An integrated model predictive control scheme with disturbance preview
Xing Fang, Wen‐Hua Chen, Fei Liu
Abstract
Abstract This article proposes an integrated model predictive control (MPC) framework with disturbance preview information for nonlinear systems. It is assumed that the disturbance can be previewed within the prediction horizon but unknown outside the horizon. First an integrated terminal control law consisting of both feedback and feedforward is considered. Based on that, a procedure is presented to calculate the associated terminal constraints and terminal cost. A new MPC formulation is then presented with these terminal elements and it is shown that stability and recursive feasibility can be guaranteed under the proposed design using the input‐to‐state stability tool. Another distinctive feature of the proposed MPC scheme is that the disturbance and the reference information in the horizon is integrated in online optimization, rather than treating disturbance rejection and trajectory following separately, which makes it possible to make full use of the predictable disturbance if it is beneficial to the control task. Numerical examples show that this integrated MPC yields a larger stability region and better performance under prescribed disturbance in comparison with the existing MPC algorithms with disturbance preview.