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A comparative study of model approximation methods applied to economic MPC

CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2022年第8期100卷 1676-1702页

作者： Huang, Zhiyinan Liu, Qinyao Liu, Jinfeng Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada Xian Jiaotong Liverpool Univ Dept Mechatron & Robot Suzhou Peoples R China

Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for next-generation smart manufacturing. It has the potential to not only improve economic performance but also significantly increase computational complexity. model approximation has been a standard approach for reducing computational complexity in process control. In this work, we perform a study on three types of representative model approximation methods applied to EMPC, including model reduction based on available first-principle models (e.g., proper orthogonal decomposition), system identification based on input-output data (e.g., subspace identification) that results in an explicitly expressed mathematical model, and neural networks based on input-output data. A representative algorithm from each model approximation method is considered. Two processes that are very different in dynamic nature and complexity were selected as benchmark processes for computational complexity and economic performance comparison, namely, an alkylation process and a wastewater treatment plant. The strengths and drawbacks of each method are summarized according to the simulation results, with future research direction regarding control-oriented model approximation proposed at the end.

关键词： dynamical optimization model approximation model predictive control model reduction

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Optimal IMC-PID controller design for large-scale power systems via EDE algorithm-based model approximation method

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第1期43卷 59-77页

作者： Vasu, G. Sivakumar, M. Ramalingaraju, M. Raghu Engn Coll Dept EEE Dakamarri India Gudlavalleru Engn Coll Dept EEE Gudlavalleu Andhra Pradesh India JNTUK UCEK Dept EEE Kakinada Andhra Pradesh India

In this paper, the authors propose an optimal IMC-PID controller design for the Load Frequency Control (LFC) of large-scale power system via model approximation method. The model approximation method uses the Enhanced Differential Evolution (EDE) algorithm to determine an optimal Reduced Order model (ROM) for the considered large-scale power system by minimizing the performance measure called Integral Square Error (ISE) between their step responses. Later, the LFC design is carried out using an optimal ROM instead of processing with the large-scale power system model. Thus, this simplifies the design, reduces the computational efforts and also helps in determining the lower order controller. An optimal IMC design methodology is proposed by minimizing ISE between the actual output and the reference input responses of the large-scale power system using EDE algorithm. Further, PID controller gains are acquired by least square model matching with the optimal IMC transfer function. The proposed IMC-PID controller design allows a satisfied reference input tracking performance, robustness in disturbance rejection and improves the dynamic stability of the power system. The proposed method is validated by applying it to a single area power system of third-order SISO model and also extended to a centralized two-area thermal-thermal non-reheated power system of a seventh-order MIMO model. The simulation results and the comparison of error performance indices show the efficacy of the proposed method over the significant methods available in the literature.

关键词： Load frequency control model approximation optimal IMC-PID controller design Enhanced Differential Evolution algorithm

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model approximation and stabilization of reactive sputter processes

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JOURNAL OF PROCESS CONTROL 2019年 83卷 121-128页

作者： Woelfel, Christian Bockhorn, Dirk Awakowicz, Peter Lunze, Jan Ruhr Univ Bochum Dept Elect Engn & Informat Technol Bochum Germany

The aim of this paper is to present a control design method for nonlinear reactive sputter processes to increase the process speed and to facilitate the deposition of stoichiometric thin films. A physical model originating the fields of thin films science and vacuum science is analyzed to develop a control-oriented model, which describes the process with respect to a reactive gas as input and the pressure as output. The model parameters are identified by experiments. A control design method is proposed to stabilize the unstable operating points of the process. Experiments for the validation of the model and the controller are given. They show that reactive sputter processes can be modeled with sufficient accuracy by a first-order nonlinear differential equation and can be controlled by a linear feedback controller. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： model approximation Process control Reactive sputter process

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Reinforcement Learning and Trajectory Planning based on model approximation with Neural Networks applied to Transition Problems 21st

Reinforcement Learning and Trajectory Planning based on Mode...

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

作者： Pritzkoleit, Max Knoll, Carsten Roebenack, Klaus Tech Univ Dresden Inst Control Theory Fac Elect & Comp Engn Dresden Germany

In this paper we use a multilayer neural network to approximate the dynamics of nonlinear (mechanical) control systems. Furthermore, these neural network models are combined with offline trajectory planning, to form a model-based reinforcement learning (RL) algorithm, suitable for transition problems of nonlinear dynamical systems. We evaluate the algorithm on the swing-up of the cart-pole benchmark system and observe a significant performance gain in terms of data efficiency compared to a state-of-the-art model-free RL method (Deep Deterministic Policy Gradient (DDPG)). Additionally, we present first experimental results on a cart-triple-pole system test bench. For a simple transition problem, the proposed algorithm shows a good controller performance. Copyright (C) 2020 The Authors.

关键词： Trajectory planning Reinforcement learning Learning control Neural-network models model approximation Tracking control

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Reinforcement Learning and Trajectory Planning based on model approximation with Neural Networks applied to Transition Problems

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IFAC-PapersOnLine 2020年第2期53卷 1581-1587页

作者： Max Pritzkoleit Carsten Knoll Klaus Röbenack TU Dresden Faculty of Electrical and Computer Engineering Institute of Control Theory Dresden Germany

关键词： Trajectory planning Reinforcement learning Learning control Neural-network models model approximation Tracking control

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model approximation for Switched Genetic Regulatory Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第8期29卷 3404-3417页

作者： Xue, Mengqi Tang, Yang Wu, Ligang Qian, Feng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150001 Heilongjiang Peoples R China

The model approximation problem is studied in this paper for switched genetic regulatory networks (GRNs) with time-varying delays. We focus on constructing a reduced-order model to approximate the high-order GRNs considered under the switching signal subject to certain constraints, such that the approximation error system between the original and reduced-order systems is exponentially stable with a disturbance attenuation performance. The stability conditions and the disturbance attenuation performance are established by utilizing two integral inequality bounding techniques and the average dwell-time method for the approximation error system. Then, the solvability conditions for the reduced-order models for the GRNs are also established using the projection method. Furthermore, the model approximation problem can be transferred into a sequential minimization problem that is subject to linear matrix inequality constraints by using the cone complementarity algorithm. Finally, several examples are provided to illustrate the effectiveness and the advantages of the proposed methods.

关键词： Bounding techniques genetic regulatory networks (GRNs) model approximation switched system time-varying delays

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State estimation of wastewater treatment plants based on model approximation

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COMPUTERS & CHEMICAL ENGINEERING 2018年 111卷 79-91页

作者： Yin, Xunyuan Liu, Jinfeng Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

In this article, we consider state estimation of wastewater treatment plants based on model approximation. In particular, we consider a wastewater treatment plant described by the Benchmark Simulation model No. 1 which consists of a five-chamber reactor and a settler. We propose to use the proper orthogonal decomposition approach with re-identification of output equations to obtain a reduced-order model of the original system. Then, the reduced-order model is taken advantage of in state estimation. An approach on how to determine an appropriate minimum measurement set is also proposed based on degree of observability. A continuous-discrete extended Kalman filtering algorithm is used to design the estimator based on the reduced-order model. We show through extensive simulations under different weather conditions that the estimator based on the reduced-order model with re-identified output equations gives good state estimates of the actual process. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： State estimation Wastewater treatment plants model approximation Proper orthogonal decomposition Observability

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Research on Manipulator trajectory tracking with model approximation RBF neural network adaptive control 29

Research on Manipulator trajectory tracking with model appro...

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第29届中国控制与决策会议

作者： Jing Jiang Linlin Pan Ying Dai Long Che School of automation and electrical Engineering Shenyang Ligong University School of mechanical engineering Nanjing University of Science and Technology

ISBN: (纸本)9781509046584

According to the problem of manipulator trajectory tracking accuracy,model approximation RBF neural network adaptive control is applied in the manipulator trajectory *** this paper,integral approximation method and model block approach method are *** control law is ***,the computer simulation results show the different adaptive abilities and tracking *** block approximation method can get the short transiton *** overshoot of integral approximation method is very small.

关键词： Manipulator Trajectory tracking Neural network Adaptive control model approximation

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Reduced-order model approximation of fuzzy switched systems with pre-specified performance

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INFORMATION SCIENCES 2016年第0期370卷 538-550页

作者： Su, Xiaojie Liu, Xinxin Song, Yong-Duan Lam, Hak Keung Wang, Lei Chongqing Univ Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Univ Coll Automat Chongqing 400044 Peoples R China Kings Coll London Dept Informat London WC2R 2LS England

The reduced-order model approximation problem for discrete-time hybrid switched non-linear systems is addressed via Takagi-Sugeno (T-S) fuzzy modelling in this paper. For a high-dimension hybrid switched nonlinear system, our aim is on how to construct a reduced-dimension hybrid switched model, approximating its original high-order model well with a pre-specified system performance level. Firstly, the mean-square exponential stability analysis is provided, in which it guarantees the given weighted, H-infinity system performance level for the augmented error dynamic system by the average dwell time analysis approach and the hybrid switched Lyapunov stability theory. The solution of corresponding model reduction problem with pre-specified performance is given by using the projection Lemma. The algorithm of the reduced-order hybrid switched model parameters is designed by the cone complementary linearization technique. Finally, the advantage and effectiveness of the proposed reduced-order model approximation approach are shown by the simulation result. (C) 2016 Elsevier Inc. All rights reserved.

关键词： model reduction model approximation Reduced-order systems Switched systems Hybrid systems

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model approximation for batch flow shop scheduling with fixed batch sizes

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DISCRETE EVENT DYNAMIC SYSTEMS-THEORY AND APPLICATIONS 2015年第4期25卷 497-529页

作者： Weyerman, W. Samuel Rai, Anurag Warnick, Sean Google Mountain View CA 94043 USA MIT Lab Informat & Decis Syst Cambridge MA 02139 USA Brigham Young Univ Dept Comp Sci Informat & Decis Algorithms Labs Provo UT 84602 USA

Batch flow shops model systems that process a variety of job types using a fixed infrastructure. This model has applications in several areas including chemical manufacturing, building construction, and assembly lines. Since the throughput of such systems depends, often strongly, on the sequence in which they produce various products, scheduling these systems becomes a problem with very practical consequences. Nevertheless, optimally scheduling these systems is NP-complete. This paper demonstrates that batch flow shops can be represented as a particular kind of heap model in the max-plus algebra. These models are shown to belong to a special class of linear systems that are globally stable over finite input sequences, indicating that information about past states is forgotten in finite time. This fact motivates a new solution method to the scheduling problem by optimally solving scheduling problems on finite-memory approximations of the original system. Error in solutions for these "t-step" approximations is bounded and monotonically improving with increasing model complexity, eventually becoming zero when the complexity of the approximation reaches the complexity of the original system.

关键词： model approximation Batch flow shop Scheduling Max-plus

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