Adaptive neural network force tracking control of hydraulic manipulators with output constraints
LIANG Xiang-long
YAO Jian-yong
Abstract：In order to solve the problem of unknown dynamics of the hydraulic manipulator with output constraints and improve the force tracking performance of the hydraulic manipulator in unknown environments,an adaptive neural network admittance control method integrating integral barrier Lyapunov function is proposed in this paper.Firstly,the mechanical and hydraulic system dynamics of the hydraulic manipulator are addressed,and according to the principle of impedance control,an adaptive generation method of reference trajectory based on environment parameters estimation is developed.Then,an adaptive radial basis function neural network tracking control is developed for the hydraulic manipulator with unknown mechanical system dynamics and output constraints.Meanwhile,the dynamic surface control approach is intro-duced to circumvent the direct derivation of virtual signals,and the stability of the closed-loop control system is analyzed via Lyapunov technique.Finally,using the MATLAB/Simulink,Simscape Multibody and Simscape Fluids simulation platforms to simulate the hydraulic manipulator,the results indicate that the developed control law has good robustness with respect to unknown mechanical system dynamics,achieves satisfactory position and force tracking performance,and ensures the system output does not exceed the prescribed range.
Keywords：hydraulic manipulatoradmittance controldynamic surface controlneural networkforce tracking controlunknown environment
Publication Date：2025-01-27
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 138-148 )