UKF-Based Active Model and Adaptive Inverse Dynamics Control for Mobile Robot

Active model estimation is of central importance in the autonomous control for unmanned vehicle. In this paper, Unscented Kalman Filter (UKF) is employed for online estimation of both motion states and dynamics parameters of mobile robot. Such an active estimation is further incorporated into a classical inverse dynamics control (IDC). This is intended to make the robot autonomously adaptive to its internal uncertainties, i.e., to achieve a robust tracking performance for time-varying unknown changes in the vehicle dynamics. Extensive simulations are conducted with respect to the dynamics of a developed omni-directional mobile robot to verify the proposed scheme. The convergence of UKF-based estimation is presented in the presence of both noise corrupted signal and quickly changing state/parameters. Results of the IDC enhanced by active estimation are also compared with those of a classic PD control with constant gains to demonstrate the effectiveness of the control scheme.