Tube-based nonlinear model predictive control for autonomous skid-steer mobile robots with tire–terrain interactions. (August 2020) - Prado, Álvaro Javier; Torres-Torriti, Miguel; Yuz, Juan; Auat Cheein, Fernando
Abstract: This work addresses the problem of robust tracking control for skid-steer mobile platforms, using tube-based Nonlinear Model Predictive Control. The strategy seeks to mitigate the impact of disturbances propagated to autonomous vehicles originated by traction losses. To this end, a dynamical model composed by two coupled sub-systems stands for lateral and longitudinal vehicle dynamics and tire behavior. The controller is aimed at tracking prescribed stable operation points of the slip and side-slip beyond the robot pose and speeds. To reach robust tracking performance on the global system, a centralized control scheme operates under a predictive control framework composed by three control actions. The first one compensates for disturbances using the reference trajectory-feedforward control. The second control action corrects the errors generated by the modeling mismatch. The third one is devoted to ensure robustness on the closed-loop system whilst compensating for deviations of the state trajectories from the nominal ones (i.e. disturbance-free). The strategy ensures robust feasibility even when tightening constraints are met. Such constraints are calculated on-line based on robust positively invariant sets characterized by polytopic sets (tubes), including a terminal region to guarantee robustness. The benefits of the controller regarding tracking performance, constraint satisfaction and computational practicability were tested through simulations with a Cat® 262C skid-steer model. Then, in field tests, the controller evidenced high tracking accuracy against terrain disturbances when benchmarking performance with respect to inherent robust predictive controllers. Graphical abstract: Highlights: A new tube-based control strategy on a centralized framework consolidates robustness in the NMPC performance. Tracking and modeling mismatch errors of the lateral and longitudinal vehicle dynamics are characterized within a single polytopic region. The online tightening of constraints on traction and turning control actions makes of NMPC restrictive to uncertainties and terrain disturbances. The computational performance assessment demonstrates execution times practical for real-time implementation of the robust NMPC. Experimental tests evidence high tracking accuracy against tire–terrain interactions when benchmarking with inherently robust controllers.