Abstract
In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are asymptotically stable and the NN weights are bounded as opposed to uniformly ultimately bounded (UUB) stability which is typical with most NN controllers. Theoretical results are demonstrated using numerical simulations.
Recommended Citation
J. Sarangapani and T. A. Dierks, "Neural Network Control of Robot Formations Using RISE Feedback," Proceedings of the International Joint Conference on Neural Networks, 2007. IJCNN 2007, Institute of Electrical and Electronics Engineers (IEEE), Jan 2007.
The definitive version is available at https://doi.org/10.1109/IJCNN.2007.4371402
Meeting Name
International Joint Conference on Neural Networks, 2007. IJCNN 2007
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Sponsor(s)
GAANN Program
University of Missouri--Rolla. Intelligent Systems Center
Keywords and Phrases
Lyapunov Method; RISE; Formation Control; Kinematic/Dynamic Controller; Neural Network
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2007
Included in
Computer Sciences Commons, Electrical and Computer Engineering Commons, Operations Research, Systems Engineering and Industrial Engineering Commons