Model predictive control for linear parameter varying constrained systems using ellipsoidal set prediction
Model predictive control for linear parameter varying constrained systems using ellipsoidal set prediction
复制标题
DOI：
10.1080/00207170601030622
复制
发表时间：
2007-02
期刊：
影响因子：
--
通讯作者：
Hiromi Suzuki;T. Sugie
中科院分区：
文献类型：
--
作者：
Hiromi Suzuki;T. Sugie
针对输入受限且参数变化有界的线性参数变化系统，提出了一种新的模型预测控制方法。该方法采用闭环预测，构造椭球集，以合理的计算量预测未来状态。然后利用参数变化率的信息来提高预测的准确性。此外，我们还引入了一个放宽的终端条件，以扩大系统的可镇定区域，从而保证了系统在无限水平上的稳定性。结果表明，MPC问题在初始阶段的可行性保证了闭环系统的稳定性。最后，仿真结果说明了该方法的有效性。
This paper proposes a new model predictive control (MPC) method for linear parameter varying systems with bounded parameter variation subject to input constraints. The method adopts closed-loop prediction and constructs ellipsoidal sets to predict the future states with reasonable computational effort. Then the information on the parameter variation rate is exploited to improve the accuracy of the prediction. Furthermore, a relaxed terminal condition, which guarantees the stability for infinite horizon, is introduced to enlarge the stabilizable region. It is shown that the feasibility of the MPC problem at the initial step ensures the stability of the closed-loop system. Finally, a simulation result illustrates the effectiveness of the proposed method.