An improved robust model predictive control for linear parameter-varying input-output models
An improved robust model predictive control for linear parameter-varying input-output models
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DOI：
10.1002/rnc.3906
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发表时间：
2018-02-01
影响因子：
3.9
通讯作者：
Meskin, N.
中科院分区：
文献类型：
--
作者：
Abbas, H. S.;Hanema, J.;Meskin, N.
本文提出了一种新的鲁棒模型预测控制（MPC）方案，用于控制受输入输出约束的离散线性变参数输入输出模型。对于潜在的在线MPC优化问题，通过包含离线解的二次端点代价和椭球端点集来保证闭环渐近稳定性。与先前发表的结果相比，该方案的主要吸引人的特点是所有离线计算现在都基于凸优化问题，这大大降低了保守性和计算复杂度。此外，该方法在不增加复杂度的前提下，可以处理更广泛的线性参数变化的输入输出模型。以连续搅拌罐式反应器为例，给出了该方法的预测控制。
This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal set, which are solved offline, for the underlying online MPC optimization problem. The main attractive feature of the proposed scheme in comparison with previously published results is that all offline computations are now based on the convex optimization problem, which significantly reduces conservatism and computational complexity. Moreover, the proposed scheme can handle a wider class of linear parameter-varying input-output models than those considered by previous schemes without increasing the complexity. For an illustration, the predictive control of a continuously stirred tank reactor is provided with the proposed method.