This paper addresses a computationally efficient robust tube-based model predictive control (RTBMPC) strategy of linear systems in the presence of bounded disturbance. In the RTBMPC strategy, a nominal system is introduced by ignoring the disturbances of uncertain system, and then the uncertain system will be controlled in a robust manner through its nominal system as well as an additional feedback term which rejects a bounded additive disturbance. In this paper, the tracking problem is converted into the regulation problem by introducing an extra system called regulation nominal system that its constraints are translated from tracking into regulation. It leads to a reduction in complexity of the objective function and simplification of driving the stability theory. On the other hand, RTBMPC strategy solves optimization problem for nominal system which ignores the disturbances. Since in the absence of disturbances, the state measured at the following sample will be the same as the one predicted by model, a variable prediction horizon is suggested to reduce the computational burden. In addition, new constraints are introduced to prove the recursive feasibility, local and asymptotic stability. The constrained sampled double integrator is presented to illustrate the effectiveness of the proposed RTBMPC.

本文讨论了在有界扰动的情况下线性系统的一种计算有效的鲁棒基于管的模型预测控制（RTBMPC）策略。在RTBMPC策略中，通过忽略不确定系统的扰动引入标称系统，然后通过其标称系统以及拒绝有界加性扰动的附加反馈项，以鲁棒方式控制不确定系统。在本文中，通过引入一个称为规制标称系统的额外系统将跟踪问题转换为规制问题，该系统将其约束条件从跟踪转化为规制。这导致目标函数的复杂度降低，并且简化了稳定性理论的驱动。另一方面，RTBMPC策略解决了忽略干扰的名义系统优化问题。由于在没有干扰的情况下，在以下样本中测量的状态将与模型预测的状态相同，因此建议使用可变的预测范围以减少计算负担。此外，引入了新的约束条件以证明递归的可行性，局部和渐近稳定性。提出了约束采样双积分器，以说明所提出的RTBMPC的有效性。局部和渐近稳定。提出了约束采样双积分器，以说明所提出的RTBMPC的有效性。局部和渐近稳定。提出了约束采样双积分器，以说明所提出的RTBMPC的有效性。