This paper proposes a multi-strategy control scheme, which modifies the optimal control problem of robust model predictive control (RMPC) to reduce the on-line computational load or extend the feasible region. The proposed controller is designed to stabilize the system with respect to a subset of the disturbance set. If the disturbance is realized from the rest of the subset, another control strategy is automatically involved to keep the state inside the pre-determined bounded set. The existence of this pre-determined set is proven, and an efficient algorithm is proposed to generate this set. In addition, it is shown that the recursive feasibility and stability of the original RMPC is sustained for the proposed controller. This implies that the proposed method can be applied to a wide range of existing RMPC. Three illustrative examples describe the fundamental ideas and practical advantages.

本文提出了一种多策略控制方案，对鲁棒模型预测控制（RMPC）的最优控制问题进行修正，以减少在线计算量或扩展可行域。所提出的控制器旨在相对于干扰集的子集稳定系统。如果干扰是从子集的其余部分实现的，则自动涉及另一种控制策略以将状态保持在预定的有界集中。证明了这个预先确定的集合的存在性，并提出了一种有效的算法来生成这个集合。此外，它表明原始 RMPC 的递归可行性和稳定性对于所提出的控制器是持续的。这意味着所提出的方法可以应用于广泛的现有 RMPC。