Disturbance-Aware Model Predictive Control of Underactuated Robotics Systems

While robust model predictive control (MPC) has been studied extensively in recent decades, addressing unmatched disturbances in underactuated robotic systems is still challenging. In this paper, we propose a method to enhance the robustness of the MPC through the online estimation of disturbances using a nonlinear disturbance observer (NDOB). We call this method disturbance-aware MPC (DA-MPC), because the proposed method explicitly utilizes the estimated disturbance in the future prediction. We provide a performance analysis of the NDOB, establishing the boundedness between the predicted and real states. The main advantages of the DA-MPC include its applicability to real-time control and its compatibility with off-the-shelf optimal control problem (OCP) solvers. We demonstrate the application of the proposed method using an underactuated quadrotor system. The simulation validation shows the effectiveness of the proposed method compared to ℒ1-adaptive MPC, which is one of the state-of-the-art robust MPC methods.