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Deep DeePC: data-enabled predictive control with low or no online optimization using deep learning

AICHE JOURNAL 2025年第3期71卷

作者： Zhang, Xuewen Zhang, Kaixiang Li, Zhaojian Yin, Xunyuan Nanyang Technol Univ Sch Chem Chem Engn & Biotechnol 62 Nanyang Dr Singapore 637459 Singapore Michigan State Univ Dept Mech Engn E Lansing MI USA Nanyang Technol Univ Nanyang Environm & Water Res Inst NEWRI Environm Proc Modelling Ctr Singapore Singapore

data-enabled predictive control (DeePC) is a data-driven control algorithm that utilizes data matrices to form a non-parametric representation of the underlying system, predicting future behaviors and generating optimal control actions. DeePC typically requires solving an online optimization problem, the complexity of which is heavily influenced by the amount of data used, potentially leading to expensive online computation. In this article, we leverage deep learning to propose a highly computationally efficient DeePC approach for general nonlinear processes, referred to as Deep DeePC. Specifically, a deep neural network is employed to learn the DeePC vector operator, which is an essential component of the non-parametric representation of DeePC. This neural network is trained offline using historical open-loop input and output data of the nonlinear process. With the trained neural network, the Deep DeePC framework is formed for online control implementation. At each sampling instant, this neural network directly outputs the DeePC operator, eliminating the need for online optimization as conventional DeePC. The optimal control action is obtained based on the DeePC operator updated by the trained neural network. To address constrained scenarios, a constraint handling scheme is further proposed and integrated with the Deep DeePC to handle hard constraints during online implementation. The efficacy and superiority of the proposed Deep DeePC approach are demonstrated using two benchmark process examples.

关键词： computationally efficient controller data-driven control data-enabled predictive control nonlinear process

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Energy management of electric vehicles based on improved long short term memory network and data-enabled predictive control

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APPLIED ENERGY 2025年 384卷

作者： Chen, Bin He, Guo Hu, Lin Li, Heng Wang, Miaoben Zhang, Rui Gao, Kai Changsha Univ Sci & Technol Coll Automot & Mech Engn Changsha 410004 Peoples R China Cent South Univ Coll Elect Informat Changsha 410086 Peoples R China Changsha Univ Coll Elect Informat & Elect Engn Changsha 410022 Peoples R China

Asa popular energy management strategy (EMS) in electric vehicles with hybrid energy storage systems (HESS), model predictive control (MPC) is vulnerable to model accuracy and parameter sensitivity effects with existing parametric modeling methods. This paper proposes a novel EMS based on hierarchical data-driven predictive control. The upper layer utilizes an optimized long short-term memory (LSTM) network for trajectory prediction, enabling the acquisition of cost-effective load power demands for the lower layer. In the lower layer, a data-enabled predictive control (DeePC) is proposed for the HESS to achieve optimal power distribution between the battery and supercapacitor while minimizing battery capacity loss. Unlike conventional MPC, DeePC is based on a non-parametric model built solely from input-output data of the HESS, enabling agile handling of diverse nonlinearities and uncertainties across different tasks and environments. Comparison with nonlinear model predictive control shows that DeePC reduces the total operating cost by 22.68%, with optimization results closer to offline dynamic programming results. Furthermore, the effectiveness of the proposed DeePC method is validated through hardware-in-the-loop (HIL).

关键词： Hybrid energy storage system Energy management PSO-LSTM data-enabled predictive control

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Closed-loop Aspects of data-enabled predictive control 22

Closed-loop Aspects of Data-Enabled Predictive Control

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Dinkla, Rogier Mulders, Sebastiaan P. van Wingerden, Jan-Willem Oomen, Tom Delft Univ Technol Delft Ctr Syst & Control Mekelweg 2 NL-2628 CD Delft Netherlands Eindhoven Univ Technol Control Syst Technol Grp NL-5600 MB Eindhoven Netherlands

ISBN: (纸本)9781713872344

In recent years, the amount of data available from systems has drastically increased, motivating the use of direct data-driven control techniques that avoid the need of parametric modeling. The aim of this paper is to analyze closed-loop aspects of these approaches in the presence of noise. To analyze this, a unified formulation of several approaches, including dataenabled predictive control (DeePC) and Subspace predictive control (SPC) is obtained and the influence of noise on closed-loop predictors is analyzed. The analysis reveals potential closedloop correlation problems, which are closely related to well-known results in closed-loop system identification, and consequent control issues. A case study reveals the hazards of noise in datadriven control. Copyright (c) 2023 The Authors.

关键词： data-driven control subspace predictive control data-enabled predictive control closed loop identification instrumental variables

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Optimal control of Combined-Cycle Power Plants: A data-enabled predictive control Perspective 9th

Optimal Control of Combined-Cycle Power Plants: A Data-Enabl...

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9th IFAC Conference on Networked Systems (NECSYS)

作者： Mahdavipour, Pouya Wieland, Christoph Spliethoff, Hartmut Tech Univ Munich Inst Energy Syst Sch Engn & Design D-85748 Garching Germany

In this work, we propose an optimal control strategy as the unit control for combinedcycle power generation units. Using the same optimal control, we propose to optimize other internal control structures. data-enabled predictive control is chosen as the optimal control problem formulation, as it does not require a parametric state-space representation of the system. This bypasses the challenging and expensive-to-solve issue of parametric modeling and linearization for highly nonlinear systems. The performance of the controller is investigated in several critical operational scenarios, such as load-following for frequency control and disturbance rejection. Simulation results in Apros, which is an environment dedicated to advanced process simulation, are presented. Copyright (C) 2022 The Authors.

关键词： data-enabled predictive control Power Networks Flexibility Power Plant control Frequency control Load Cycling

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Closed-loop Aspects of data-enabled predictive control

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IFAC-PapersOnLine 2023年第2期56卷 1388-1393页

作者： Rogier Dinkla Sebastiaan P. Mulders Jan-Willem van Wingerden Tom Oomen Delft University of Technology Delft Center for Systems and Control Mekelweg 2 2628 CD Delft The Netherlands Eindhoven University of Technology Control Systems Technology group De Zaale 5600 MB Eindhoven The Netherlands.

In recent years, the amount of data available from systems has drastically increased, motivating the use of direct data-driven control techniques that avoid the need of parametric modeling. The aim of this paper is to analyze closed-loop aspects of these approaches in the presence of noise. To analyze this, a unified formulation of several approaches, including data-enabled predictive control (DeePC) and Subspace predictive control (SPC) is obtained and the influence of noise on closed-loop predictors is analyzed. The analysis reveals potential closed-loop correlation problems, which are closely related to well-known results in closed-loop system identification, and consequent control issues. A case study reveals the hazards of noise in data-driven control.

关键词： data-driven control subspace predictive control data-enabled predictive control closed loop identification instrumental variables

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A data-enabled predictive control Method for Frequency Regulation of Power Systems 11

A Data-Enabled Predictive Control Method for Frequency Regul...

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11th IEEE-PES Innovative Smart Grid Technologies Europe (IEEE-PES ISGT Europe)

作者： Zhao, Yunzheng Liu, Tao Hill, David J. Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9781665448758

In this paper, we propose a modified data-enabled predictive control (DeePC) algorithm to solve load frequency control (LFC) problem of power systems. Compared with the existing DeePC algorithm, which is based on behavioral system theory, the following three aspects of modifications are made based on the characteristics of LFC problem of power systems with high penetration of renewable energy sources. First, the external input signal, i.e., the net load demand, to the system is considered also as the control input signal so that predictive control can be achieved for LFC only using input/output data. Second, the l(2) regularization term and slack variables are added on the DeePC algorithm to address the uncertainty of net load demand. Third, the mechanical power input of the generator is regarded as an output of the power system model so that generation rate constraints (GRC) can be dealt with by making some constraints on the output. By applying the modified DeePC algorithm, effective control for LFC can be achieved in a model-free and receding horizon control framework. Simulation results on a power system with two control areas demonstrate the effectiveness of the DeePC based method.

关键词： data-enabled predictive control load frequency control behavioral system theory

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Optimal control of Combined-Cycle Power Plants: A data-enabled predictive control Perspective

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IFAC-PapersOnLine 2022年第13期55卷 91-96页

作者： Pouya Mahdavipour Christoph Wieland Hartmut Spliethoff Institute for Energy Systems School of Engineering and Design Technical University of Munich Boltzmannstraße 15 85748 Garching Germany

In this work, we propose an optimal control strategy as the unit control for combined-cycle power generation units. Using the same optimal control, we propose to optimize other internal control structures. data-enabled predictive control is chosen as the optimal control problem formulation, as it does not require a parametric state-space representation of the system. This bypasses the challenging and expensive-to-solve issue of parametric modeling and linearization for highly nonlinear systems. The performance of the controller is investigated in several critical operational scenarios, such as load-following for frequency control and disturbance rejection. Simulation results in Apros®, which is an environment dedicated to advanced process simulation, are presented.

关键词： data-enabled predictive control Power Networks Flexibility Power Plant control Frequency control Load Cycling

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Efficient data-driven predictive control of nonlinear systems: A review and perspectives

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DIGITAL CHEMICAL ENGINEERING 2025年 14卷

作者： Li, Xiaojie Yan, Mingxue Zhang, Xuewen Han, Minghao Law, Adrian Wing-Keung Yin, Xunyuan Nanyang Technol Univ Sch Chem Chem Engn & Biotechnol 62 Nanyang Dr Singapore 637459 Singapore Nanyang Technol Univ Nanyang Environm & Water Res Inst NEWRI Environm Proc Modelling Ctr 1 CleanTech Loop Singapore 637141 Singapore Natl Univ Singapore Dept Civil & Environm Engn 1 Engn Dr 2 Singapore 117576 Singapore

Model predictive control (MPC) has become a key tool for optimizing real-time operations in industrial systems and processes, particularly to enhance performance, safety, and resilience. However, the growing complexity and nonlinearity of modern industrial systems present significant challenges for both first-principles modeling and real-time implementation of typical non-convex optimization associated with conventional MPC designs based on nonlinear models. In this review, we aim to provide an overview of current data- driven predictive control methods that have attributes of being computationally efficient as well as having the distinctive potential to address the above two challenges simultaneously. We focus particularly on two promising frameworks: (1) Koopman-based model predictive control, and (2) data-enabled predictive control, both of which are capable of formulating the optimization problem into a convex form even in the presence of strong nonlinearity in the underlying system. Additionally, we provide an outlook on the potential applications of these methods and briefly discuss their future directions across various industrial sectors.

关键词： Model predictive control data-driven control Koopman modeling data-enabled predictive control

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Fast and memory-efficient optimization for large-scale data-driven predictive control 4

Fast and memory-efficient optimization for large-scale data-...

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4th IFAC Conference on Modelling, Identification and control of Nonlinear Systems (MICNON)

作者： Schmitz, Philipp Schaller, Manuel Voigt, Matthias Worthmann, Karl Tech Univ Ilmenau Inst Math Ilmenau Germany UniDistance Suisse Fac Math & Comp Sci Brig Switzerland

Recently, data-enabled predictive control (DeePC) schemes based on Willems' fundamental lemma have attracted considerable attention. At the core are computations using Hankel-like matrices and their connection to the concept of persistency of excitation. We propose an iterative solver for the underlying data-driven optimal control problems resulting from linear discrete-time systems. To this end, we apply factorizations based on the discrete Fourier transform of the Hankel-like matrices, which enable fast and memory-efficient computations. To take advantage of this factorization in an optimal control solver and to reduce the effect of inherent bad conditioning of the Hankel-like matrices, we propose an augmented Lagrangian BFGS-method. We illustrate the performance of our method by means of a numerical study. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： data-enabled predictive control data-driven control Hankel matrices Toeplitz matrices DFT large-scale systems

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data-Driven control Strategies for Rotary Wing Aerial Vehicles 6th

Data-Driven Control Strategies for Rotary Wing Aerial Vehicl...

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6th Iberian Robotics Conference (Robot)

作者： Caeiro, Simao Guerreiro, Bruno J. Cunha, Rita Univ Lisbon Inst Syst & Robot ISR LARSYS Inst Super Tecn P-1049001 Lisbon Portugal NOVA Univ Lisbon NOVA Sch Sci & Technol UNINOVA CTS LASI P-2829516 Caparica Portugal

ISBN: (纸本)9783031591662;9783031591679

To evaluate the performance of data-driven control methods applied to Unmanned Aerial Vehicles (UAVs), this work addresses the implementation of these strategies, particularly the data-enabled predictive control (DeePC) algorithm. This strategy computes optimal controls for unknown systems through real-time output feedback using a receding horizon implementation. Moreover, this research investigates the influence of different hyperparameters on the DeePC's performance and conducts a realistic comparison between this method and two model-based control approaches: Linear Quadratic Regulator (LQR) and Model predictive control (MPC). The simulation results validate the applicability of DeePC algorithm and highlight its superior robustness to system degradation and yaw calibration errors. However, it is less suitable for complex nonlinear systems subject to aggressive trajectories.

关键词： data-driven control data-enabled predictive control Unmanned Aerial Vehicles Performance Evaluation

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