Quasi-minmax MPC for constrained nonlinear systems with guaranteed input-to-state stability
Quasi-minmax MPC for constrained nonlinear systems with guaranteed input-to-state stability
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用于约束非线性系统的准最小最大 MPC，并保证输入状态稳定性
DOI：
10.1016/j.jfranklin.2014.03.006
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
陈秋霞
中科院分区：
文献类型：
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作者：
何德峰;黄骅;陈秋霞
针对一类由线性变参数（LPV）系统描述的非线性系统，在输入约束和未知但有界干扰的作用下，提出了一种鲁棒准最小最大模型预测控制算法。所提出的控制算法解决了一个半定规划问题，显式地采用了有限时域成本函数和线性矩阵不等式（LMI）的约束。为了递归的优化的可行性的目的，双模方法是隐含的。在线性矩阵不等式（LMI）范式下，将输入状态稳定性（ISS）和拟最小最大预测控制器相结合，实现了控制器对扰动的闭环ISS。连续搅拌釜式反应器（CSTR）和耦合质量弹簧系统的两个例子被用来证明所提出的结果的有效性。
This paper presents a robust quasi-min–max model predictive control algorithm for a class of nonlinear systems described by linear parameter varying (LPV) systems subject to input constraints and unknown but bounded disturbances. The proposed control algorithm solves a semi-definite programming problem that explicitly incorporates a finite horizon cost function and linear matrix inequalities (LMI) constraints. For the purpose of the recursive feasibility of the optimization, the dual-mode approach is implied. Input-to-state stability (ISS) and quasi-min–max MPC are combined to achieve the closed-loop ISS of the controller with respect to the disturbance in LMI paradigm. Two examples of continuous stirred tank reactor (CSTR) and couple-mass-spring system are used to demonstrate the effectiveness of the proposed results.