원문정보
초록
영어
Intelligent robot not only can realize reservation on conduct and actions, but also can be understand the characteristics of the unknown environment and adapt to changes in the environment through their own "sensory system", robots get outside information mainly by vision, this paper take the joint robot of binocular robot vision system as control plant, pointing on the problem of the uncertainties that existed in the dynamic model of binocular vision robot may cause instability. This paper has proposed a sliding model control scheme with RBF neural network adaptive control strategy based on sub-block approximation algorithm, in this control method, sliding model control was used to control trajectory of the joints of robot, and utilize the RBF neural network to approximate the each uncertain in the dynamic model of robot. The simulation results show that compared with the RBF neural network adaptive control strategy based on integral approximation for uncertainness, the proposed control method has features with good position tracking.
목차
1. Introduction
2. Design of the Binocular Vision
3. Radial Basis Function Neural Network
4. Problem Statement
5. Controller Based on RBF with Overall Approach
5.1. The Design of the Controller
5.2. Stability and Convergence Analysis
6. RBF Neural Network Adaptive Control Strategy Based on Sub-Block Approximation Algorithm
6.1 Control Law
6.2. Numerical Simulation
7. Conclusions
References