结合阀控系统响应速度快和泵控系统能量效率高的优势，提出了一种泵阀并联 系统，并研究了该系统的智能控制方法。首先针对泵阀并联系统中的泵控子系统设计了 可以实现权值自适应调节的单神经元 PID 控制器，然后针对泵阀并联系统中阀控子系统 的参数不确定性和外负载干扰问题设计了 RBF 神经网络滑模控制器，并利用 Lyapunov 函数证明了闭环系统的稳定性。最后搭建了泵阀并联电液位置伺服系统的 Matlab /Simulink 和 AMESim 联合仿真模型。仿真结果表明，泵控子系统的单神经元 PID 控制器相比 于传统 PID 控制器具有更好的转速跟踪控制性能，阀控子系统的 RBF 神经网络滑模控 制器相比于传统 PID 控制器和传统滑模控制器具有更高的位置跟踪精度和更强的抗干扰 能力，所提出的泵阀并联智能控制方法有效改善了系统的控制性能。

In light of the valve control system's advantages of fast response and high energy efficiency，a pumpvalve parallel electro-hydraulic position servo system was proposed，and its intelligent control method is studied． Firstly，a single neuron PID controller，which can realize the self-adaptive adjustment of the weight，is designed for the pump-controlled subsystem of the pump-valve parallel system． Secondly，a RBF ( Radial Basis Function) neural network-based sliding mode controller was designed for the valve-controlled subsystem of the pump-valve parallel system． The Lyapunov function was designed to prove the stability of the closed-loop system． Finally，the co-simulation was conducted by using Matlab /Simulink and AMESim． The co-simulation results show that the single neuron PID controller of the pump-controlled subsystem has better speed tracking control performance than the traditional PID controller． The RBF neural network-based sliding mode controller of the valve-controlled subsystem has better position tracking accuracy and stronger anti-interference ability than the traditional PID controller． The proposed intelligent control method can significantly improve the control performance of the pump-valve parallel system.