Discrete-Time Decentralized Inverse Optimal Higher Order Neural Network Control for a Multi-Agent Omnidirectional Mobile Robot

Abstract

This chapter presents a new approach to multi- agent control of complex systems with unknown parameters and dynamic uncertainties. A key strategy is to use of neural inverse optimal control. This approach consists in synthesizing a suitable controller for each subsystem, which is approximated by an identifier based on a recurrent high order neural network (RHONN), trained with an extended Kalman filter (EKF) algorithm. On the basis of this neural model and the knowledge of a control Lyapunov function, then an inverse optimal controller is synthesized to avoid solving the Hamilton Jacobi Bellman (HJB) equation. We have adopted an omnidirectional mobile robot, KUKA youBot, as robotic platform for our experiments. Computer simulations are presented which confirm the effectiveness of the proposed tracking control law.