Neural Network Control of Nonholonomic Robot Formations Using Limited Communication with Reliability Assessment

Architectures for the control of mobile robot formations are often described by three levels of abstraction: an intelligence layer for task planning, a network layer for relaying commands and information throughout the formation, and finally, at the lowest level of abstraction is a robot model layer where each robot is locally controlled to be consistent with the current formation task. In this work, the network and robot model layers are considered, and an output feedback control law for leader-follower based formation control is developed using neural networks (NN) and limited communication. A NN is introduced to approximate the dynamics of the follower as well as its leader using online weight tuning while a novel NN observer is designed to estimate the linear and angular velocities of both the follower robots and its leader. Thus, each robot can achieve its control objective with limited knowledge of its leader\u27s states and dynamics while simultaneously reducing the communication load required in the network layer. It is shown using Lyapunov theory that the errors for the entire formation are uniformly ultimately bounded while relaxing the separation principle. Numerical results are provided to verify the theoretical conjectures, and the reliability of the scheme is evaluated by introducing processing and communication delays, as well as communication failures