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Economic Stochastic Model Predictive Control Using the Unscented Kalman Filter ?

机译：使用无味卡尔曼滤波器的经济随机模型预测控制 ？

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摘要

Economic model predictive control is a popular method to maximize the efficiency of a dynamic system. Often, however, uncertainties are present, which can lead to lower performance and constraint violations. In this paper, an approach is proposed that incorporates the square root Unscented Kalman filter directly into the optimal control problem to estimate the states and to propagate the mean and covariance of the states to consider noise from disturbances, parametric uncertainties and state estimation errors. The covariance is propagated up to a predefined “robust horizon” to limit open-loop covariances, and chance constraints are introduced to maintain feasibility. Often variables in chemical engineering are non-negative, which however can be violated by the Unscented Kalman filter leading to erroneous predictions. This problem is solved by log-transforming these variables to ensure consistency. The approach was verified and compared to a nominal nonlinear model predictive control algorithm on a semi-batch reactor case study with an economic objective via Monte Carlo simulations.
机译：经济模型预测控制是使动态系统效率最大化的一种流行方法。但是，通常会存在不确定性，这可能导致性能降低和违反约束。在本文中，提出了一种方法，该方法将平方根无味卡尔曼滤波器直接合并到最优控制问题中，以估计状态并传播状态的均值和协方差，以考虑来自干扰，参数不确定性和状态估计误差的噪声。将协方差传播到预定义的“鲁棒视野”以限制开环协方差，并引入机会约束以保持可行性。化学工程中的变量通常是非负的，但是Unscented Kalman过滤器可能会违反该变量，从而导致错误的预测。通过对数转换这些变量以确保一致性来解决此问题。通过蒙特卡罗模拟，在经济目标的半间歇反应器案例研究中，验证了该方法并将其与名义非线性模型预测控制算法进行了比较。

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