Constrained Neuro-Identifier for Controlling the Unicycle Mobile Robot

This work proposes the design of a robust controller for the perturbed kinematic model of the unicycle mobile robot, considering a neuro-identifier that imposes restrictions on the identification error. The controller is based on integral sliding modes (ISMs) and the approximation provided by a differen-tial neural network (DNN) for the tracking error dynamics, represented as an uncertain time-varying linear system. The methodology ensures asymptotic stability for the tracking error despite multiplicative disturbances in the control channel. The ISM compensates for the matched dynamics identified with the DNN. Then, a feedback controller based on a Barrier Lyapunov function minimizes the effect of unmatched dynamics while fulfilling state restrictions by solving a set of Linear Matrix Inequalities. Simulation results show the feasibility of the proposed strategy against classical controllers.