Paper Info
Title
Model-Based Path Planning And Tracking Control Using Neural Networks For A Robot Manipulator
Abstract
In this paper, a new method is presented for a path planning and tracking control of a planar robot manipulator in the presence of system uncertainty and obstacles using neural networks combined with the conventional feedback controller. Our path planning provides not only the (sub)optimal trajectory for a given cost function through evolutionary algorithm bur also the configurations of the robot manipulator along the path by considering the robot dynamics. The path can be made to keep away from the singular points and avoid the obstacles. An additional neural controller compensates for the tracking errors caused by uncertainty and disturbance, which provides the robustness with a good tracking performance. Computer simulations show the effectiveness of the proposed method.
Year
DOI
Venue
1997
10.1109/ICNN.1997.614162
1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4
Keywords
Field
DocType
cost function,trajectory,evolutionary computation,computer simulation,evolutionary algorithm,path planning,tracking,control systems,adaptive control,robust control,neural network,singular point,neural networks,genetic algorithms,feedback,uncertainty,robots,robustness
Motion planning,Control theory,Computer science,Robustness (computer science),Control system,Adaptive control,Robot,Robust control,Artificial neural network,Trajectory
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
2
Name
Order
Citations
PageRank
Sangbong PARK1154.60
cheol hoon217830.78