证明,所设计的控制器使系统的输出对给定信号的跟踪精度高,响应快,具有较强的鲁棒性。

RBF Neural Network Sliding Mode Control for Electro Hydraulic Servo System

Aiming at the problem of interference and difficulty of model parameter determination caused by non-linearity of valve-controlled hydraulic cylinder position servo system, on the basis of analysis of electro-hydraulic control servo system principle and model of third-order position control, we introduce a neural network RBF model and an algorithm of adaptive sliding mode control, and non-one negative feedback parameters are considered at the same time. A mathematical model of electro-hydraulic servo control system based on RBF neural network sliding mode control is established. By choosing the appropriate Lyapunov function and analyzing system stability, the design problem of electro-hydraulic servo system controller under the condition of uncertain parameters and torsion is solved. The simulation results show that the designed controller has high tracking accuracy, fast response and strong robustness to a given signal.