Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks
Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks
复制标题
DOI：
10.1016/j.compchemeng.2020.107174
复制
发表时间：
2021-01-06
影响因子：
4.3
通讯作者：
Mesbah, Ali
中科院分区：
文献类型：
--
作者：
Bonzanini, Angelo D.;Paulson, Joel A.;Mesbah, Ali
基于场景的模型预测控制（MPC）方法将资源引入最优控制，从而可以减少开环鲁棒MPC固有的保守性。然而，不确定性的情况下，往往产生离线使用最坏情况下的不确定性界限量化的先验，限制了控制性能的潜在收益。本文提出了一种基于学习的多级MPC（msMPC）的系统难以建模的动态和时变对象模型失配。高斯过程（GP）用于实时学习状态和输入相关的工厂模型失配，并相应地在线调整场景树。由于与将GP预测纳入最优控制问题相关的计算复杂性增加，基于学习的msMPC（LB-msMPC）律由深度神经网络（DNN）近似，该深度神经网络在线评估成本低，内存占用量小，这使其适合嵌入式应用。此外，我们提出了一种新的算法来训练基于DNN的控制器，该控制器使用对工厂模型失配的GP描述来生成闭环仿真数据，从而确保在与闭环操作最相关的状态空间区域中评估LB-msMPC法。所提出的LB-msMPC的策略证明了冷大气等离子体射流与（生物）材料加工中的应用。仿真结果表明，近似LB-msMPC策略的承诺，难以建模的系统，快速动态毫秒时间尺度上的控制。(C)2020爱思唯尔有限公司保留所有权利。
Scenario-based model predictive control (MPC) methods introduce recourse into optimal control and can thus reduce the conservativeness inherent to open-loop robust MPC. However, the uncertainty scenarios are often generated offline using worst-case uncertainty bounds quantified a priori, limiting the potential gains in control performance. This paper presents a learning-based multistage MPC (msMPC) for systems with hard-to-model dynamics and time-varying plant-model mismatch. Gaussian Processes (GP) are used to learn state- and input-dependent plant-model mismatch in real-time and accordingly adapt the scenario tree online. Due to the increased computational complexity associated with incorporating the GP predictions into the optimal control problem, the learning-based msMPC (LB-msMPC) law is approximated by a deep neural network (DNN) that is cheap-to-evaluate online and has a small memory footprint, which makes it suitable for embedded applications. In addition, we present a novel algorithm for training the DNN-based controller that uses a GP description of the plant-model mismatch to generate closed-loop simulation data, which ensures the LB-msMPC law is evaluated in regions of the state space most relevant to closed-loop operation. The proposed LB-msMPC strategy is demonstrated on a cold atmospheric plasma jet with applications in (bio)materials processing. The simulation results indicate the promise of the approximate LB-msMPC strategy for control of hard-to-model systems with fast dynamics on millisecond timescales. (C) 2020 Elsevier Ltd. All rights reserved.