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High Efficiency data driven control Based on Dynamic Linearization and PIDNN With Cohen-Coon for Discrete Nonlinear Fast Time-Varying Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第2期71卷 782-786页

作者： Hao, Jun Zhang, Guoshan Zhu, Desheng Ye, Hui Jiangsu Univ Sci & Technol Coll Automat Zhenjiang 212100 Jiangsu Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China

In this brief, a high efficiency data driven control algorithm based on dynamic linearization (DL) and proportional-integral-derivative neural network (PIDNN) with Cohen-Coon (CC) is designed to achieve trajectory tracking control of discrete nonlinear fast time-varying systems (DNFTS). Firstly, CC approach is used to reliably obtain initial parameters of PIDNN and initial pseudo partial derivative (PPD) of DNFTS by exciting systems to produce input/output (I/O) data, meanwhile, DL method utilizes system I/O data to online establish a virtual data model equivalent to DNFTS. Secondly, the equivalent virtual data model generates PPD to online compensate PIDNN weights, which guarantees that PIDNN weights can be promptly tuned in the right direction. Moreover, one algorithm complexity criterion is defined to estimate proposed algorithm execution efficiency. The simulation studies indicate that proposed DL-PIDNN-CC has high execution efficiency in terms of fast convergence speed with low computational burden and has superior control performance in terms of Integral Squared Error (ISE) and Integral Absolute Error (IAE).

关键词： Heuristic algorithms data models Time-varying systems Nonlinear dynamical systems Neural networks control systems Stability criteria data driven control initial parameter settings high execution efficiency discrete nonlinear fast time-varying systems

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A data-driven control Scheme for Improving Tracking control Performance of Robot Manipulators: Experimental Studies

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2024年第8期22卷 2504-2512页

作者： Lee, Sang Deok Jung, Seul Chungnam Natl Univ Dept Mechatron Engn 99 Daehak Ro Daejeon 34134 South Korea

This article presents a data-driven control application to robot manipulation for implementing the time-delayed control (TDC) algorithm. TDC scheme uses the previous information to cancel out all the dynamics except the inertial torque in robot manipulators. The accuracy of estimating the inertia matrix plays an important role in control performance as well as the stability of TDC. Necessary information for the time-delayed control is inertia and acceleration signals. Since selecting the constant inertia matrix is simple but concerned with the poor performance, better estimation is required. Based on the input and output data of a robot manipulator, necessary models are obtained by a recursive least squares (RLS) algorithm and those models are used for estimating acceleration signals by designing a state observer (SOB). Here the models of a robot arm are decoupled, linearized, and identified by RLS algorithm and the joint acceleration signals are identified by a state observer in on-line fashion. Combining RLS, SOB, and TDC yields RST scheme for a robot manipulator to improve the tracking control performance by providing solutions for TDC problems. Tracking control performances of a mobile manipulator by the RST scheme are empirically tested.

关键词： data driven control recursive least squares robot manipulators stae observer time-delayed control

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Stochastic data-driven Predictive control: Chance-Constraint Satisfaction With Identified Multi-Step Predictors

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IEEE control SYSTEMS LETTERS 2024年 8卷 3249-3254页

作者： Balim, Haldun Carron, Andrea Zeilinger, Melanie N. Kohler, Johannes Swiss Fed Inst Technol Inst Dynam Syst & Control CH-8052 Zurich Switzerland Sch Engn & Appl Sci Harvard MA 02138 USA

We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements. Our approach leverages multi-step predictors to efficiently propagate uncertainty, ensuring chance constraint satisfaction. In particular, we present a strategy to identify multi-step predictors and quantify the associated uncertainty using a surrogate (data-driven) state space model. Then, we utilize the derived distribution to formulate a constraint tightening that ensures chance constraint satisfaction despite the parametric uncertainty. A numerical example highlights the reduced conservatism of handling parametric uncertainty in the proposed method compared to state-of-the-art solutions.

关键词： Uncertainty Kalman filters State-space methods Stochastic processes Technological innovation Predictive models Predictive control Maximum likelihood estimation Noise Noise measurement Predictive control for linear systems data driven control identification for control

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Toward eXplainabile data-driven control (XDDC): The Property-Preserving Framework

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IEEE control SYSTEMS LETTERS 2024年 8卷 478-483页

作者： Riva, Giorgio Formentin, Simone Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy

As Artificial Intelligence (AI) techniques continue to advance, the need for explainability becomes increasingly crucial, especially in sensitive or safety-critical domains. eXplainable AI (XAI) has emerged to address this need, aiming to enhance transparency in complex models. While XAI has gained traction in mainstream machine learning, its application in data-driven control systems remains relatively unexplored. This letter introduces a novel concept of explainability tailored for data-driven control, allowing one to design feedback loops from data incorporating prior knowledge and preserving important system properties. Through two case studies, we demonstrate the efficacy of this property-preserving framework in direct and indirect data-driven control system design. This letter lays the foundation for further research at the intersection of AI and data-driven control, offering insights into enhancing transparency in complex control systems.

关键词： Batteries Mathematical models Explainable AI Training data models Hybrid power systems Closed box data driven control machine learning identification for control

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Passive iFIR Filters for data-driven control

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IEEE control SYSTEMS LETTERS 2024年 8卷 1289-1294页

作者： Wang, Zixing Huo, Yongkang Forni, Fulvio Univ Cambridge Dept Engn Cambridge CB2 1PZ England

We consider the design of a new class of passive iFIR controllers given by the parallel action of an integrator and a finite impulse response filter. iFIRs are more expressive than PID controllers but retain their features and simplicity. This letter provides a model-free data-driven design for passive iFIR controllers based on virtual reference feedback tuning. Passivity is enforced through constrained optimization (three different formulations are discussed). The proposed design does not rely on large datasets or accurate plant models.

关键词： Finite impulse response filters Optimization Tuning Stability analysis PI control PD control Filters data driven control identification for control PID control LMIs

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data-driven Predictive control of CVT System for Improving Energy Efficiency of Autonomous Vehicles

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第2期72卷 1501-1514页

作者： Mola, Mirhamed Moradi Amani, Ali Jalili, Mahdi Khayyam, Hamid RMIT Univ Sch Engn Melbourne 3000 Australia

In this paper, the predictive data-driven control of Continuously Variable Transmission (CVT) systems is investigated to improve the energy efficiency of autonomous vehicles (AVs). Algorithms in AVs are expected to autonomously adapt and optimize themselves with different drive cycle conditions using the collected data during driving operation. We propose a data driven control (DDC) framework for the speed ratio of CVTs in AVs which can learn the optimum pattern from archived real data. It includes a data archiving cycle in which the required DDC input/output signals of the vehicle are recorded. A DDC framework then runs in the driving cycle which includes a rolling optimization problem. More specifically, we use a predictive DDC algorithm to learn the optimal CVT speed ratio during the driving cycle instead of using the traditional pre-defined mapping. Using this framework, the CVT controller can adapt itself to any drive cycle by appropriately collecting data during the archiving cycle. The proposed control framework shows a satisfactory performance in HWFET and NEDC drive cycles. It reduces energy consumption by 1-5% compared to the pre-defined mappings.

关键词： Autonomous vehicle continuously variable transmission system data driven control energy efficiency

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Comprehensive evaluation of a data driven control strategy: Experimental application to a pharmaceutical crystallization process

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2020年 163卷 248-261页

作者： Oner, Merve Montes, Frederico C. C. Stahlberg, Tim Stocks, Stuart M. Bajtner, Johan Eriksson Sin, Gurkan Tech Univ Denmark Proc & Syst Engn Ctr PROSYS Dept Chem & Biochem Engn DK-2800 Lyngby Denmark LEO Pharma AS Ind Pk 55 DK-2750 Ballerup Denmark

In this contribution, a data-driven control approach was developed and applied experimentally to a pharmaceutical batch cooling crystallization process. In this approach, a radial basis functions (RBF) network model was trained in real-time with experimental data (time varied temperature and chord length distribution) with two different input data update strategies. The control objective was to optimize the cooling profile with the aid of trained RBF to achieve the desired crystal population profile throughout the process. The robustness of the proposed control strategy was tested with 10 comprehensive experiments in the presence of several disturbances (initial supersaturation, impeller speed, water composition and seed size). The presented control strategy was able to easily handle all the case scenarios. In 8 cases, the experimental crystal population profile followed successfully the reference with less than 10% offset. In the remaining 2 cases, the offset was 17% that was due to the absence of the supersaturation. The proposed RBF network-driven control is a promising strategy that is easy to implement, fully-automated and relies on relatively limited data for training. Therefore, the RBF control is expected to contribute to quick process development and control, especially when there is a lack of comprehensive process understanding and historical data especially in the pharmaceutical industry. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Pharmaceutical crystallization data driven control Radial basis functions Ibuprofen PAT tools

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On the impact of regularization in data-driven predictive control 62

On the impact of regularization in data-driven predictive co...

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62nd IEEE Conference on Decision and control (CDC)

作者： Breschi, Valentina Chiuso, Alessandro Fabris, Marco Formentin, Simone Univ Padua Dept Informat Engn Padua Italy Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands Politecn Milan Dipartimento Elettron Informat & Bioingn DEIB Milan Italy

ISBN: (纸本)9798350301243

Model predictive control (MPC) is a control strategy widely used in industrial applications. However, its implementation typically requires a mathematical model of the system being controlled, which can be a time-consuming and expensive task. data-driven predictive control (DDPC) methods offer an alternative approach that does not require an explicit mathematical model, but instead optimize the control policy directly from data. In this paper, we study the impact of two different regularization penalties on the closed-loop performance of a recently introduced data-driven method called.-DDPC. Moreover, we discuss the tuning of the related coefficients in different data and noise scenarios, to provide some guidelines for the end user.

关键词： data driven control Predictive control for linear systems Uncertain systems

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Feedforward control design for shaking table by data driven control considering control input limitation 21st

Feedforward control design for shaking table by Data driven ...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Ishihara, Shinji Tahara, Koichi Hironaka, Koji Hitachi Ltd Res Dev Grp Ctr Technol Innovat Controls Ibaraki Japan Hitachi Ind Prod Ltd Machinery Control Syst Dept Ibaraki Japan

The burden of control adjustment of shaking table is increasing with the background of lack of skilled operators. With such background, there is a strong demand for a method to achieve desired control performance regardless of the operator's skill. The data-driven control is a promising approach to meet this requirement. However, even though an actual controller has an input limit, the data-driven control cannot handle it. Therefor, we proposed a novel method that considers the input limit based on data-driven prediction and an optimal problem with a penalty function. We verified the effectiveness of the proposed method through experiments using a small shaking test device. Copyright (C) 2020 The Authors.

关键词： data driven control Feedforward control Industrial control Self tuning control SISO

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A Novel Approach to Optimal Input Signal Design for data driven control 32

A Novel Approach to Optimal Input Signal Design for Data Dri...

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32nd Chinese control And Decision Conference (CCDC)

作者： Tang, Dezhi Song, Tinglun Wang, Jianhong Chery Automobile Stock Ltd Co Wuhu 241006 Anhui Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243002 Anhui Peoples R China Nanjing Univ Aeronaut & Astronaut Sch Energy & Power Engn Nanjing 210016 Jiangsu Peoples R China Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou 341000 Jiangxi Peoples R China

ISBN: (纸本)9781728158556

In the last few years, data driven control is growing very rapidly owing that it does not require identification process of the plant. The data driven controller can be designed directly by the input and output measurement data. This paper studies how to design the optimal input signal for data driven control, and proposes a new input signal optimal correlation function. The optimal input signal is designed such that the expected value of the considered control is reduced. Here some results and conditions which are tools to improve the convergence of classical prediction error method are extended into data driven control. In addition, the candidate attraction domain of the objective function is proposed, and a convergence condition is derived, which can ensure that a given set is a candidate attraction domain. In general, the results and conditions corresponding to the optimal input signal for data driven control are different from classical model based methods. Finally, simulation examples verify the effectiveness and feasibility of the proposed strategy.

关键词： data driven control Optimal Input Signal Asymptotic Variance Convergence Condition

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