Event-Triggered Data-Driven Robust Model Predictive Control for an Omni-Directional Mobile Manipulator

Omni-directional mobile manipulators (OMMs) are inherently nonlinear, strongly coupled, and multiple-input multiple-output systems, posing significant challenges in developing accurate mechanistic models due to their complexity. Koopman operator theory offers a data-driven modeling framework that leverages input–output data to characterize system dynamics, but there often exist modeling errors. In this paper, an event-triggered data-driven linear model predictive control (MPC) framework is proposed for an OMM, without using any prior knowledge of the robot system. A finite-dimensional approximate linear Koopman model is established for an OMM using input–output data. The Gaussian process regression (GPR) is employed to estimate the model’s errors, while an extended state observer (ESO) is designed to estimate external disturbances. Since the introduction of GPR increases the computational burden, an event-triggered (ET) mechanism is introduced to reduce unnecessary controller recomputations and controller update frequency. Finally, comparative experiments are carried out to verify the effectiveness and performance superiority of the proposed control scheme.