Asymptotic Adaptive Neural Network Tracking Control of Nonhlonomic Mobile Robot Formations
In this paper, asymptotically stable control laws are developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation. First, a kinematic controller is developed around control strategies for single mobile robots and the idea of virtual leaders. The virtual leader is replaced with a physical mobile robot leader, and an auxiliary velocity control law is developed in order to prove the global asymptotic stability of the followers which in turn allows the local asymptotic stability of the entire formation. A novel approach is taken in the development of the dynamical controller such that the torque control inputs for the follower robots include the dynamics of the follower robot as well as the dynamics of its leader, and two cases are considered-the case when the robot dynamics are known and the case when they are unknown. In the first case, a robust adaptive control term is utilized to account for unmodeled dynamics. For the latter, a robust adaptive term is augmented with a NN control law to achieve asymptotic tracking performance in contrast with most NN controllers where a bounded tracking error result is shown. Additionally, the NN approximation error is assumed to be a function of tracking errors instead of a constant upper bound, which is commonly found in the literature. The stability of the follower robots as well as the entire formation is demonstrated in each case using Lyapunov methods and numerical results are provided.
T. A. Dierks and J. Sarangapani, "Asymptotic Adaptive Neural Network Tracking Control of Nonhlonomic Mobile Robot Formations," Journal of Intelligent and Robotic Systems, Springer-Verlag, Sep 2009.
The definitive version is available at http://dx.doi.org/10.1007/s10846-009-9336-8
Electrical and Computer Engineering
Keywords and Phrases
Backstepping Control; Mobile Robot Formation Control; Neural Networks; Nonholonomic System
Library of Congress Subject Headings
Article - Journal
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