Deep Adaptive Nonlinear H∞ Control for Wheeled Mobile Robots
Resumo
Autonomous Mobile Robots (AMRs) are subject to parametric uncertainties and external disturbances. Parameter uncertainties usually arise due to extra devices or loads attached to the robot since they influence the parameters of mass, inertia, the center of mass, and other parameters initially raised to compose the vehicle mathematical model. And the external disturbances are related to the robot’s collision with static or dynamic obstacles or in overcoming obstacles on the ground by the robot’s wheels, where skidding and slippage of the wheels can occur. Based on this context, the present work proposes a robust and adaptive control architecture for the trajectory tracking problem of a mobile robot subject to external disturbances and parametric uncertainties. The proposed approach will comprise a Nonlinear Adaptive Control H∞ based on Deep Learning. The nonlinear H∞ controller will attenuate external disturbances, and the adaptive part, based on Deep Neural Network, will learn the parametric uncertainties related to the mathematical model of the robot. Simulation results demonstrate the effectiveness of the proposed control strategies for a four-wheel mobile robot.
