Research On Tracking Control Of Manipulator Arm Based On Adaptive Neural Network

In recent years,the problem of trajectory tracking control of robotic arm systems have received more and more attention.In the actual system model,the external disturbance and the uncertainty of the system are inevitable that will make the control method which based on the accurate model is difficult to achieve or reach the ideal performance of tracking control,and it is even more difficult to ensure the stability and uniform ultimate boundedness of the entire closed-loop system.This paper aims at two types of nonlinear systems with full state constraints,a state-constrained control scheme based on an adaptive neural network is proposed in the condition of fully considering the uncertainty of the system and external disturbances,and the proposed control scheme could successfully achieve asymptotically tracking control.As for the trajectory tracking control problem of the robotic arm system,this paper mainly studies the following two aspects:1)A kind of trajectory tracking control problems of manipulator with full state constraints is studied.First,an adaptive neural network algorithm is used to deal with uncertainty terms and external disturbances of the system.An obstructive Lyapunov function is designed to ensure the state constraint is not violated,and the Moore-Penrose pseudo-inverse term is used in the control design.Secondly,through stability analysis,it is proved that each state finally converges to a compact set,and the entire closed-loop system is finally uniformly bounded,and the asymptotically tracking control of the system can be realized.Finally,the effectiveness of the control method is verified by selecting appropriate parameters of design system for simulation experiments.2)A kind of adaptive impedance control problems of manipulator with input saturation is studied,considering the uncertainty and input saturation of the system.First,an adaptive neural network algorithm is used to approximate the uncertainty of the system,and an auxiliary system is designed to deal with input saturation.Secondly,an adaptive neural impedance controller is designed by using the Lyapunov method,and the stability of the closed-loop system is proved through the Lyapunov stability theorem.Finally,in order to verify the effectiveness of the proposed control protocol,a numerical simulation of the system is carried out.