For linear parameter varying (LPV) systems with unknown system states, this article investigates an interval state estimation-based robust model predictive control algorithm. Two interval state estimation approaches, including interval observer systems and zonotope-based box computations, are considered to estimate the upper and lower bounds of system states. The on-line interval estimation error boxes are contained within the scaled and time-varying ellipsoidal robust positively invariant sets. Then, the centers of the state constraint boxes are steered to a region near the origin. When the interval estimation error boxes and the centers of state constraint boxes simultaneously converge to the neighborhood of the origin, the controlled LPV systems are robust stable.

中文翻译：

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基于区间状态估计的线性参数变化系统鲁棒模型预测控制

对于系统状态未知的线性参数变化 (LPV) 系统，本文研究了一种基于区间状态估计的鲁棒模型预测控制算法。两种区间状态估计方法，包括区间观测器系统和基于区位的盒计算，被认为是估计系统状态的上限和下限。在线区间估计误差框包含在缩放和时变椭球鲁棒正不变集内。然后，状态约束框的中心被引导到原点附近的区域。当区间估计误差框和状态约束框的中心同时收敛到原点邻域时，受控LPV系统是鲁棒稳定的。