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data-driven control for discrete-time piecewise affine systems✩

AUTOMATICA 2023年第1期155卷

作者： Wang, Meng Qiu, Jianbin Yan, Huaicheng Tian, Yongxiao Li, Zhichen East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Peoples R China Shanghai Univ Sch Artificial Intelligence Shanghai 200444 Peoples R China Chengdu Univ Sch Informat Sci & Engn Chengdu 610106 Peoples R China

This paper studies the problem of data-driven control for discrete-time piecewise affine (PWA) systems. Based on a sequel of sampled control inputs and states with satisfactory rank conditions, the closed loop dynamics of PWA systems can be represented and therefore the explicit information of system matrices is not needed in system stability analysis and controller synthesis processes. First, via S procedure, system stability analysis results have been obtained, and then, two novel data-driven controller design methods in the form of linear matrix inequalities and equations have been proposed such that the resulting closed loop system is asymptotically stable. For PWA systems with unknown state space partition information, an online data-driven control method has been further proposed to guarantee the asymptotic stability of the resulting closed loop systems. Finally, simulation studies of two examples have been given to validate the effectiveness of the proposed data-driven controller design approaches.& COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Piecewise affine systems data-driven control control design Convex optimization

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data-driven control of dynamic event-triggered systems with delays

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2023年第12期33卷 7071-7093页

作者： Wang, Xin Sun, Jian Berberich, Julian Wang, Gang Allgoewer, Frank Chen, Jie Beijing Inst Technol Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Beijing Peoples R China Univ Stuttgart Inst Syst Theory & Automat Control Stuttgart Germany Tongji Univ Dept Control Sci & Engn Shanghai Peoples R China Beijing Inst Technol Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

This article studies data-driven control of unknown sampled-data linear systems with communication delays under an event-triggering transmission mechanism. While event-triggered control has received much attention in the existing literature, its design and implementation typically require detailed model knowledge. Due to the difficulties in finding accurate models and the abundance of data in many practical applications, we propose a novel data-driven event-triggered control scheme for unknown, delayed systems with theoretical guarantees. To this end, as a first step, a model-based stability condition for the resulting event-triggered time-delay system is established using a novel looped-functional approach. Combining this model-based condition with the data-driven representation of linear systems, a data-based stability condition is derived. Further, methods for co-designing the controller gain and the event-triggering matrix are subsequently provided without using any prior knowledge of the system matrices. Finally, numerical examples are presented to corroborate the merits of the proposed data-driven event-triggered control schemes relative to existing results.

关键词： data-driven control event-trigger stability sampled-data systems

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data-driven control for networked systems with multiple packet dropouts

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2023年第12期33卷 7357-7373页

作者： Chen, Wenli Li, Xiaojian Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Northeastern Univ State Key Lab Synthet Automation Proc Ind Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

This paper investigates data-driven control for a class of networked control systems with multiple packet dropouts and unknown system parameters. Here, multiple packet dropouts occur randomly in the controller-to-actuator (C/A) and sensor-to-controller (S/C) channels, where successive packet dropouts are limited by known upper bounds. By introducing the summation inequality, a model-based mean-square asymptotic stability condition is established for the closed-loop networked control system with the potential to migrate to a data-driven solution. Then, the data-driven mean-square asymptotic stability condition for the closed-loop system is presented by merging the model-based stability condition with noisy data-parameterized representations. On this basis, the data-driven controller gain design approaches for combining unknown and known input gain matrices are presented in turn. Finally, two simulation examples are provided to show the applicability of the developed approaches.

关键词： data-driven control multiple packet dropouts networked control systems noisy data

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data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第5期20卷 8561-8582页

作者： Djordjevic, Vladimir Tao, Hongfeng Song, Xiaona He, Shuping Gao, Weinan Stojanovic, Vladimir Univ Kragujevac Fac Mech & Civil Engn Kraljevo 36000 Serbia Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Henan Univ Sci & Technol Sch Informat Engn Luoyang 471023 Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Sch Elect Engn & Automat Hefei 230601 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great power, high accuracy and dynamic motion. It is well known that an HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, an inability to measure some parameters and disturbances. This paper considers an event-triggered learning control problem of the HSA with unknown dynamics based on adaptive dynamic program-ming (ADP) via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system pa-rameters. Hence, the ADP-based data driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. Then, an event-based feedback strategy is introduced to the closed-loop system to save the communication resources and reduce the number of control updates. The convergence of the ADP-based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSAs.

关键词： data-driven control adaptive dynamic programming event-triggered control hydraulic servo actuator

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data-driven control of input saturated systems: a LMI-based approach 20

Data-driven control of input saturated systems: a LMI-based ...

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20th IFAC Symposium on System Identification (SYSID)

作者： Porcari, F. Breschi, V. Zaccarian, L. Formentin, S. Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands Univ Toulouse LAAS CNRS Toulouse France Univ Trento Dipartimento Ingn Ind Trento Italy

This paper addresses three complex control challenges related to input-saturated systems from a data-driven perspective. Unlike the traditional two-stage process involving system identification and model-based control, the proposed approach eliminates the need for an explicit model description. The method combines data-based closed-loop representations, Lyapunov theory, instrumental variables, and a generalized sector condition to formulate data-driven linear matrix inequalities (LMIs). These LMIs are applied to maximize the origin's basin of attraction, minimize the closed-loop reachable set with bounded disturbances, and introduce a new data-driven l(2)-gain minimization problem. Demonstrations on benchmark examples highlight the advantages and limitations of the proposed approach compared to an explicit identification of the system, emphasizing notable benefits in handling nonlinear dynamics. 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-driven control saturated systems linear matrix inequalities

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data-driven control of Networked Systems with Quantized Measurements Under Try-Once-Discard Protocols 39

Data-Driven Control of Networked Systems with Quantized Meas...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Wang, Chenxi Zou, Lei Li, Na Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Digitalized Textile & Fash Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9798350390780;9798350379228

This paper is concerned with the data-driven control problem for a networked systems with try-once-discard (TOD) protocol and quantization effect. To begin with, we establish a data-based input-output model based on the data collected in a prior experiment. A so-called TOD protocol is adopted to schedule the measurement data of sensors for the purpose of avoiding data collisions, under which only one sensor is permitted to access to the network at each transmission instant. In addition, the measurement data would be quantized before transimission. The objective of this paper is to present a design for an output-feedback controller such that the system output is exponentially ultimately bounded. By utilizing linear matrix inequalities (LMIs), a sufficient condition is guaranteed for the design of the controller. Finally, a numerical simulation is provided to illustrate the effectiveness and validity of the proposed control strategy.

关键词： data-driven control Try-Once-Discard protocol quantization effect ultimate boundedness

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data-driven control and a prey-predator model for sourcing decisions in the low-carbon intertwined supply chain

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BUSINESS STRATEGY AND THE ENVIRONMENT 2024年第8期33卷 9141-9160页

作者： El Harraki, Imad Abedin, Mohammad Zoynul Belhadi, Amine Kamble, Sachin Zkik, Karim Oudani, Mustapha Mines Rabat Rabat Morocco Swansea Univ Sch Management Dept Accounting & Finance Bay Campus Swansea SA1 8EN Wales Int Univ Rabat Rabat Business Sch BEARLab Sala Al Jadida Morocco EDHEC Business Sch Roubaix France Rennes Sch Business Rennes France Int Univ Rabat TICLab Sala Al Jadida Morocco

This paper addresses the challenges of low-carbon sourcing in intertwined supply chains by proposing a data-driven control framework and a prey-predator model for sourcing decisions. The objective is to optimize low-carbon objectives and reduce environmental impact. Existing static models fail to capture the dynamic nature of supply chain systems and overlook the ripple effects when sourcing decisions propagate throughout the interconnected network. To bridge this gap, our study develops a dynamic model that explicitly captures the bullwhip effect and leverages real-time and historical data. This model conceptualizes suppliers as prey and manufacturers and consumers as predators, employing an ecological analogy to decipher the intricate interactions and dependencies within the supply chain. Through this approach, we identify strategies to promote sustainable practices and motivate suppliers to adopt low-carbon measures. We assess two data-driven algorithms, the nonlinear auto-regressive exogenous (NARX) network and sparse identification of nonlinear dynamic systems with input variables (SINDYc). The results reveal that SINDYc outperforms prediction accuracy and control, offering significant advantages for rapid decision-making. The study highlights how shifts in market demands and regulatory pressures critically influence the strategies of chemical firms and fertilizer markets. Moreover, it discusses the economic challenges in transitioning from high carbon footprint suppliers (HCFSs) to low carbon footprint suppliers (LCFSs), exacerbated by a notable cost disparity where HCFSs are approximately 30% cheaper. By advancing beyond conventional static models, this research provides a deeper understanding of the environmental impacts and operational dynamics within supply chains, emphasizing the significant "ripple effect" where decisions at one node profoundly affect others within the chain.

关键词： data-driven control dynamic modeling low-carbon sourcing intertwined supply chains optimization algorithms prey-predator model

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data-driven control design for UAV autolanding: a pitch-only case study 3

Data-driven control design for UAV autolanding: a pitch-only...

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3rd Conference on Fully Actuated System Theory and Applications (FASTA)

作者： Tian, Qiuyang Chen, Weiwen Wang, Zelin Sun, Mingxuan Zhu, Bo Jia, Laibing Hu, Tianjiang Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen 518107 Peoples R China Sun Yat Sen Univ Sch Artificial Intelligence Zhuhai 519000 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Univ Strathclyde Dept Naval Architecture Ocean & Marine Engn Glasgow Lanark Scotland

ISBN: (纸本)9798350373707;9798350373691

This paper aims at exploring a new-type mode for autolanding control of fixed-wing unmanned aerial vehicles (UAVs). A discrete-time data-driven control scheme is tentatively proposed and developed with its pitch-only channel as a case study. Eventually, data-driven controllers inspired by attracting laws are introduced for a series of difference models. Numerical simulation for pitch-only dynamics is demonstrated to validate and compare performances of proposed DDC laws. Simulation results indicate that DDC laws can achieve the desired performance by altering data-driven models with different orders.

关键词： data-driven control characteristic model attracting laws (ALs)

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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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Meta-learning for model-reference data-driven control

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AUTOMATICA 2025年 172卷

作者： Busetto, Riccardo Breschi, Valentina Formentin, Simone Politecn Milan Dipartimento Elettron Bioingn & Informaz Via Ponzio 34-5 Milan Italy Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands

One-shot direct model-reference control design techniques, like the Virtual Reference Feedback Tuning (VRFT) approach, offer time-saving solutions for calibrating fixed-structure controllers. Nonetheless, such methods are known to be highly sensitive to the quality of data, often requiring long and costly experiments to attain acceptable closed-loop performance. These features might prevent the widespread adoption of such techniques, especially in low-data regimes. In this paper, we argue that the inherent similarity of many industrially relevant systems may come at hand, offering additional information from plants that are similar (yet not equal) to the system one aims to control. Assuming that this supplementary information is available, we propose a novel, direct design approach that leverages data from similar plants, the knowledge of controllers calibrated on them, and the corresponding closed-loop performance to enhance model-reference control design. By constructing the new controller as a combination of the available ones, our approach exploits all the available priors following a meta-learning philosophy, ensuring non-decreasing performance. An extensive numerical analysis supports our claims, highlighting the effectiveness of the proposed method in achieving performance comparable to iterative approaches, while retaining the efficiency of one-shot direct data-driven methods like VRFT. (c) 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Meta-learning data-driven control Virtual reference feedback tuning Model-reference control

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