Neural Network Based Adaptive Backstepping Control for Electro-Hydraulic Servo System Position Tracking

Abstract

The modeling uncertainties and external disturbances of electro-hydraulic servo system (EHSS) deteriorate the system’s trajectory tracking performance. To cope with this issue, an adaptive backstepping controller based on neural network (NN) is proposed in this paper. A radial-basis-function neural network (RBF NN) is constructed to approximate the lumped uncertainties caused by modeling uncertainties and external disturbances, where the adaptive law is adopted to adjust controller parameters online. The backstepping control is used to eliminate mismatched nonlinear terms and stabilize the system. The dynamic surface control (DSC) is adopted to handle the “explosion of complexity” problem of backstepping method and reduce the computational burden. Compared to the traditional backstepping control, the proposed control scheme improves the steady-state tracking precision and makes the control signal smaller. In addition, the stability analysis shows that the tracking error can asymptotically converge to zero in the face of time-varying unknown dynamics. Simulation and experiment results demonstrate the effectiveness of the controller in term of tracking accuracy and disturbance rejection in comparison with other controllers for the EHSS.

Research on Compliance Control of Electro-Hydraulic Loading Experimental System

“…Adaptive sliding mode control [18] combines the benefits of sliding mode control and adaptive methods to handle system uncertainties effectively. Neural network control [19] employs artificial neural networks to model complex nonlinearities, offering a sophisticated approach to control hydraulic systems. Indirect adaptive backstepping control [20] integrates the robustness of backstepping control with the adaptability of indirect adaptive methods, making it particularly suitable for systems with uncertain dynamics.…”

An Alternative Nonlinear Lyapunov Redesign Velocity Controller for an Electrohydraulic Drive

“…In this approach, the system is dismembered in 1rst order subsystems and the clf is recursively constructed through virtual controls. Researchers combine this approach with adaptive control [32], sliding mode control [33], neural networks [34], [35]. However, common EHSS system order varies between three and five [36], [37].…”