Research on neural network model reference adaptive disturbance rejection control of digital hydraulic cylinder

The nonlinear factors in the digital hydraulic cylinder will reduce the accuracy of the control system. In order to improve the control accuracy of the control system, in this paper, a model reference adaptive disturbance rejection control method based on neural network is proposed. Firstly, the dead zone model is used to replace the nonlinear link in the feedback mechanism. A detailed mathematical model of digital hydraulic cylinder is established and the nonlinear hydraulic spring force is also considered, and a complete nonlinear state space model of digital hydraulic cylinder is derived based on LuGre friction model. Then the reference model is designed. By introducing ESO (extended state observer), the uncertainties and external disturbances of the controlled object are all equivalent to a total disturbance. The RBF (Radial Basis Function) network is used to approximate the unknown function FZ, the neural model reference adaptive disturbance rejection composite controller is designed by using Lyapunov direct method and Barbalat lemma. Finally, the simulation verification is carried out by using MATLAB. The simulation results show that the control strategy can effectively improve the response characteristics of the system, reduce the steady-state error of the system, and improve the robustness of the system.

Automated summary

This paper proposes a model reference adaptive disturbance rejection control method based on neural network for digital hydraulic cylinders. By introducing dead zone model, ESO, and RBF network, the control accuracy and system robustness can be effectively improved.