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Statistical information based two-layer model predictive control with dynamic economy and control performance for non-Gaussian stochastic process
Journal of the Franklin Institute ( IF 4.1 ) Pub Date : 2021-01-13 , DOI: 10.1016/j.jfranklin.2021.01.007
Mifeng Ren , Junghui Chen , Peng Shi , Gaowei Yan , Lan Cheng

In this paper, a two-layer model predictive control (MPC) hierarchical architecture of dynamic economic optimization (DEO) and reference tracking (RT) is proposed for non-Gaussian stochastic process in the framework of statistical information. In the upper layer, with state feedback and dynamic economic information, the economically optimal trajectories are estimated by entropy and mean based dynamic economic MPC, which uses the nonlinear dynamic model instead of the steady-state model. These estimated optimal trajectories from the upper layer are then employed as the reference trajectories of the lower layer control system. A survival information potential based MPC algorithm is used to maintain the controlled variables at their reference trajectories in the nonlinear system with non-Gaussian disturbances. The stability condition of closed-loop system dynamics is proved using the statistical linearization method. Finally, a numerical example and a continuous stirred-tank reactor are used to illustrate the merits of the proposed economic optimization and control method.



中文翻译:

基于统计信息的非高斯随机过程动态经济性和控制性能的两层模型预测控制

在统计信息的框架下,针对非高斯随机过程,提出了动态经济优化(DEO)和参考跟踪(RT)的两层模型预测控制(MPC)层次结构。在上层,利用状态反馈和动态经济信息,通过基于熵和均值的动态经济MPC估算经济最优轨迹,该模型使用非线性动态模型代替稳态模型。然后,将来自上层的这些估计的最佳轨迹用作下层控制系统的参考轨迹。基于生存信息势的MPC算法用于在非高斯扰动的非线性系统中将控制变量维持在其参考轨迹上。利用统计线性化方法证明了闭环系统动力学的稳定性条件。最后,通过一个数值例子和一个连续搅拌釜反应器来说明所提出的经济优化和控制方法的优点。

更新日期:2021-03-02
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