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Zeitschriftenartikel zum Thema „Contrôle LPV Robuste“

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Veröffentlicht am 1. Juni 2024

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1

Xie, W. „Robust control system design for polytopic stable LPV systems“. IMA Journal of Mathematical Control and Information 20, Nr. 2 (01.06.2003): 201–16. http://dx.doi.org/10.1093/imamci/20.2.201.

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2

Hasseni, Seif-El-Islam, und Latifa Abdou. „Robust LFT-LPV H∞ Control of an Underactuated Inverted Pendulum on a Cart with Optimal Weighting Functions Selection by GA and ES“. Acta Mechanica et Automatica 14, Nr. 4 (01.12.2020): 186–97. http://dx.doi.org/10.2478/ama-2020-0027.

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Abstract This article investigates the robust stabilization and control of the inverted pendulum on a cart against disturbances, measurement noises, and parametric uncertainties by the LFT-based LPV technique (Linear-Fractional-Transformation based Linear-Parameter-Varying). To make the applying of the LPV technique possible, the LPV representation of the inverted pendulum on a cart model is developed. Besides, the underactuated constraint of this vehicle is overcome by considering both degrees of freedom (the rotational one and the translational one) in the structure. Moreover, the selection of the weighting functions that represent the desired performance is solved by two approaches of evolutionary algorithms; Genetic Algorithms (GA) and Evolutionary Strategies (ES) to find the weighting functions’ optimal parameters. To validate the proposed approach, simulations are performed and they show the effectiveness of the proposed approach to obtain robust controllers against external signals, as well as the parametric uncertainties.

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3

Bianchi, Fernando D., und Ricardo S. Sánchez-Peña. „Robust identification/invalidation in an LPV framework“. International Journal of Robust and Nonlinear Control 20, Nr. 3 (27.03.2009): 301–12. http://dx.doi.org/10.1002/rnc.1430.

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4

Pimentel, Guilherme, und Daniel Coutinho. „Controle robusto por realimentação linearizante parcial de bioreatores em modo de operação descontínua com alimentação“. Sba: Controle & Automação Sociedade Brasileira de Automatica 23, Nr. 2 (April 2012): 138–52. http://dx.doi.org/10.1590/s0103-17592012000200002.

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Uma metodologia para o projeto de controladores robustos foi desenvolvida para bioreatores operando em modo descontínuo com alimentação. Inicialmente, apresenta-se um modelo geral que descreve a dinâmica do crescimento da bactéria Escherichia Coli e da levedura Saccharomyces Cerevisiae que são atualmente os dois microorganismos mais utilizados na indústria biotecnológica. A partir do modelo dinâmico da cultura de microorganismos, busca-se projetar uma lei de controle que mantenha a concentração do produto secundário em níveis próximos a zero visando, desta forma, maximizar a produção de biomassa. Com este objetivo, a dinâmica não linear é descrita em termos de parâmetros variantes no tempo (abordagem quasi-LPV) possibilitando a utilização da formulação por desigualdades matriciais lineares para o projeto da dinâmica livre resultante da aplicação de uma lei de controle do tipo linearizante parcial. As condições propostas permitem garantir a estabilidade robusta do sistema em malha fechada frente a incertezas paramétricas, além de assegurar um certo desempenho com relação a perturbações. Para verificar o comportamento da metodologia proposta, vários testes por simulação são realizados para avaliar o comportamento da estratégia proposta em relação a trabalhos disponíveis na literatura especializada

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5

Hadian, Mohsen, Amin Ramezani und Wenjun Zhang. „Robust Model Predictive Controller Using Recurrent Neural Networks for Input–Output Linear Parameter Varying Systems“. Electronics 10, Nr. 13 (28.06.2021): 1557. http://dx.doi.org/10.3390/electronics10131557.

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This paper develops a model predictive controller (MPC) for constrained nonlinear MIMO systems subjected to bounded disturbances. A linear parameter varying (LPV) model assists MPC in dealing with nonlinear dynamics. In this study, the nonlinear process is represented by an LPV using past input–output information (LPV-IO). Two primary objectives of this study are to reduce online computational load compared with the existing literature of MPC with an LPV-IO model and to confirm the robustness of the controller in the presence of disturbance. For the first goal, a recurrent neural network (RNN) is employed to solve real-time optimization problems with lower online computation. Regarding robustness, a new control law is developed, which comprises a fixed control gain (K) and a free perturbation (C). The proposed method enjoys a shrunken conservatism owing to the finding of a larger possible terminal region and using free control moves. The strategy is examined in an alkylation of benzene process and displays outstanding performance in both setpoint tracking and disturbance rejection problems. Moreover, the superiority of RNN over three conventional optimization algorithms is underlined in terms of MSE, the average time for solving the optimization problem, and the value of the cost function.

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6

Song, Lei, und Jianying Yang. „Robust reliable tracking controller design against actuator faults for LPV systems“. Asian Journal of Control 13, Nr. 6 (02.12.2010): 1075–81. http://dx.doi.org/10.1002/asjc.286.

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7

Varrier, Sébastien, Damien Koenig und John J. Martinez. „Robust fault detection for Uncertain Unknown Inputs LPV system“. Control Engineering Practice 22 (Januar 2014): 125–34. http://dx.doi.org/10.1016/j.conengprac.2013.10.002.

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8

Varga, Andreas, und Daniel Ossmann. „LPV model-based robust diagnosis of flight actuator faults“. Control Engineering Practice 31 (Oktober 2014): 135–47. http://dx.doi.org/10.1016/j.conengprac.2013.11.004.

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9

Lee, S. M., S. C. Won, D. H. Ji und J. H. Park. „Robust model predictive control for LPV systems using relaxation matrices“. IET Control Theory & Applications 1, Nr. 6 (01.11.2007): 1567–73. http://dx.doi.org/10.1049/iet-cta:20060525.

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10

Xu, Chao, Xianqiang Yang und Miao Yu. „Robust LPV models identification approach based on shifted asymmetric Laplace distribution“. Measurement and Control 54, Nr. 9-10 (November 2021): 1336–46. http://dx.doi.org/10.1177/00202940211028904.

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This paper focuses on the robust parameters estimation algorithm of linear parameters varying (LPV) models. The classical robust identification techniques deal with the polluted training data, for example, outliers in white noise. The paper extends this robustness to both symmetric and asymmetric noise with outliers to achieve stronger robustness. Without the assumption of Gaussian white noise pollution, the paper employs asymmetric Laplace distribution to model broader noise, especially the asymmetrically distributed noise, since it is an asymmetric heavy-tailed distribution. Furthermore, the asymmetric Laplace (AL) distribution is represented as the product of Gaussian distribution and exponential distribution to decompose this complex AL distribution. Then, a shifted parameter is introduced as the regression term to connect the probabilistic models of the noise and the predict output that obeys shifted AL distribution. In this way, the posterior probability distribution of the unobserved variables could be deduced and the robust parameters estimation problem is solved in the general Expectation Maximization algorithm framework. To demonstrate the advantage of the proposed algorithm, a numerical simulation example is employed to identify the parameters of LPV models and to illustrate the convergence.

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11

Buzachero, Luiz F. S., Edvaldo Assunção, Marcelo C. M. Teixeira und Emerson R. P. da Silva. „Switched Optimized Robust Control of Uncertain LPV Systems Subject to Structural Faults“. IFAC-PapersOnLine 51, Nr. 25 (2018): 353–58. http://dx.doi.org/10.1016/j.ifacol.2018.11.132.

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12

Zhang, Xian, und Huanyu Zhu. „Robust Stability and Stabilization Criteria for Discrete Singular Time-Delay LPV Systems“. Asian Journal of Control 14, Nr. 4 (01.08.2011): 1084–94. http://dx.doi.org/10.1002/asjc.418.

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13

Ding, Baocang, Xubin Ping und Hongguang Pan. „On dynamic output feedback robust MPC for constrained quasi-LPV systems“. International Journal of Control 86, Nr. 12 (Dezember 2013): 2215–27. http://dx.doi.org/10.1080/00207179.2013.809796.

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14

Németh, Balázs, Péter Gáspár und Zoltán Szabó. „Guaranteed performances for a learning-based eco-cruise control using robust LPV method“. IFAC-PapersOnLine 54, Nr. 8 (2021): 83–88. http://dx.doi.org/10.1016/j.ifacol.2021.08.585.

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15

Biannic, Jean-Marc, Anthony Bourdelle, Hélène Evain, Sabine Moreno und Laurent Burlion. „On robust LPV-based observation of fuel slosh dynamics for attitude control design“. IFAC-PapersOnLine 52, Nr. 28 (2019): 170–75. http://dx.doi.org/10.1016/j.ifacol.2019.12.369.

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16

Hooshmandi, Kaveh, Farhad Bayat, Mohammad Reza Jahed-Motlagh und Ali Akbar Jalali. „Polynomial LPV approach to robust H ∞ control of nonlinear sampled-data systems“. International Journal of Control 93, Nr. 9 (19.11.2018): 2145–60. http://dx.doi.org/10.1080/00207179.2018.1547422.

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17

Do, Manh-Hung, Damien Koenig und Didier Theilliol. „Robust H∞ proportional-integral observer-based controller for uncertain LPV system“. Journal of the Franklin Institute 357, Nr. 4 (März 2020): 2099–130. http://dx.doi.org/10.1016/j.jfranklin.2019.11.053.

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18

Bujarbaruah, Monimoy, Ugo Rosolia, Yvonne R. Stürz, Xiaojing Zhang und Francesco Borrelli. „Robust MPC for LPV systems via a novel optimization-based constraint tightening“. Automatica 143 (September 2022): 110459. http://dx.doi.org/10.1016/j.automatica.2022.110459.

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19

Isaza-Hurtado, J. A., H. A. Botero-Castro und H. Alvarez. „Robust estimation for LPV systems in the presence of non-uniform measurements“. Automatica 115 (Mai 2020): 108901. http://dx.doi.org/10.1016/j.automatica.2020.108901.

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20

Armeni, Saverio, Alessandro Casavola und Edoardo Mosca. „Robust fault detection and isolation for LPV systems under a sensitivity constraint“. International Journal of Adaptive Control and Signal Processing 23, Nr. 1 (Januar 2009): 55–72. http://dx.doi.org/10.1002/acs.1044.

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21

Zhang, Shuang, Vicenç Puig und Sara Ifqir. „Robust Fault Detection using Set-based Approaches for LPV Systems: Application to Autonomous Vehicles“. IFAC-PapersOnLine 55, Nr. 6 (2022): 31–36. http://dx.doi.org/10.1016/j.ifacol.2022.07.101.

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22

Abbas, Hossam S., Jurre Hanema, Roland Tóth, Javad Mohammadpour und Nader Meskin. „A New Approach to Robust MPC Design for LPV Systems in Input-Output Form“. IFAC-PapersOnLine 51, Nr. 26 (2018): 112–17. http://dx.doi.org/10.1016/j.ifacol.2018.11.159.

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23

Rosa, Paulo, Gary J. Balas, Carlos Silvestre und Michael Athans. „A Synthesis Method of LTI MIMO Robust Controllers for Uncertain LPV Plants“. IEEE Transactions on Automatic Control 59, Nr. 8 (August 2014): 2234–40. http://dx.doi.org/10.1109/tac.2014.2301572.

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24

Wang, Xin, Xian Zhang und Xiaona Yang. „Delay-dependent Robust Dissipative Control for Singular LPV Systems with Multiple Input Delays“. International Journal of Control, Automation and Systems 17, Nr. 2 (18.01.2019): 327–35. http://dx.doi.org/10.1007/s12555-018-0237-0.

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25

Rotondo, Damiano, Fatiha Nejjari und Vicenç Puig. „Robust state-feedback control of uncertain LPV systems: An LMI-based approach“. Journal of the Franklin Institute 351, Nr. 5 (Mai 2014): 2781–803. http://dx.doi.org/10.1016/j.jfranklin.2014.01.018.

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26

Hooshmandi, Kaveh, Farhad Bayat, Mohammad Reza Jahed-Motlagh und AliAkbar Jalali. „Robust sampled-data control of non-linear LPV systems: time-dependent functional approach“. IET Control Theory & Applications 12, Nr. 9 (12.06.2018): 1318–31. http://dx.doi.org/10.1049/iet-cta.2017.0980.

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27

Ho, Lok Man. „Robust Residual Generator Synthesis for Uncertain LPV Systems Applied to Lateral Vehicle Dynamics“. IEEE Transactions on Control Systems Technology 27, Nr. 3 (Mai 2019): 1275–83. http://dx.doi.org/10.1109/tcst.2018.2789461.

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28

Gupta, Ankit, Manas Mejari, Paolo Falcone und Dario Piga. „Computation of parameter dependent robust invariant sets for LPV models with guaranteed performance“. Automatica 151 (Mai 2023): 110920. http://dx.doi.org/10.1016/j.automatica.2023.110920.

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29

Chen, Lejun, Ron Patton und Philippe Goupil. „Robust fault estimation using an LPV reference model: ADDSAFE benchmark case study“. Control Engineering Practice 49 (April 2016): 194–203. http://dx.doi.org/10.1016/j.conengprac.2015.12.006.

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30

Dewei Li und Yugeng Xi. „The Feedback Robust MPC for LPV Systems With Bounded Rates of Parameter Changes“. IEEE Transactions on Automatic Control 55, Nr. 2 (Februar 2010): 503–7. http://dx.doi.org/10.1109/tac.2009.2037464.

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31

Li, Dewei, Furong Gao und Yugeng Xi. „Separated design of robust model predictive control for LPV systems with periodic disturbance“. Journal of Process Control 24, Nr. 1 (Januar 2014): 250–60. http://dx.doi.org/10.1016/j.jprocont.2013.10.010.

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32

Chang, H., A. Krieger, A. Astolfi und E. N. Pistikopoulos. „Robust multi-parametric model predictive control for LPV systems with application to anaesthesia“. Journal of Process Control 24, Nr. 10 (Oktober 2014): 1538–47. http://dx.doi.org/10.1016/j.jprocont.2014.07.005.

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33

Bastos, Edson Vinicius Pontes, Vinícius Da Silva Matos und Marcelo Álvaro da Silva Macedo. „Relevância da Informação Contábil de Lucro e Fluxo de Caixa: um Estudo com Foco em Ações Ordinárias e Preferenciais“. Revista de Gestão dos Países de Língua Portuguesa 18, Nr. 2 (11.11.2019): 104. http://dx.doi.org/10.12660/rgplp.v18n2.2019.78785.

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<p>Objetiva-se verificar se o lucro líquido por ação (LPA) e o fluxo de caixa operacional por ação (FCO_A) apresentam capacidades distintas de explicar o comportamento dos preços das ações ordinárias e preferenciais. Formaram-se três grupos: o primeiro, com ambas as classes acionárias; o segundo, apenas com ações ordinárias; e o terceiro, apenas com preferenciais. Para cada grupo foram aplicados dois modelos de regressões, um usando o LPA e outro usando o FCO_A, tendo em ambos o patrimônio líquido, QTobin e o tamanho como variável de controle. Os modelos foram estimados por OLS e, posteriormente, por GMM como análise de robustez. Os resultados sugerem que o FCO é mais relevante para as ações ordinárias do que para as ações preferenciais. Já a variável LPA é significativa para ambas as classes acionárias, e seus modelos apresentaram maior capacidade explicativa para as ações preferenciais. Logo, têm-se evidências de que o lucro líquido (LL) é mais relevante para as ações preferenciais do que para as ações ordinárias.</p>

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34

Ping, Xubin, Sen Yang, Baocang Ding, Tarek Raïssi und Zhiwu Li. „A Convexity Approach to Dynamic Output Feedback Robust MPC for LPV Systems with Bounded Disturbances“. International Journal of Control, Automation and Systems 18, Nr. 6 (26.12.2019): 1378–91. http://dx.doi.org/10.1007/s12555-019-0089-2.

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35

Tasoujian, Shahin, Saeed Salavati, Matthew A. Franchek und Karolos M. Grigoriadis. „Robust delay-dependent LPV synthesis for blood pressure control with real-time Bayesian parameter estimation“. IET Control Theory & Applications 14, Nr. 10 (02.07.2020): 1334–45. http://dx.doi.org/10.1049/iet-cta.2019.0651.

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36

Heydari, Reza, und Mohammad Farrokhi. „Robust tube-based model predictive control of LPV systems subject to adjustable additive disturbance set“. Automatica 129 (Juli 2021): 109672. http://dx.doi.org/10.1016/j.automatica.2021.109672.

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37

Song, Lei, und Jianying Yang. „An improved approach to robust stability analysis and controller synthesis for LPV systems“. International Journal of Robust and Nonlinear Control 21, Nr. 13 (28.09.2010): 1574–86. http://dx.doi.org/10.1002/rnc.1655.

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38

Rotondo, Damiano, Andrea Cristofaro, Tor Arne Johansen, Fatiha Nejjari und Vicenç Puig. „Robust fault and icing diagnosis in unmanned aerial vehicles using LPV interval observers“. International Journal of Robust and Nonlinear Control 29, Nr. 16 (28.10.2018): 5456–80. http://dx.doi.org/10.1002/rnc.4381.

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39

Tan, Junbo, Feng Xu, Jun Yang, Xueqian Wang und Bin Liang. „Robust Fault Detection and Isolation of Discrete-Time LPV Systems Combining Set-theoretic UIO and Invariant Sets“. IFAC-PapersOnLine 53, Nr. 2 (2020): 724–29. http://dx.doi.org/10.1016/j.ifacol.2020.12.822.

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40

Shao, Pengyuan, Jin Wu, Chengfu Wu und Songhui Ma. „Model and robust gain‐scheduled PID control of a bio‐inspired morphing UAV based on LPV method“. Asian Journal of Control 21, Nr. 4 (Juli 2019): 1681–705. http://dx.doi.org/10.1002/asjc.2187.

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41

Morato, Marcelo M. „A robust model predictive control algorithm for input–output LPV systems using parameter extrapolation“. Journal of Process Control 128 (August 2023): 103021. http://dx.doi.org/10.1016/j.jprocont.2023.103021.

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42

López-Estrada, Francisco Ronay, Jean-Christophe Ponsart, Didier Theilliol, Youmin Zhang und Carlos-Manuel Astorga-Zaragoza. „LPV Model-Based Tracking Control and Robust Sensor Fault Diagnosis for a Quadrotor UAV“. Journal of Intelligent & Robotic Systems 84, Nr. 1-4 (06.11.2015): 163–77. http://dx.doi.org/10.1007/s10846-015-0295-y.

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43

Yassuda, Julio Yuzo, Cristiano Marcos Agulhari und Emerson Ravazzi Pires da Silva. „Sampled-data robust control of a 2-DoF helicopter modeled using a quasi-LPV framework“. Control Engineering Practice 145 (April 2024): 105870. http://dx.doi.org/10.1016/j.conengprac.2024.105870.

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44

Mattei, Massimiliano. „An LPV approach to the robust control of a class of quasi-linear propagation processes“. Journal of Process Control 14, Nr. 6 (September 2004): 651–60. http://dx.doi.org/10.1016/j.jprocont.2004.01.001.

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45

Hachem, Mohamad, Ariel M. Borrell, Olivier Sename, Hussam Atoui und Marcelo Morato. „ROS Implementation of Planning and Robust Control Strategies for Autonomous Vehicles“. Electronics 12, Nr. 17 (31.08.2023): 3680. http://dx.doi.org/10.3390/electronics12173680.

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Autonomous vehicles are rapidly emerging as a crucial sector within the automotive industry. Several companies are investing in the development and enhancement of technologies, which presents challenging problems in the context of robotics and control. In particular, this work primarily focuses on the creation of an autonomous vehicle architecture utilizing the robotic operating system (ROS2) framework, accompanied by advanced control algorithms. In order to facilitate the development and implementation of lateral vehicle dynamics controllers, a reduced-size automated car available in GIPSA-Lab is used as an experimental platform. The objective is to design robust controllers capable of achieving optimal tracking and stability. Accordingly, this problem is tackled under different robust control syntheses, considering the H∞ approach: using linear time-invariant (LTI) and linear parameter-varying (LPV) model representations. Several simulation and experimental results are included to demonstrate the efficiency of the controllers.

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46

Ping, Xu-Bin, Peng Wang und Jia-Feng Zhang. „A Multi-step Output Feedback Robust MPC Approach for LPV Systems with Bounded Parameter Changes and Disturbance“. International Journal of Control, Automation and Systems 16, Nr. 5 (13.09.2018): 2157–68. http://dx.doi.org/10.1007/s12555-017-0630-0.

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47

Heydari, Reza, und Mohammad Farrokhi. „Robust event-triggered model predictive control of polytopic LPV systems: An input-to-state stability approach“. Systems & Control Letters 163 (Mai 2022): 105202. http://dx.doi.org/10.1016/j.sysconle.2022.105202.

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48

Rizzello, Gianluca. „Robust output regulation of a class of smart actuators described by a minimum phase LPV dynamics“. Mechatronics 94 (Oktober 2023): 103003. http://dx.doi.org/10.1016/j.mechatronics.2023.103003.

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49

Wu, L., C. Wang, H. Gao und P. Shi. „Delay-dependent robust ℋ∞ and ℒ2-ℒ∞ filtering for LPV systems with both discrete and distributed delays“. IEE Proceedings - Control Theory and Applications 153, Nr. 4 (01.07.2006): 483–92. http://dx.doi.org/10.1049/ip-cta:20050296.

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50

Yang, Weilin, Jianwei Gao, Gang Feng und TieJun Zhang. „An optimal approach to output-feedback robust model predictive control of LPV systems with disturbances“. International Journal of Robust and Nonlinear Control 26, Nr. 15 (06.01.2016): 3253–73. http://dx.doi.org/10.1002/rnc.3505.

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