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7th IEEE International Symposium on Advanced control of Industrial Processes (AdCONIP)

作者： Tang, Jun Wen Yan, Yitao Bao, Jie Huang, Biao UNSW Sydney Sch Chem Engn Sydney NSW 2052 Australia Univ Alberta Dept Chem & Mat Engn 116 St & 85 Ave Edmonton AB T6G 2R3 Canada

ISBN: (数字)9781665471749

ISBN: (纸本)9781665471749

Many chemical processes have multi-timescale dynamics. This paper proposes a data-based control approach for these processes based on the behavioral systems theory. A data re-sampling method and an innovative multi-optimization horizon data predictive control design are developed to deal with different timescales. The optimization horizons with small to large time intervals are used to predict and optimize future steps from near to distant future and deal with the dynamics of different timescales. The time intervals in different optimization horizons are aligned and the control action based on the dynamics of different timescales are optimized simultaneously. An illustrative example is included to demonstrate the proposed approach.

关键词： data-based control multi-timescale dynamics behavioral systems theory

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A data-based Adaptive Predictive controller Design for Stabilizing Unknown Nonlinear Systems Featuring Input and Output Saturations. 10

A Data-Based Adaptive Predictive Controller Design for Stabi...

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10th RSI International Conference on Robotics and Mechatronics (ICRoM)

作者： Asadi, Yasin Mir, Mohammadreza Farsangi, Malihe Maghfoori Bijami, Ehsan Shahid Bahonar Univ Kerman Fac Engn Kerman Iran

ISBN: (纸本)9781665454520

This article proposes a data-based Adaptive Predictive control (DBAPC) scheme for a set of unknown nonlinear dynamical systems featuring Input and Output (I/O) saturations. In the beginning, a new dynamic model is provided for a discrete-time unknown nonlinear system by considering I/O saturated data. Furthermore, according to the developed model, a predictive control method is designed for stabilizing the system. Hence, Input and Output saturations are common physical constraints in industrial procedures;the stability analysis and proving the boundedness of the tracking error are provided in the presence of the limitations mentioned above. Proving the stability of the proposed controller in the presence of I/O saturations makes it more applicable than conventional methods of model free adaptive based predictive control. Simulation studies on the Load Frequency control (LFC) problem of an interconnected three-area power system and one numerical example reveal the advantage and applicability of the proposed controller.

关键词： Input saturation Output Saturations Adaptive control data-based control

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data-driven distributed MPC of dynamically coupled linear systems 25th

Data-driven distributed MPC of dynamically coupled linear sy...

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25th International Symposium on Mathematical Theory of Networks and Systems (MTNS)

作者： Koehler, Matthias Berberich, Julian Mueller, Matthias A. Allgoewer, Frank Univ Stuttgart Inst Syst Theory & Automat Control Stuttgart Germany Leibniz Univ Hannover Inst Automat Control Hannover Germany

In this paper, we present a data-driven distributed model predictive control (MPC) scheme to stabilise the origin of dynamically coupled discrete-time linear systems subject to decoupled input constraints. The local optimisation problems solved by the subsystems rely on a distributed adaptation of the Fundamental Lemma by Willems et al., allowing to parametrise system trajectories using only measured input-output data without explicit model knowledge. For the local predictions, the subsystems rely on communicated assumed trajectories of neighbours. Each subsystem guarantees a small deviation from these trajectories via a consistency constraint. We provide a theoretical analysis of the resulting non-iterative distributed MPC scheme, including proofs of recursive feasibility and (practical) stability. Finally, the approach is successfully applied to a numerical example. Copyright (C) 2022 The Authors.

关键词： data-based control predictive control distributed control large-scale systems linear systems

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data-based Sequential Design of 2x2 Multi-loop PID controllers 13th

Data-based Sequential Design of 2x2 Multi-loop PID Controlle...

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13th IFAC Symposium on Dynamics and control of Process Systems, including Biosystems (DYCOPS)

作者： Zhang, Yinan Kumar, Anikesh Yang, Xin Samavedham, Lakshminarayanan Chiu, Min-Sen Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117585 Singapore Chongqing Univ Technol Sch Chem & Chem Engn Chongqing 400054 Peoples R China

This paper introduces a sequential design method for multi-loop PID controllers under the Virtual Reference Feedback Tuning (VRFT) methodology. Simulation studies are presented to illustrate the effect of different reference models on the proposed design for two-input two-output (TITO) systems. Lastly, the proposed design is compared with benchmark designs for well-known multivariable processes. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommonsmrg/licenses/by-nc-nd 4,0)

关键词： MIMO control data-based control Sequential design VRFT

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Koopman operator dynamical models: Learning, analysis and control

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ANNUAL REVIEWS IN control 2021年 52卷 197-212页

作者： Bevanda, Petar Sosnowski, Stefan Hirche, Sandra Tech Univ Munich Dept Elect & Comp Engn Informat Oriented Control D-80333 Munich Germany

The Koopman operator allows for handling nonlinear systems through a globally linear representation. In general, the operator is infinite-dimensional - necessitating finite approximations - for which there is no overarching framework. Although there are principled ways of learning such finite approximations, they are in many instances overlooked in favor of, often ill-posed and unstructured methods. Also, Koopman operator theory has long-standing connections to known system-theoretic and dynamical system notions that are not universally recognized. Given the former and latter realities, this work aims to bridge the gap between various concepts regarding both theory and tractable realizations. Firstly, we review data-driven representations (both unstructured and structured) for Koopman operator dynamical models, categorizing various existing methodologies and highlighting their differences. Furthermore, we provide concise insight into the paradigm's relation to system-theoretic notions and analyze the prospect of using the paradigm for modeling control systems. Additionally, we outline the current challenges and comment on future perspectives.

关键词： Koopman operator Dynamical models Representation learning System analysis data-based control

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data-Driven control of Highly Interactive Systems using 3DoF Model-On-Demand MPC: Application to a MIMO CSTR ⁎

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IFAC-PapersOnLine 2024年第15期58卷 420-425页

作者： Sarasij Banerjee Owais Khan Mohamed El Mistiri Naresh N. Nandola Daniel E. Rivera Control Systems Engineering Laboratory School for Engineering of Matter Transport and Energy Arizona State University Tempe AZ 85287 USA Siemens Technology Princeton NJ 08540 USA

This paper presents a Three-Degree-of-Freedom Model Predictive control (3DoF MPC) framework based on Multi-Input-Multi-Output (MIMO) “Model-on-Demand” (MoD) estimation. MoD is a data-centric weighted regression algorithm that generates local models over adaptively varying neighborhoods of changing operating conditions. The 3DoF formulation enables individualized tuning of parameters relating to setpoint tracking and measured and unmeasured disturbance rejection. Online estimation of system dynamics using MIMO MoD and augmentation with the 3DoF MPC structure allows the generation of control laws based on efficient locally linear approximations of system nonlinearities. This paper evaluates the framework through a case study involving a nonlinear MIMO Continuous Stirred Tank Reactor (CSTR) model. The MIMO CSTR system is highly interactive, making data-driven estimation and control notably more challenging than its SISO counterpart. The generation of an informative database using modified “zippered” multisines is presented. The paper concludes with a case study demonstrating the effectiveness of 3DoF MoD MPC in achieving constrained MIMO control of reactor concentration and temperature in the presence of disturbances through a flexible and intuitive approach.

关键词： data-based control nonlinear system identification time-varying systems model predictive control process control

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data-driven design of safe control for polynomial systems

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EUROPEAN JOURNAL OF control 2024年 75卷

作者： Luppi, Alessandro Bisoffi, Andrea De Persis, Claudio Tesi, Pietro Univ Groningen ENTEG NL-9747AG Groningen Netherlands Univ Groningen JC Willems Ctr Syst & Control NL-9747AG Groningen Netherlands Politecn Milan DEIB I-20133 Milan Italy Univ Florence DINFO I-50139 Florence Italy

We consider the safe control problem of designing a robustly invariant set using only a finite set of data collected from an unknown input-affine polynomial system in continuous time. We consider input/state/state derivative data that are noisy, i.e., are corrupted by an unknown -but -bounded disturbance. We derive a data -dependent sum -of -squares program that enforces robust invariance of a set and also optimizes the size of that set while keeping it within a set of user -defined safety constraints;the solution of this program, obtained by alternation of the decision variables, directly provides a polynomial robustly invariant set and a state -feedback controller. We numerically test the design on a system of two platooning vehicles.

关键词： data-based control Robust control of nonlinear systems Set invariance Sum of squares Application of nonlinear analysis and design

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Online learning constrained model predictive controller based on double prediction

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2021年第18期31卷 8813-8829页

作者： Manzano, J. M. Munoz de la Pena, D. Calliess, J. Limon, D. Univ Seville Dept Ingn Sistemas & Automat Camino Descubrimientos S-N Seville 41092 Spain Univ Oxford Oxford Man Inst Quantitat Finance Oxford England

A data-based predictive controller is proposed, offering both robust stability guarantees and online learning capabilities. To merge these two properties in a single controller, a double-prediction approach is taken. On the one hand, a safe prediction is computed using Lipschitz interpolation on the basis of an offline identification dataset, which guarantees safety of the controlled system. On the other hand, the controller also benefits from the use of a second online learning-based prediction as measurements incrementally become available over time. Sufficient conditions for robust stability and constraint satisfaction are given. Illustrations of the approach are provided in a simulated case study.

关键词： data-based control learning-based MPC nonlinear MPC robust control

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data-Driven control Approach for Scalability Enhancements in Reconfigurable Manufacturing Systems

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IFAC-PapersOnLine 2024年第19期58卷 115-120页

作者： Abdelhak Dahmani Lyes Benyoucef Aix-Marseille University University of Toulon CNRS LIS Marseille France

Scalability is crucial for reconfigurable manufacturing systems (RMS), allowing throughput capacity adjustments based on market demand shifts. This research applies control theory to assess scalability’s impact on RMS, understanding dynamic responses to production demand variations. Strategies are explored to efficiently manage changes, maximizing manufacturing process effectiveness. Using a discrete transfer function, the study employs virtual reference feedback tuning (VRFT) with a proportional-integral-derivative (PID) control strategy. This sophisticated, data-driven approach analyzes system responses, identifying opportunities for optimization. Integrating VRFT-PID offers a nuanced perspective, enhancing understanding of RMS behavior and refining control strategies to optimize throughput capacity in response to market dynamics.

关键词： data-based control Reconfigurable manufacturing systems (RMS) Scalability control theory VRFT Discrete system

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data-predictive control of Multi-Timescale Nonlinear Processes ⁎

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IFAC-PapersOnLine 2024年第14期58卷 109-114页

作者： Jun Wen Tang Yitao Yan Jie Bao Biao Huang School of Chemical Engineering UNSW Sydney NSW 2052 Australia Department of Chemical and Materials Engineering University of Alberta 116 St. and 85 Ave. Edmonton AB Canada T6G 2R3

A novel big data-predictive control approach for nonlinear multi-timescale processes is presented in this paper. Multiple Dynamical Latent Variable Autoencoders (DLVAEs) are employed to approximate multi-timescale dynamics, utilizing timescale-based low-pass filtering and resampling of historical input-output data. The encoder in each DLVAE projects the nonlinear physical variable space onto a linear latent variable space, represented by a kernel space in behavioral system theory. During training, we not only impose kernel spaces and reconstruct data but also establish connections among latent variables from different DLVAEs at matching time-steps. Collectively, these multi-level latent variables span a wide prediction time horizon with limited (non-uniformly spaced) steps encompassing the current, near, and distant future. In online tracking control, we guide the latent variables from each DLVAE to their respective setpoints (derived from physical variable setpoints) while maintaining consistent physical variable values at matching time-steps, all within a linear framework.

关键词： data-based control nonlinear processes multi-timescale dynamics behavioral systems theory autoencoder

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