Relevant bibliographies by topics / Non minimum phase plant

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Non minimum phase plant'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Non minimum phase plant"

1

Antić, D., and S. Dimitrijević. "Non-minimum phase plant control using fuzzy sliding mode." Electronics Letters 34, no. 11 (1998): 1156. http://dx.doi.org/10.1049/el:19980827.

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Freeman, C., P. Lewin, and E. Rogers. "Phase-Lead Based Iterative Learning Control Implemented on a Non-Minimum Phase Plant." IFAC Proceedings Volumes 37, no. 14 (September 2004): 187–92. http://dx.doi.org/10.1016/s1474-6670(17)31102-3.

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TSAI, TE-PING, and TE-SHING WANG. "Optimal design of non-minimum-phase control systems with large plant uncertainty." International Journal of Control 45, no. 6 (June 1987): 2147–59. http://dx.doi.org/10.1080/00207178708933872.

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Mai, Nghia Thi, Kou Yamada, Takayuki Moki, Takaaki Hagiwara, and Fuminori Kanno. "Study on the Model Feedback Control System for a Class of Non-Minimum Phase Systems." Key Engineering Materials 497 (December 2011): 234–45. http://dx.doi.org/10.4028/www.scientific.net/kem.497.234.

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In the present paper, we examine model feedback control systems (MFCSs). Because MFCSis simple, the MFCS has been applied in many applications such as the trajectory control of robotmanipulators, serially connected water tanks, etc. The control structure of the MFCS is limited, butYamada and Moki reported about whether or not MFCS can represent all of the stabilizing controllersof a minimum phase plant. However, no research has been reported whether or not MFCS can representall of the stabilizing controllers of a non-minimum phase plant. The purpose of the present paper isto give a solution to the question as to whether or not all of the stabilizing controllers for a plantare expressible in the MFCS structure. The relation between MFCS and the parameterization of allstabilizing controllers for a class of non-minimum phase plants is shown. A simple design method tospecify control characteristics is also presented.
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BAKHTIAR, T. "PERFORMANCE LIMITATION OF SYSTEMS UNDER SIGNAL-TO-NOISE CONSTRAINED CHANNEL." Journal of Mathematics and Its Applications 5, no. 2 (December 1, 2006): 1. http://dx.doi.org/10.29244/jmap.5.2.1-12.

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This paper re-discusses [1] and [11], where the prob- lems of feedback stabilization over a signal-to-noise ratio (SNR) constrained channel are studied. The first paper considers both continuous and discrete-time minimum phase systems, while the second extends the results to non-minimum phase ones and pro- poses a linear time-varying feedback strategies to eliminate the effect of non-minimum phase zeros in SNR limited stabilization. In general, the limitations on the ability to stabilize a plant over an SNR constrained channel are imposed mainly by unstable poles and non-minimum phase zeros of the plant.
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Murakami, Iwanori, Nghia Thi Mai, Kou Yamada, Takaaki Hagiwara, Yoshinori Ando, and Hideharu Yamamoto. "A Design Method for Modified Smith Predictors for Non-Minimum-Phase Time-Delay Plants with Multiple Feedback-Connected Time-Delays." Applied Mechanics and Materials 36 (October 2010): 253–62. http://dx.doi.org/10.4028/www.scientific.net/amm.36.253.

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In this paper, we examine a design method for modified Smith predictors for non-minimum-phase time-delay plants with multiple feedback-connected time-delays. The Smith predictor is proposed by Smith to overcome time-delay and known as an effective time-delay compensator for a plant with large time-delay. The Smith predictor by Smith cannot be used for plants having an integral mode, because a step disturbance will result in a steady state error. Several papers considered the problem to design modified Smith predictors for unstable plants. However, no paper examines a design method for modified Smith predictors for non-minimum-phase time-delay plants with multiple feedback-connected time-delays. In this paper, we examine a design method for modified Smith predictors for non-minimum-phase time-delay plants with multiple feedback-connected time-delays.
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Hagiwara, Takaaki, Kou Yamada, Tatsuya Hoshikawa, Yuuto Karasawa, and Fuminori Kanno. "A Study on the Class of Model Feedback Controllers Formultiple-Input/Multiple-Output Non-Minimum Phase System." Key Engineering Materials 534 (January 2013): 182–94. http://dx.doi.org/10.4028/www.scientific.net/kem.534.182.

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In the present paper, we examine model feedback control systems (MFCSs). BecauseMFCS is simple, the MFCS has been applied in many applications such as the trajectorycontrol of robot manipulators, serially connected water tanks, etc. The control structure of theMFCS is limited, but Yamada and Moki reported about whether or not MFCS can representall stabilizing controllers of a minimum phase plant. However, no research has been reportedwhether or not MFCS can represent all stabilizing controllers of a non-minimum phase plant.The purpose of the present paper is to give a solution to the question as to whether or not allstabilizing controllers for a plant are expressible in the MFCS structure. The relation betweenMFCS and the parameterization of all stabilizing controllers for a class of non-minimum phasemultiple-input/multiple-output plants is shown. A simple design method to specify controlcharacteristics is also presented.
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Yuan, Jing. "Improving an adaptive controller for non-minimum phase plants." Automatica 38, no. 5 (May 2002): 869–73. http://dx.doi.org/10.1016/s0005-1098(01)00258-8.

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Campestrini, L., D. Eckhard, M. Gevers, and A. S. Bazanella. "Virtual Reference Feedback Tuning for non-minimum phase plants." Automatica 47, no. 8 (August 2011): 1778–84. http://dx.doi.org/10.1016/j.automatica.2011.04.002.

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OKAJIMA, Hiroshi, Gou ICHIMASA, and Nobutomo MATSUNAGA. "Design of Model Error Compensator for Non-minimum Phase Plants." Transactions of the Society of Instrument and Control Engineers 51, no. 11 (2015): 794–801. http://dx.doi.org/10.9746/sicetr.51.794.

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Dissertations / Theses on the topic "Non minimum phase plant"

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Zhao, Shen. "Practical Solutions to the Non-minimum Phase and Vibration Problems under the Disturbance Rejection Paradigm." Cleveland State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=csu1334670962.

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Šalda, Zbyněk. "Ukázky regulací s prediktivním řízením." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-232182.

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This thesis deals with model predictive control principally Based Predictive Control (MPC). The first part describes the principle of predictive control, cost function, the choice of a constraints in regulation and the choice of weights. In the next section is an analysis system: a system with non-minimal phase (control water turbine), oscillating systems (trolley frame control) and system with a time-delay . In all of these systems is performed classical feedback control using PID control and concurrently regulation with the MPC. MPC is selected as the solution fy Mathworks Model Predictive Control Toolbox and Simulink. The results are then analyzed using the criteria of quality control.
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Apasa, Ryad Faddel. "Non-minimum phase parallel-coupled microstrip filters." Thesis, Cranfield University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302738.

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Dudiki, Venkatesh. "Feed-Forward Compensation of Non-Minimum Phase Systems." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1545163596477852.

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Hashemi-Zahan, Saeid. "Inversion of non-minimum phase systems in signal processing." Thesis, University of Southampton, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266944.

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Boudria, Yacine. "Tracking control for non-minimum phase system and brain computer interface." Thesis, University of Rhode Island, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3716673.

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For generations, humans dreamed about the ability to communicate and interact with machines through thought alone or to create devices that can peer into a person’s mind and thoughts. Researchers have developed new technologies to create brain computer interfaces (BCIs), communication systems that do not depend on the brain’s normal output pathways of peripheral nerves and muscles. The objective of the first part of this thesis is to develop a new BCI based on electroencephalography (EEG) to move a computer cursor over a short training period in real time. The work motivations of this part are to increase: speed and accuracy, as in BCI settings, subject has a few seconds to make a selection with a relatively high accuracy.

Recently, improvements have been developed to make EEG more accurate by increasing the spatial resolution. One such improvement is the application of the surface Laplacian to the EEG, the second spatial derivative. Tripolar concentric ring electrodes (TCREs) automatically perform the Laplacian on the surface potentials and provide better spatial selectivity and signal-to-noise ratio than conventional EEG that is recorded with conventional disc electrodes. Another important feature using TCRE is the capability to record the EEG and the TCRE EEG (tEEG) signals concurrently from the same location on the scalp for the same electrical activity coming from the brain. In this part we also demonstrate that tEEG signals can enable users to control a computer cursor rapidly in different directions with significantly higher accuracy during their first session of training for 1D and 2D cursor control.

Output tracking control of non-minimum phase systems is a highly challenging problem encountered in many practical engineering applications. Classical inversion techniques provide exact output tracking but lead to internal instability, whereas modern inversion methods provide stable asymptotic tracking but produce large transient errors. Both methods provide an approximation of feedback control, which leads to non robust systems, very sensitive to noise, considerable tracking errors and a significant singularity problem. Aiming at the problem of system inversion to the true system, the objective of the second part of this thesis is to develop a new method based on true inversion for minimum phase system and approximate inversion for non-minimum phase systems. The proposed algorithm is automatic and has minimal computational complexities which make it suitable for real-time control.

The process to develop the proposed algorithm is partitioned into (1) minimum phase feedforward inverse filter, and (2) non-minimum phase inversion. In a minimum phase inversion, we consider the design of a feedforward controller to invert the response of a feedback loop that has stable zero locations. The complete control system consists of a feedforward controller cascaded with a closed-loop system. The outputs of the resulting inverse filter are delayed versions of the corresponding reference input signals, and delays are given by the vector relative degree of the closed-loop.

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Hu, Ai-Ping. "Nonlinear non-minimum phase output tracking via output redefinition and learning control." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/16644.

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Wang, Pengfei. "Causal tracking control of a non-minimum phase HIL transmission test system." Thesis, University of Bath, 2009. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518103.

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The automotive industry has long relied on testing powertrain components in real vehicles, which causes the development process to be slow and expensive. Therefore, hardware in the loop (HIL) testing techniques are increasingly being adopted to develop electronic control units (ECU) for engine and other components of a vehicle. In this thesis, HIL testing system is developed to provide a laboratory testing environment for continuously variable transmissions (CVTs). Two induction motors were utilized to emulate a real engine and vehicle. The engine and vehicle models, running in real-time, provide reference torque and speed signals for input and output dynamometers, respectively. To design torque and speed tracking controllers, linear models of the motor and drive systems were firstly identified from the test results. Feedforward controllers were then designed according to the inverse dynamics of the identified models. Because of the existence of unstable zeros in the model, design effort was focused on the stability and causality of the inverse process. Digital preview filters were formulated to approximate the stable inverse of unstable zeros as part of the feedforward controller. Normally, future information of input trajectory is required when implementing the digital preview filters, which makes the feedforward controller non-causal. Since the engine and vehicle model require current information to calculate the next output and no future value can be provided in advance, the application of non-causal digital controllers was limited. A novel method is proposed here to apply non-causal digital controllers causally. Robustness of the controllers is also considered when the two motors are coupled and the gear ratio between them was changed. The proposed coupled control method was tested and verified experimentally by using a manual gearbox before recommending its use for a CVT testing. A multifrequency test signal as well as simulation results of a whole vehicle model were used as torque and speed demand signals in the experiments. A HIL testing case was also presented. Frequency and time domain results showed the effectiveness of the method under both testing procedures to fully compensate for the dynamics of both actuators.
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Lan, Chenyang. "Synthesis of controllers for non-minimum phase and unstable systems using non-sequential MIMO quantitative feedback theory." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/2245.

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Considered in this thesis is multi-input multi-output (MIMO) systems with non-minimum phase (NMP) zeros and unstable poles where some of the unstable poles are located to the right of the NMP zeros. In the single-input single-output (SISO) case such systems pose serious difficulties in controller synthesis for performance and stability. In spite of the added degrees of freedom the MIMO case also poses difficulties as has been shown in the stabilization of the X-29 aircraft. When using the MIMO QFT technique the synthesis starts by considering a set of equivalent SISO plants derived from the plant transfer function matrix that are used to develop a controller. In effect the design problem is reduced to several MISO designs with the diagonal entries of as the equivalent SISO plants. Developed is a transformation scheme that can be used to condition the resulting equivalent SISO plants so that the difficult problem of NMP zeros lying to the left of unstable poles is avoided. Examples illustrate the use of the proposed transformation.
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Liu, Gang. "Sliding mode output tracking control of nonlinear non-minimum phase and time-delay systems." Thesis, University of Sheffield, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443901.

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Books on the topic "Non minimum phase plant"

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Ho, Long T. Neural self-tuning adaptive control strategies of non-minimum phase system developed for flexible robotic arm. Denver, Colo: University of Colorado at Denver, 1993.

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Flamm, David S. Progress on H optimal sensitivity for delay systems. Part I: Minimum phase plant with input delay. l pole/zero weighting function. Cambridge, Mass: Massachusetts Institute of Technology, Laboratory for Information and Decision Systems, 1985.

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Neural self-induced adaptive control of non-minimum phase system. [Washington, DC: National Aeronautics and Space Administration, 1993.

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T, Bialasiewicz Jan, Ho Hai T, and United States. National Aeronautics and Space Administration, eds. Neural self-induced adaptive control of non-minimum phase system. [Washington, DC: National Aeronautics and Space Administration, 1993.

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Neural self-induced adaptive control of non-minimum phase system. [Washington, DC: National Aeronautics and Space Administration, 1993.

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T, Bialasiewicz Jan, Ho Hai T, and United States. National Aeronautics and Space Administration., eds. Neural self-induced adaptive control of non-minimum phase system. [Washington, DC: National Aeronautics and Space Administration, 1993.

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Book chapters on the topic "Non minimum phase plant"

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Pandolfi, L. "Some Inequalities for Non Minimum Phase Systems." In Analysis and Optimization of Systems, 325–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/bfb0007569.

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Delli Priscoli, F., A. Isidori, and L. Marconi. "Nonlinear Output Regulation: Exploring Non-minimum Phase Systems." In Three Decades of Progress in Control Sciences, 141–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11278-2_10.

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Testoni, N., L. De Marchi, N. Speciale, and G. Masetti. "Non-Minimum Phase Iterative Deconvolution of Ultrasound Images." In IFMBE Proceedings, 664–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89208-3_158.

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Toudeft, Abdelmoumène. "Neural Control of Nonlinear Non-Minimum Phase Dynamical Systems." In Artificial Neural Nets and Genetic Algorithms, 404–7. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_105.

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Fliess, M., H. Sira-Ramírez, and R. Marquez. "Regulation of non-minimum phase outputs: a flatness based approach." In Perspectives in Control, 143–63. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1276-1_11.

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Jaiswal, Vivek Kumar, Anurag Singh, Shekhar Yadav, and Shyam Krishna Nagar. "Controlling of Non-minimum Phase System Using Harmony Search Algorithm." In Data and Communication Networks, 185–94. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2254-9_16.

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Pal, Mita, Gautam Sarkar, Ranjit Kumar Barai, and Tamal Roy. "Two-Degree-of-Freedom Control of Non-minimum Phase Mechanical System." In Advances in Intelligent Systems and Computing, 365–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74808-5_31.

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Jackson, Michael R., and Jack H. Dean. "The minimum non-clinical package for initiating Phase I clinical trials." In The Timing of Toxicological Studies to Support Clinical Trials, 99–108. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1424-0_10.

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Xia, Bin, and Liqing Zhang. "Multichannel Blind Deconvolution of Non-minimum Phase System Using Cascade Structure." In Neural Information Processing, 1186–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_184.

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Lozano, Rogelio, and Dionisio A. Suárez. "Adaptive control of non-minimum phase systems subject to unknown bounded disturbances." In Stability Theory, 125–33. Basel: Birkhäuser Basel, 1996. http://dx.doi.org/10.1007/978-3-0348-9208-7_14.

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Conference papers on the topic "Non minimum phase plant"

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Cai, Zhonglun, Chris Freeman, Eric Rogers, and Paul Lewin. "Reference Shift Iterative Learning Control for a Non-minimum Phase Plant." In 2007 American Control Conference. IEEE, 2007. http://dx.doi.org/10.1109/acc.2007.4282192.

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Li Meng and Dingyu Xue. "Fractional order QFT controller for non-minimum phase hydro power plant." In 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012. http://dx.doi.org/10.1109/ccdc.2012.6244446.

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Albertos, Pedro, Gustavo Scaglia, Juan Yuz, and Cui Wei. "Trajectory Control in Non-Minimum Phase Plants." In 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2021. http://dx.doi.org/10.1109/iciea51954.2021.9516229.

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Meng, Li, and Dingyu Xue. "QFT fractional order robust controller for non-minimum phase hydro power plant." In 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2012. http://dx.doi.org/10.1109/csae.2012.6272916.

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Chida, Yuichi, Shota Sekiguchi, Hiroyuki Kobayashi, and Yuichi Ikeda. "Unknown Disturbance Estimator Design for Non-Minimum Phase Plants Using Parallel Feed-Forward Model." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3774.

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A novel design method of an unknown disturbance observer for non-minimum phase plants is proposed in the present paper. In order to improve the estimation performance, we introduce the approach as a virtual augmented plant by adding a parallel model to the non-minimum phase real plant. The parallel model is designed so that the virtual augmented model becomes the minimum phase. Thus, it is possible to design the unknown disturbance estimator for the minimum phase plant but for the non-minimum phase plant. As the result, it is possible to improve the estimation performances. In this case, it is important to clarify the relationship between the unknown disturbance estimation signal for the real plant and the virtual augmented plant. In the present paper, the unknown disturbance estimation signal of the real plant is re-constructed by using the disturbance estimation of the virtual plant. And the parallel model design method is also proposed. The effectiveness of the proposed method is verified by numerical simulations for several mechanical vibration systems. The results show that the proposed method can improve estimation performances in comparison with conventional methods.
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Amann, N. "Non-minimum phase plants in iterative learning control." In Second International Conference on `Intelligent Systems Engineering'. IEE, 1994. http://dx.doi.org/10.1049/cp:19940610.

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Filho, Ricardo Scheid, Diego Eckhard, Gustavo R. Goncalves da Silva, and Luciola Campestrini. "Application of Virtual Reference Feedback Tuning to a non-minimum phase pilot plant." In 2016 IEEE Conference on Control Applications (CCA). IEEE, 2016. http://dx.doi.org/10.1109/cca.2016.7587989.

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Khodabakhshian, A., H. Rahimi, and N. Golbon. "QFT design for load frequency control of non-minimum phase hydro power plant." In 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control. IEEE, 2006. http://dx.doi.org/10.1109/cacsd-cca-isic.2006.4776843.

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Khodabakhshian, A., H. Rahimi, and N. Golbon. "QFT Design for Load Frequency Control of Non-Minimum Phase Hydro Power Plant." In 2006 IEEE International Conference on Control Applications. IEEE, 2006. http://dx.doi.org/10.1109/cca.2006.286059.

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Campestrini, Luciola, Michel Gevers, and Alexandre Sanfelice Bazanella. "Virtual Reference Feedback Tuning for non minimum phase plants." In 2009 European Control Conference (ECC). IEEE, 2009. http://dx.doi.org/10.23919/ecc.2009.7074690.

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Reports on the topic "Non minimum phase plant"

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Meyer, Perry A., Ellen BK Baer, Judith A. Bamberger, James A. Fort, and Michael J. Minette. Assessment of Differences in Phase 1 and Phase 2 Test Observations for Waste Treatment Plant Pulse Jet Mixer Tests with Non-Cohesive Solids. Office of Scientific and Technical Information (OSTI), October 2010. http://dx.doi.org/10.2172/992015.

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Korte, N. E., S. C. Hall, and J. L. Baker. Refinement of the Kansas City Plant site conceptual model with respect to dense non-aqueous phase liquids (DNAPL). Office of Scientific and Technical Information (OSTI), October 1995. http://dx.doi.org/10.2172/117791.

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Flamm, David S., and Sanjoy K. Mitter. Progress on H (at Infinity) Optimal Sensitivity for Delay Systems. Part 1. Minimum Phase Plant with Input Delay. 1 Pole/Zero Weighting Function. Revised. Fort Belvoir, VA: Defense Technical Information Center, November 1985. http://dx.doi.org/10.21236/ada165766.

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McLaurin, Eric. Six-Phase Heating(trademark) Pilot-Scale Test. Technology Performance Report, Dense Non-Aqueous Phase Liquid, Eastern Parking Lot Groundwater Plume. Air Force Plant 4, Fort Worth, Texas. Fort Belvoir, VA: Defense Technical Information Center, May 2001. http://dx.doi.org/10.21236/ada522634.

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Ruosteenoja, Kimmo. Applicability of CMIP6 models for building climate projections for northern Europe. Finnish Meteorological Institute, September 2021. http://dx.doi.org/10.35614/isbn.9789523361416.

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In this report, we have evaluated the performance of nearly 40 global climate models (GCMs) participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). The focus is on the northern European area, but the ability to simulate southern European and global climate is discussed as well. Model evaluation was started with a technical control; completely unrealistic values in the GCM output files were identified by seeking the absolute minimum and maximum values. In this stage, one GCM was rejected totally, and furthermore individual output files from two other GCMs. In evaluating the remaining GCMs, the primary tool was the Model Climate Performance Index (MCPI) that combines RMS errors calculated for the different climate variables into one index. The index takes into account both the seasonal and spatial variations in climatological means. Here, MCPI was calculated for the period 1981—2010 by comparing GCM output with the ERA-Interim reanalyses. Climate variables explored in the evaluation were the surface air temperature, precipitation, sea level air pressure and incoming solar radiation at the surface. Besides MCPI, we studied RMS errors in the seasonal course of the spatial means by examining each climate variable separately. Furthermore, the evaluation procedure considered model performance in simulating past trends in the global-mean temperature, the compatibility of future responses to different greenhouse-gas scenarios and the number of available scenario runs. Daily minimum and maximum temperatures were likewise explored in a qualitative sense, but owing to the non-existence of data from multiple GCMs, these variables were not incorporated in the quantitative validation. Four of the 37 GCMs that had passed the initial technical check were regarded as wholly unusable for scenario calculations: in two GCMs the responses to the different greenhouse gas scenarios were contradictory and in two other GCMs data were missing from one of the four key climate variables. Moreover, to reduce inter-GCM dependencies, no more than two variants of any individual GCM were included; this led to an abandonment of one GCM. The remaining 32 GCMs were divided into three quality classes according to the assessed performance. The users of model data can utilize this grading to select a subset of GCMs to be used in elaborating climate projections for Finland or adjacent areas. Annual-mean temperature and precipitation projections for Finland proved to be nearly identical regardless of whether they were derived from the entire ensemble or by ignoring models that had obtained the lowest scores. Solar radiation projections were somewhat more sensitive.
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