LAUSR

This letter proposes a stabilizing model predictive control (MPC) scheme with preview information of disturbance for nonlinear systems. The proposed MPC algorithm is able to not only reject disturbance by making use of disturbance preview information as necessary, but also take advantage of the disturbance if it is good for a control task. This is realized by taking into account both the task (e.g., reference trajectory) and disturbance preview in the prediction horizon when performing online optimization. Conditions are established to ensure recursive feasibility and stability under disturbance. First the disturbance within the horizon is augmented with the state to form a new composite system and then the stage cost function is modified accordingly. With the help of input-to-state stability theory, a terminal cost and a terminal constraint are constructed and added to the MPC algorithm with preview to guarantee its recursive feasibility and stability under a pre-bounded disturbance. Numerical simulation results demonstrate the effectiveness of the proposed MPC algorithm.