Real-time neural identification and inverse optimal control for a tracked robot

Abstract

This work presents the implementation in real-time of a neural identifier based on a recurrent high-order neural network which is trained with an extended Kalman filter-based training algorithm and an inverse optimal control applied to a tracked robot. The recurrent high-order neural network identifier is developed without the knowledge of the plant model or its parameters; on the other hand, the inverse optimal control is designed for tracking velocity references. This article includes simulation and real-time results, both using MATLAB®, and also the experimental tests use a modified HD2® Treaded ATR Tank Robot Platform with wireless communication.