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.
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
International Joint Conference on Neural Networks, 2007. IJCNN 2007
Electrical and Computer Engineering
University of Missouri--Rolla. Intelligent Systems Center
Keywords and Phrases
Lyapunov Method; RISE; Formation Control; Kinematic/Dynamic Controller; Neural Network
Article - Conference proceedings
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