Title: Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise Show Chinese title

Title: 基于灵敏度的动态性能评估用于具有高斯噪声的模型预测控制

Abstract: Economic model predictive control and tracking model predictive control are two popular advanced process control strategies used in various of fields. Nevertheless, which one should be chosen to achieve better performance in the presence of noise is uncertain when designing a control system. To this end, a sensitivity-based performance assessment approach is proposed to pre-evaluate the dynamic economic and tracking performance of them in this work. First, their controller gains around the optimal steady state are evaluated by calculating the sensitivities of corresponding constrained dynamic programming problems. Second, the controller gains are substituted into control loops to derive the propagation of process and measurement noise. Subsequently, the Taylor expansion is introduced to simplify the calculation of variance and mean of each variable. Finally, the tracking and economic performance surfaces are plotted and the performance indices are precisely calculated through integrating the objective functions and the probability density functions. Moreover, boundary moving (i.e., back off) and target moving can be pre-configured to guarantee the stability of controlled processes using the proposed approach. Extensive simulations under different cases illustrate the proposed approach can provide useful guidance on performance assessment and controller design. Show Chinese abstract

Abstract: 经济模型预测控制和跟踪模型预测控制是两种在多个领域中广泛应用的先进过程控制策略。 然而，在设计控制系统时，面对噪声情况下应选择哪一种以获得更好的性能仍不确定。 为此，本文提出了一种基于灵敏度的性能评估方法，以预先评估它们的动态经济性能和跟踪性能。 首先，通过计算相应约束动态规划问题的灵敏度，评估其在最优稳态附近的控制器增益。 其次，将控制器增益代入控制回路，以推导过程噪声和测量噪声的传播。 随后，引入泰勒展开式以简化各变量方差和均值的计算。 最后，通过积分目标函数和概率密度函数，绘制跟踪和经济性能曲面，并精确计算性能指标。 此外，可以预先配置边界移动（即后退）和目标移动，以利用所提出的方法保证受控过程的稳定性。 在不同情况下的大量仿真表明，所提出的方法可以在性能评估和控制器设计方面提供有用的指导。