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Accelerating nonlinear model predictive control through machine learning
Journal of Process Control ( IF 4.2 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.jprocont.2020.06.012
Yannic Vaupel , Nils C. Hamacher , Adrian Caspari , Adel Mhamdi , Ioannis G. Kevrekidis , Alexander Mitsos

Abstract The high computational requirements of nonlinear model predictive control (NMPC) are a long-standing issue and, among other methods, learning the control policy with machine learning (ML) methods has been proposed in order to improve computational tractability. However, these methods typically do not explicitly consider constraint satisfaction. We propose two methods based on learning the optimal control policy by an artificial neural network (ANN) and using this for initialization to accelerate computations while meeting constraints and achieving good objective function value. In the first, the ANN prediction serves as the initial guess for the solution of the optimal control problem (OCP) solved in NMPC. In the second, the ANN prediction is improved by solving a single quadratic program (QP). We compare the performance of the two proposed strategies against two benchmarks representing the extreme cases of (i) solving the NMPC problem to convergence using the shift-initialization strategy and (ii) implementing the controls predicted by the ANN prediction without further correction to reduce the computational delay. We find that the proposed ANN initialization strategy mostly results in the same control policy as the shift-initialization strategy. The computational times are on average ∼ 45% longer but the maximum time is ∼ 42% smaller and the distribution is tighter, thus more predictable. The proposed QP-based method yields a good compromise between finding the optimal control policy and solution time. Closed-loop infeasibilities are negligible and the objective function is typically greatly improved as compared to benchmark (ii). The computational time required for the necessary second-order sensitivity integration is typically an order of magnitude smaller than for solving the NMPC problem to convergence.

中文翻译:

通过机器学习加速非线性模型预测控制

摘要 非线性模型预测控制 (NMPC) 的高计算要求是一个长期存在的问题,除其他方法外,已提出使用机器学习 (ML) 方法学习控制策略以提高计算易处理性。然而,这些方法通常不明确考虑约束满足。我们提出了两种基于通过人工神经网络 (ANN) 学习最优控制策略并将其用于初始化以加速计算同时满足约束并实现良好目标函数值的方法。首先,ANN 预测作为在 NMPC 中求解的最优控制问题 (OCP) 的初始猜测。在第二种情况下,通过求解单个二次程序 (QP) 来改进 ANN 预测。我们将两种提议的策略的性能与代表以下极端情况的两个基准进行比较:(i)使用移位初始化策略解决 NMPC 问题以收敛;(ii)实施 ANN 预测预测的控制,无需进一步校正以减少计算延迟。我们发现所提出的 ANN 初始化策略主要导致与移位初始化策略相同的控制策略。计算时间平均长约 45%,但最大时间缩短约 42%,分布更紧密,因此更可预测。所提出的基于 QP 的方法在寻找最佳控制策略和求解时间之间取得了很好的折衷。与基准 (ii) 相比,闭环不可行性可以忽略不计,并且目标函数通常有很大改进。
更新日期:2020-08-01
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