电液力伺服系统的神经网络并行自适应预测 PI 控制

考虑电液伺服系统的复杂非线性和不确定性特性,提出一类基于神经网络的并行自适应预测PI控制结构,该结构使控制参数的调整和系统的实时控制操作可并行进行,不仅做到了神经模型和控制器的在线辨识和设计,而且避免了神经网络方法通常存在的实时控制的困难,使复杂系统的在线学习控制成为可能。仿真结果表明该控制器具有良好的适应性和鲁棒性。 

ADAPTIVE PREDICTIVE PI CONTROL BASED ON NEURAL NETWORKS FOR ELECTROHYDRAULIC FORCE SERVO SYSTEM

An adaptive predictive PI controllor based on neural networks for electrohydraulic servo system is presented with respect to its complex nonlinearities and uncertainties.The method can be realized by a parallel structure which is composed of a real-time controller for real-time control operation,a predictive controller for regulation of control parameters and a supervising mechanism to determine whether or not the realtime controller is replaced by the predictive controller according to the variations of control performance. Thus, the real-time controller can be adjusted independently of the real operation of the controlled system by introducing neural networks as predictive model and predictive controller.It avoids the difficulty of the real-time control in control methods based on neural networks,thus makes the on-line learning control of complex systems possible.The learning ability of neural networks enables the predictive model to be modified on-line to simulate the behavior of the controlled system, and makes the predictive controller adjustable adaptively to variation of the system dynamical properties.Simulation results demonstrates its satisfactory control performance and it has been applied to a electrohydraulic servo structural testing system with success.