Implementation of a robust data-driven control approach for an ommi-directional mobile manipulator based on koopman operator

The dynamic modeling and control of omni-directional mobile manipulators (OMM) are challenging since they are highly nonlinear, strongly coupled, and multi-input multi-output uncertainty systems. Koopman operator theory can provide an explicit linear dynamic model for OMM, only based on input-output data. However, the derived dynamic model usually has modeling errors and cannot capture external unknown disturbances. In this paper, a robust data-driven control scheme is designed and implemented for an OMM, based on Koopman operator and a disturbance observer. Firstly, a data-driven Koopman model of OMM is constructed, solely using the collected input-output data. Then a disturbance observer is designed to online estimate the inherent modeling error of the Koopman model as well as external disturbances. The controller is designed by combining linear MPC with feedback compensation of the estimated disturbances. Finally, both simulations and experimental tests are conducted to verify the effectiveness and robustness of the proposed control scheme.