Robust model predictive control with disturbance invariant sets
Robust model predictive control with disturbance invariant sets
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DOI：
10.1109/acc.2010.5531520
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
Shuyou Yu;C. Böhm;Hong Chen;F. Allgöwer
中科院分区：
文献类型：
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作者：
Shuyou Yu;C. Böhm;Hong Chen;F. Allgöwer
针对具有状态约束和输入约束以及未知有界扰动的非线性系统，提出了一种鲁棒模型预测控制方案。在线求解了一个具有收紧约束的标准标称模型预测控制问题，其解定义了标称轨迹。离线确定辅助控制律，使误差系统的轨迹保持在扰动不变集中。因此，原始非线性系统的演化在于以标称轨迹为中心的扰动不变量集。进一步证明了当标准标称优化问题初始可行时，闭环系统的可行性和稳定性都得到了保证。
This paper proposes a robust model predictive control scheme for nonlinear systems with state and input constraints and unknown but bounded disturbances. A standard nominal model predictive control problem with tightened constraints is solved online, and its solution defines the nominal trajectory. An ancillary control law is determined off-line which keeps the trajectories of the error system in a disturbance invariant set. Thus, the evolution of original nonlinear system lies in the disturbance invariant set centered along the nominal trajectory. Furthermore, it is shown that both feasibility and stability of the closed-loop system are guaranteed if the standard nominal optimization problem is initially feasible.