Neural Network-Based Hybrid Position/Force Tracking Control for Robotic Systems Without Velocity Measurement

摘要

In this paper, a hybrid position/force tracking control scheme based on neural network observer is proposed for robotic systems with uncertain parameters and external disturbances. First, an observer based on neural network is designed to estimate joint velocities. Then, a neural network-based adaptive hybrid position/force controller is proposed based on the observed joint velocities. By using strict positive real method and Lyapunov stability theory, it is proved that all the signals of the closed-loop system are ultimately uniformly bounded. Finally, the simulation tests on a two-link manipulator are conducted. The simulation results show the feasibility and effectiveness of the control scheme.