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Aperiodic data-driven model predictive control With Feasibility and Stability Guarantees

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 14461-14473页

作者： Wang, Pengbiao Ren, Xuemei Zheng, Dongdong Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

This paper proposes an event-triggered data-driven model predictive control method with terminal ingredients for unknown linear time-invariant (LTI) systems under the input constraints. Specifically, the proposed method utilizes an implicit model description based on behavioral systems theory as well as input-output measurements, without requiring prior system identification steps. We explicitly design terminal ingredients: the terminal data-driven controller, terminal weight matrix and terminal region. To reduce computational costs, we develop an event-triggered scheme to activate the optimization problem only when necessary, rather than periodically. Moreover, a terminal data-driven controller is employed in the terminal region to avoid solving the optimization problem, further reducing computational requirements. We prove that the proposed method guarantees recursive feasibility and closed-loop stability under sufficiently small channel noise. Finally, the effectiveness of our method is verified by simulations. Note to Practitioners-model predictive control (MPC) is a widely-used advanced control methodology that relies on explicit system models. This requirement limits its applicability to unknown systems, such as unknown LTI systems. data-driven MPC offers a promising alternative by bypassing the need for explicit knowledge of system models, and it relies solely on initial measurement data and online input-output measurements to make control decisions. The existing data-driven MPC methods solve the optimization problem in a periodic manner, resulting in significant computational burden. Event-triggered scheme can alleviate the computational burden by reducing the frequency of solving the optimization problem. However, designing such a scheme typically requires knowledge of the system model, which poses a challenge for unknown systems. To address this challenge, this paper develops an event-triggered data-driven MPC approach for unknown LTI systems under input c

关键词： Optimization Event detection Linear systems Noise Computational modeling Observability Trajectory System identification predictive models predictive control data-driven model predictive control event-triggered scheme terminal ingredients unknown linear time-invariant systems

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data-driven model predictive control of DC-to-DC Buck-Boost Converter

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IEEE ACCESS 2021年 9卷 101902-101915页

作者： Prag, Krupa Woolway, Matthew Celik, Turgay Univ Witwatersrand Sch Comp Sci & Appl Math ZA-2000 Johannesburg South Africa Imperial Coll London Dept Math London SW7 2BX England Univ Johannesburg Fac Engn & Built Environm ZA-2000 Johannesburg South Africa Univ Witwatersrand Sch Elect & Informat Engn ZA-2000 Johannesburg South Africa Univ Witwatersrand Wits Inst Data Sci ZA-2000 Johannesburg South Africa

The data-driven model predictive control (DDMPC) scheme is proposed to obtain fast convergence to a desired reference and to be utilised to mitigate the destabilising effects that a DC-to-DC buck-boost converter (BBC) with an active load experiences. The DDMPC strategy uses the observed state to derive an optimal control policy using a reinforcement learning (RL) algorithm. The employed Proximal Policy Optimisation (PPO) algorithm's performance is benchmarked against the PI controller. From the simulated results obtained using the MATLAB Simulink solver, the most robust methods for short settling time and stability were the hybrid methods. These methods take advantage of the short settling time provided by the PPO algorithm and the stability provided by the PI controller or the filtering mechanism over the transient time. The source code for this study is available on GitHub to support reproducible research in industrial electronics society.

关键词： Inductors Voltage control Switching circuits MOSFET Load modeling Capacitors Resistance Adaptive control data-driven model predictive control DC-to-DC buck-boost converter proximal policy optimisation reinforcement learning

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Identification for control approach to data-driven model predictive control

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INTERNATIONAL JOURNAL OF AUTOMATION AND control 2024年第3期18卷 281-301页

作者： Zakeri, Yadollah Sheikholeslam, Farid Haeri, Mohammad Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran Sharif Univ Technol Dept Elect Engn Tehran 111554363 Iran

data-driven model predictive control has become an attractive research subject in recent years. There are two main approaches to design data-driven model predictive control;direct methods in which controller is identified directly, and indirect ones where controller is designed based on an identified model of the plant. In this paper a new method for direct and offline identification of predictive controller is developed. Here, data-driven MPC design problem is reduced to basic model reference data-driven control problem which is more recognised, and can be analysed and designed by existing methods for LTI systems. The required tuning methods are adapted and four algorithms using modified correlation-based and virtual reference feedback tunings are developed. In comparison with other works, the proposed method can be used for both performance-oriented and model reference criteria, is direct, offline, and identifiable. Meanwhile, stability, and feasibility of the proposed algorithms can be guaranteed and certified using established analysis and synthesis methods for data-driven control.

关键词： data-driven control direct data-driven control data-driven model predictive control identification for control

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Real long-term performance evaluation of an improved office building operation involving a data-driven model predictive control

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ENERGY AND BUILDINGS 2025年 338卷

作者： Klanatsky, Peter Veynandt, Francois Heschl, Christian Stelzer, Roman Zogas, Panagiotis Siokas, Georgios Balomenos, Athanasios Hsch Burgenland Univ Appl Sci Campus Pinkafeld Campus 1 A-7000 Eisenstadt Austria Forsch Burgenland GmbH Standort Pinkafeld Campus 1 A-7000 Eisenstadt Austria LIBRA AI Technol PC Leof Andrea Syggrou 106 Athens 11741 Greece

data-driven model predictive control (DMPC) strategies, coupled with holistically optimized HVAC system control, represent a promising approach to achieve climate targets through significant reductions in building energy consumption and associated emissions. To validate this potential in a real-world environment, a comprehensive optimization study was conducted on an office building serving as a living laboratory. Through systematic analysis of historical operational data, multiple Energy Conservation Measures (ECMs) were identified and implemented. The cornerstone of these improvements was the development and deployment of a centralized adaptive DMPC system, which was operated and evaluated over a full year. The implemented system employs reduced-order state-space (resistance-capacitance) models for each building zone, complemented by simplified representations of storage tanks and heat pump dynamics. By integrating weather forecasting with a 24-hour prediction horizon into the control strategy, the DMPC simultaneously manages heating or cooling loads and shading blinds to optimize solar gain utilization. This integrated approach to controlling the primary heat fluxes enables advanced load shifting and peak shaving capabilities through automated management. The implementation yielded substantial technological advantages in HVAC operation: the DMPC algorithm's effective peak shaving significantly reduced maximum load requirements, enabling the complete elimination of the gas boiler. This reduction in peak demands made it possible for the heat pump to operate efficiently, maintaining full operational capabilities with just a small-sized ground source heat exchanger, instead of the conventional air source configuration. This demonstrates how the DMPC system successfully leveraged the building's demand-side flexibility potential, yielding improved operational economics and extended equipment service life. When compared to a baseline year of operation utilizing rule-bas

关键词： data-driven model predictive control Reduced grey-box model Glass facade model External shading model Thermally activated building structures Living laboratory experiment Long term demonstration test Optimal energy control Demand-side flexibility

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Real-life data-driven model predictive control for building energy systems comparing different machine learning models

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ENERGY AND BUILDINGS 2024年 305卷

作者： Stoffel, Phillip Berktold, Max Mueller, Dirk Rhein Westfal TH Aachen Inst Energy Efficient Bldg & Indoor Climate E ON Energy Res Ctr Mathieustr 10 D-52074 Aachen Germany

By considering forecasts and exploiting storage effects, model predictive control can achieve significant energy and cost savings in the building sector. However, due to the high individual modeling effort, model predictive control lacks practical applicability. For that reason, data -driven process models, approximating the system behavior based on measurements, have become increasingly popular in recent years. Still, scientific literature lacks consent about the most promising model types and efficient workflows to integrate different machine learning models into a model predictive controller. With this work, we present a workflow to provide efficient model predictive controllers based on measurement data automatically. The main idea is to translate different machine learning models into optimization syntax to enable efficient optimization with full access to gradients. We currently consider artificial neural networks, gaussian process regression, and simple linear regression process models. We use a generic model ontology to automatize the controller generation further and test the methodology on two real -life use cases. The first use case is the application of five office rooms with smart thermostat valves. The second use case is a test hall with an air handling unit and a concrete core activation. Using only two days of initial training data, we deploy controllers based on the different model types for six weeks in the offices and apply online learning to improve the models continuously. We observe only minor differences in controller performance despite the artificial neural networks showing the highest prediction accuracy. The second use case shows that the simple linear models require less controller tuning effort. Thus, for practical applications, we recommend linear regression models.

关键词： data-driven model predictive control Building energy systems Artificial neural networks Gaussian process regression Cloud platform

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Safe operation of online learning data driven model predictive control of building energy systems

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Energy and AI 2023年第4期14卷 536-549页

作者： Phillip Stoffel Patrick Henkel Martin Ratz Alexander Kumpel Dirk Muller RWTH Aachen University E.ON Energy Research CenterInstitute for Energy Efficient Buildings and Indoor ClimateMathieustraße 10Aachen52074Germany

model predictive control is a promising approach to reduce the CO 2 emissions in the building ***,the vast modeling effort hampers the widescale practical ***,data-driven process models,like artificial neural networks,are well-suited to automatize the ***,the underlying data set strongly determines the quality and reliability of artificial neural *** general,the validity domain of a machine learning model is limited to the data that was used to train *** based on system states outside that domain,so-called extrapolations,are unreliable and can negatively influence the control *** present a safe operation approach combined with online learning to deal with extrapolation in data-driven model predictive ***,the k-nearest neighbor algorithm is used to detect extrapolation to switch to a robust fallback *** continuously retraining the artificial neural networks during operation,we successively increase the validity domain of the artificial neural networks and the control *** apply the approach to control a building energy system provided by the BOPTEST *** compare controllers based on two data sets,one with extensive system excitation and one with baseline *** system is controlled to a fixed temperature set point in baseline ***,the artificial neural networks trained on this data set tend to extrapolate in other operating *** show that safe operation in combination with online learning significantly improves performance.

关键词： data-driven model predictive control Online learning Novelty detection Artificial neural networks Building energy systems

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Driver-Centric data-driven model predictive Vehicular Platoon With Longitudinal-Lateral Dynamics

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 13961-13975页

作者： Wu, Yanhong Zuo, Zhiqiang Wang, Yijing Han, Qiaoni Li, Ji Xu, Hongming Tianjin Univ Sch Elect & Informat Engn Tianjin Key Lab Intelligent Unmanned Swarm Technol Tianjin 300072 Peoples R China Univ Birmingham Dept Mech Engn Birmingham B15 2TT England

This paper proposes a driver-centric data-driven model predictive control (DDMPC) strategy to improve driving comfort while maintaining driving safety of vehicular platoon. This strategy combines a data-driven model predictive controller and the driver-centric driving policy. The data-driven platoon model involving longitudinal-lateral dynamics is established with subspace identification to alleviate the adverse effects of uncertain dynamics. Then, a subspace predictor-based distributed data-driven model predictive controller is developed for vehicular platoon. To overcome the cutting-corner phenomenon on curved roads, the reference point is shifted from the preceding vehicle to an optimal corridor point behind it. In this way, a driver-centric driving policy is designed with a flexible spacing and soft control constraints to balance driving safety and driving comfort in terms of different driving styles. Finally, several experiments with sixty drivers are carried out on a self-developed vehicular platoon platform. The experimental results demonstrate the effectiveness of the proposed DDMPC strategy.

关键词： Vehicular platoon driver-centric data-driven model predictive control longitudinal-lateral dynamics

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data-driven Switched Affine modeling for model predictive control

Data-driven Switched Affine Modeling for Model Predictive Co...

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6th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS) / Federated Logic Conferences (FLOC)

作者： Smarra, Francesco Jain, Achin Mangharam, Rahul D'Innocenzo, Alessandro Univ Aquila Dept Informat Engn Comp Sci & Math Via Vetoio I-67100 Laquila Italy Univ Penn Dept Elect & Syst Engn 200 South 33rd St Philadelphia PA 19104 USA

model predictive control (MPC) is a well consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict the systems behavior over a predictive horizon. However, building physics-based models for large scale systems, such as buildings and process control, can be cost and time prohibitive. To overcome this problem we propose in this paper a methodology to exploit machine learning techniques (i.e. regression trees and random forests) in order to build a state-space switched affine dynamical model of a large scale system only using historical data. Finite Receding Horizon control (RHC) setup using control-oriented data-driven models based on regression trees and random forests is presented as well. A comparison with an optimal MPC benchmark and a related methodology is provided on an energy management system to show the performance of the proposed modeling framework. Simulation results show that the proposed approach is very close to the optimum and provides better performance with respect to the related methodology in terms of cost function optimization. (C) 2018, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： data-driven modeling data-driven model predictive control machine learning switched systems

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A data-driven MPC approach for virtually coupled train set with non-analytic safety distance

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2025年 174卷

作者： Luo, Xiaolin Wang, Dongming Tang, Tao Liu, Hongjie Beijing Jiaotong Univ State Key Lab Adv Rail Autonomous Operat Beijing 100044 Peoples R China Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China Beijing Jiaotong Univ Natl Engn Res Ctr Rail Transportat Operat & Contro Beijing 100044 Peoples R China

Resorting to the emerging virtual coupling technology, multiple train units can operate as a virtually coupled train set (VCTS) to improve the flexibility and efficiency of train operations. To strictly guarantee collision avoidance, the space-time separation principle should be employed, where a non-analytic safety distance is used to safely separate units among VCTS. Thus, the formation control of VCTS is challenging, since it lacks analytic models to tune controllers with tracking accuracy and computational efficiency. To solve this problem, this paper proposes a data-driven model predictive control (DDMPC) approach. Based on a database with previously measured VCTS trajectories, we present a linear data-driven model to describe the non- analytic VCTS formation, such that the controller of DDMPC is yielded by solving a quadratic programming problem in a computationally efficient way. Next, to improve tracking accuracy, we optimize the modeling accuracy in the cost function of DDMPC, and bound the uncertainties from data-driven modeling and coupled states of VCTS. Furthermore, sufficient conditions are derived to guarantee constraint satisfaction and stability for VCTS. Finally, the advantages of the proposed DDMPC approach are demonstrated by comparing with several approaches in tracking accuracy and computational efficiency.

关键词： Virtually coupled train set Space-time separation Non-analytic model data-driven model predictive control

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Identifying the validity domain of machine learning models in building energy systems

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Energy and AI 2024年第1期15卷 328-341页

作者： Martin Rätz Patrick Henkel Phillip Stoffel Rita Streblow Dirk Müller RWTH Aachen University E.ON Energy Research CenterInstitute for Energy Efficient Buildings and Indoor ClimateMathieustraβe 10Aachen52074Germany

The building sector significantly contributes to climate *** improve its carbon footprint,applications like model predictive control and predictive maintenance rely on system ***,the high modeling effort hinders practical *** learning models can significantly reduce this modeling *** ensure a machine learning model’s reliability in all operating states,it is essential to know its validity *** states outside the validity domain might lead to extrapolation,resulting in unpredictable *** paper addresses the challenge of identifying extrapolation in data-driven building energy system models and aims to raise knowledge about *** that,a novel approach is proposed that calibrates novelty detection algorithms towards the machine learning *** novelty detection algorithms are identified through a literature review and a benchmark test with 15 candidates.A subset of five algorithms is then evaluated on building energy ***,on two-dimensional data,displaying the results with a novel visualization *** on more complex multi-dimensional use *** methodology performs well,and the validity domain could be *** visualization allows for a profound analysis and an improved understanding of the fundamental effects behind a machine learning model’s validity domain and the extrapolation regimes.

关键词： Extrapolation detection Validity domain Novelty detection Machine learning Artificial neural network data-driven model predictive control Building energy systems

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