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Relevant bibliographies by topics / Control/multivariable systems

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Academic literature on the topic 'Control/multivariable systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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Journal articles on the topic "Control/multivariable systems"

1

Shen, Shih-Haur, and Cheng-Ching Yu. "Indirect feedforward control: multivariable systems." Chemical Engineering Science 47, no. 12 (August 1992): 3085–97. http://dx.doi.org/10.1016/0009-2509(92)87008-e.

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HARA, Shinji, and Yutaka YAMAMOTO. "Stability of Multivariable Repetitive Control Systems." Transactions of the Society of Instrument and Control Engineers 22, no. 12 (1986): 1256–61. http://dx.doi.org/10.9746/sicetr1965.22.1256.

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EL-SHAHAT, ISMAIL. "Suboptimal Decentralized Control of Multivariable Systems." Journal of King Abdulaziz University-Engineering Sciences 11, no. 2 (1999): 107–14. http://dx.doi.org/10.4197/eng.11-2.9.

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4

Gough, N. E., I. H. Ting, G. M. Dimirovski, and V. P. Deskov. "Decentralized Control in Multivariable Convolution Systems." IFAC Proceedings Volumes 26, no. 2 (July 1993): 887–90. http://dx.doi.org/10.1016/s1474-6670(17)49260-3.

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Johansson, Karl Henrik. "Interaction bounds in multivariable control systems." Automatica 38, no. 6 (June 2002): 1045–51. http://dx.doi.org/10.1016/s0005-1098(01)00285-0.

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LIU, G. P., H. UNBEHAUEN, and R. J. PATTON. "Robust control of multivariable critical systems." International Journal of Systems Science 26, no. 10 (October 1995): 1907–18. http://dx.doi.org/10.1080/00207729508929144.

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Johansson, Karl Henrik. "Interaction bounds in multivariable control systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 1975–80. http://dx.doi.org/10.1016/s1474-6670(17)56335-1.

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Kolavennu, S., S. Palanki, and J. C. Cockburn. "Nonlinear control of nonsquare multivariable systems." Chemical Engineering Science 56, no. 6 (March 2001): 2103–10. http://dx.doi.org/10.1016/s0009-2509(00)00470-x.

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Jensen, N., D. G. Fisher, and S. L. Shah. "Interaction analysis in multivariable control systems." AIChE Journal 32, no. 6 (June 1986): 959–70. http://dx.doi.org/10.1002/aic.690320606.

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Hsu, Liu, José Paulo Vilela Soares da Cunha, Ramon R. Costa, and Fernando Lizarralde. "UNIT VECTOR CONTROL OF MULTIVARIABLE SYSTEMS." IFAC Proceedings Volumes 35, no. 1 (2002): 331–36. http://dx.doi.org/10.3182/20020721-6-es-1901.01041.

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Dissertations / Theses on the topic "Control/multivariable systems"

1

Chang, Anton On Tak. "Multivariable predictive control." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336179.

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Perng, Ming-Hwei. "Nearly decoupled multivariable control systems design." Thesis, University of Cambridge, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306496.

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Iyer, S. N. "Modelling and control of multivariable systems." Thesis, University of Oxford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.233534.

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Dong, Y. W. "Genetic design of multivariable control systems." Thesis, University of Salford, 2015. http://usir.salford.ac.uk/36014/.

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In the real world there are three types of multivariable control systems. The first one is when the number of inputs is equal to the number of the outputs, this type of multivariable control system is defined as a squared multivariable control system and the main type of controller designed is a decoupling controller which minimizes interactions and gives good set-point tracking. The second type of multivariable control system is where the number of inputs is greater than the number of the outputs, for this type of system the main controller designed is a fail-safe controller. This controller remains stable if a sub-set of actuator fail. The third type of multivariable control system is the number of outputs is greater than the number of inputs, for this type of system the main controller designed is an override control system. This controller only controls a sub-set of outputs based on a lowest wins control strategy. All the three types of multivariable control systems are included in this thesis. In this thesis the design of multivariable decoupling control, multivariable fail-safe control and multivariable override control as considered. The invention of evolutionary computing techniques has changed the design philosophy for control system design. Rather than using conventional techniques such as Nyquest plots or root-loci control systems can be designed using evolutionally algorithm. Such algorithms evolve solutions using cost functions and optimization. There are a variety of system performance indicators such as integral squared error operator has been used as cost functions to design controllers using such algorithms. The design of both fail-safe and override multivariable controllers is a difficult problem and there are very few analytical design methods for such controllers. Therefore, the main objective of this thesis is to use the genetic algorithms to involve both fail-safe and override controller multivariable controllers, such that they perform well in the time-domain.

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Rossiter, J. A. "Multivariable self-tuning." Thesis, University of Oxford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.279927.

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Alukaidey, R. A. S. "Multivariable identification and adaptive control." Thesis, Brunel University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384517.

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Smith, Keith J. "Multivariable control of dynamic structural test systems." Thesis, Loughborough University, 1997. https://dspace.lboro.ac.uk/2134/13857.

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Multi-actuator structural testing has traditionally been regarded, from a control point of view, as a multi-loop single-input, single-output problem. This approach does not take into account the interaction between. different actuators, due to the dynamics of the structure under test, which can be considerable. The result of this is often poor laboratory reproduction of the actual service data. This project shows that the mass of the structure under test has a considerable impact upon the stability of the traditional multi-loop, single-input, single-output control system. Where stability is prejudiced, the loop gains have to be reduced to maintain stability and this can degrade the performance of the test. In these circumstances multivariable control offers the potential for a significant improvement in performance. Two experimental rigs are used in this project, both exhibit major interaction and pose a significant control problem. The first rig consists of a laboratory scale cantilever beam excited by two electro-dynamic vibrators with displacements measured by Linear Variable Differential Transformers (L VDTs). The second, industrial-scale, rig consists of a large steel frame excited by two hydraulic actuators with applied force measured by load cells. Multivariable controllers are designed and implemented on these rigs based on the frequency-domain Characteristic Locus method. The multivariable controllers are shown to demonstrate superior performance to traditional multi-loop controllers. Mathematical models of the rigs are not required for controller design, instead experimental frequency responses are all that are needed. This is a major attraction of the Characteristic Locus method since the task.of modelling the dynamics of a multichannel structural test system is not trivial. However, obtaining the frequency response of the second rig is made difficult by the imposition of closed-loop control during the identification experiment. A technique is presented to overcome this problem using an existing correlation method.

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El-Rabaie, Nabila Mahmoud. "Multivariable self-tuning control of boiler systems." Thesis, Queen's University Belfast, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335335.

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Latchman, H. A. "Frequency response methods for uncertain multivariable systems." Thesis, University of Oxford, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.371544.

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Chang, Michael. "Adaptive switching control applied to multivariable systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ27888.pdf.

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Books on the topic "Control/multivariable systems"

1

Bernar, D. Real time multivariable control systems. Manchester: UMIST, 1994.

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2

Lunze, Jan. Robust multivariable feedback control. New York: Prentice Hall, 1988.

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Bates, Declan. Robust multivariable control of aerospace systems. Delft: DUP Science, 2002.

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4

Distributed fuzzy control of multivariable systems. Dordrecht: Kluwer Academic Publishers, 1996.

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5

Doctor, Sala Antonio, ed. Multivariable control systems: An engineering approach. London: Springer, 2004.

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Gegov, Alexander. Distributed Fuzzy Control of Multivariable Systems. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8640-5.

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Gegov, Alexander. Distributed Fuzzy Control of Multivariable Systems. Dordrecht: Springer Netherlands, 1996.

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8

Linear control systems: Synthesis of multivariable systems and multidimensional systems. Taunton, Somerset, England: Research Studies Press, 1992.

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9

Chang, Michael. Adaptive control applied to unmodelled multivariable systems. Ottawa: National Library of Canada, 1993.

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10

Bongiorno, Joseph J., and Kiheon Park. Design of Linear Multivariable Feedback Control Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44356-6.

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Book chapters on the topic "Control/multivariable systems"

1

Yu, Cheng-Ching. "Multivariable Systems." In Advances in Industrial Control, 67–108. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3636-1_5.

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2

Ackermann, Jürgen. "Multivariable Systems." In Sampled-Data Control Systems, 415–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82554-5_9.

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3

Isermann, Rolf. "Multivariable State Control Systems." In Digital Control Systems, 109–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-86420-9_10.

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Isermann, Rolf. "Structures of Multivariable Processes." In Digital Control Systems, 71–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-86420-9_7.

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Isermann, Rolf. "Parameter-optimized Multivariable Control Systems." In Digital Control Systems, 89–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-86420-9_8.

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Isermann, Rolf. "Multivariable Matrix Polynomial Control Systems." In Digital Control Systems, 105–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-86420-9_9.

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7

Mackenroth, Uwe. "Basic Properties of Multivariable Feedback Systems." In Robust Control Systems, 133–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09775-5_6.

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8

Tao, Gang, Shuhao Chen, Xidong Tang, and Suresh M. Joshi. "Designs for Multivariable Systems." In Adaptive Control of Systems with Actuator Failures, 103–22. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3758-0_5.

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9

Glattfelder, A. H., and W. Schaufelberger. "Multivariable Control with Constraints." In Control Systems with Input and Output Constraints, 447–81. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0047-8_8.

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Fradkov, Alexander L., Iliya V. Miroshnik, and Vladimir O. Nikiforov. "Nonlinear Control of Multivariable Systems." In Nonlinear and Adaptive Control of Complex Systems, 127–81. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9261-1_4.

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Conference papers on the topic "Control/multivariable systems"

1

Liu, Rui-Juan, Guo-Ping Liu, and Min Wu. "A novel decoupling control method for multivariable systems with disturbances." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334609.

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2

Schrader, Cheryl B., and Michael K. Sain. "Subzeros of Linear Multivariable Systems." In 1989 American Control Conference. IEEE, 1989. http://dx.doi.org/10.23919/acc.1989.4790202.

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3

Wang, Chi-Lun, and Jia-Ying Tu. "Mixed H2/H∞ feedback control of multivariable dynamically substructured systems." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915134.

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4

Huang, Jin-Quan, and Jian-Guo Sun. "Multivariable Adaptive Control for Turbojet Engines." In ASME 1993 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/93-gt-044.

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Integrated flight and propulsion control systems will require propulsion systems to reduce in time to accelerate engine and to improve transient performance for a full flight envelope operation. A multivariate model reference adaptive control (MRAC) scheme is proposed in this paper. Acceleration time is reduced by uptrimming engine pressure ratio in some operating conditions and in parts of the flight envelope where excess stall margin and turbine temperature margin exist. The MRAC scheme is obtained by using Liapunov direct method. Simulation studies are performed for a two-spool turbojet engine. The satisfactory transient responses are obtained at different operating points from idle to maximum dry power in flight envelope which shows good robustness for unmodelled dynamics of nonlinear engine. Simulation results also show the good effectiveness of reducing interaction in multivariable system with significant coupling. Using the Multivariable MRAC controller, the engine acceleration time is reduced about 16 percent in comparison with the conventional engine controller.

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Ilyasov, B. G. "System approach to design of multifunctional multivariable control systems." In UKACC International Conference on Control (CONTROL '98). IEE, 1998. http://dx.doi.org/10.1049/cp:19980463.

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Beale, S., B. Shafai, P. LaRocca, and E. Cusson. "Adaptive Forced Balancing for Multivariable Systems." In 1993 American Control Conference. IEEE, 1993. http://dx.doi.org/10.23919/acc.1993.4793273.

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Freudenberg, J. S. "Frequency Dependent Tradeoffs in Multivariable Systems." In 1989 American Control Conference. IEEE, 1989. http://dx.doi.org/10.23919/acc.1989.4790203.

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8

Berger, W. A., R. J. Perry, and H. H. Sun. "Independent Parameter Control in Multivariable Systems." In 1989 American Control Conference. IEEE, 1989. http://dx.doi.org/10.23919/acc.1989.4790209.

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Otsuka, N. "Generalized invariant subspaces for linear multivariable systems." In UKACC International Conference on Control (CONTROL '98). IEE, 1998. http://dx.doi.org/10.1049/cp:19980461.

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Bar-on, J. R., and E. A. Jonckheere. "Gain margins for multivariable control systems." In 29th IEEE Conference on Decision and Control. IEEE, 1990. http://dx.doi.org/10.1109/cdc.1990.203609.

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Reports on the topic "Control/multivariable systems"

1

Morse, A. S. Adaptive Control of Multivariable Systems. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada205103.

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Morse, A. S. Adaptive Control of Multivariable Systems. Fort Belvoir, VA: Defense Technical Information Center, October 1985. http://dx.doi.org/10.21236/ada162795.

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Hovakimyan, Naira. Robust Adaptive Control of Multivariable Nonlinear Systems. Fort Belvoir, VA: Defense Technical Information Center, November 2008. http://dx.doi.org/10.21236/ada501711.

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Mears, Mark J., and Marios M. Polycarpou. Stable Neural Control of Uncertain Multivariable Systems. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada411951.

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Hovakimyan, Naira. Robust Adaptive Control of Multivariable Nonlinear Systems. Fort Belvoir, VA: Defense Technical Information Center, March 2011. http://dx.doi.org/10.21236/ada565190.

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Packard, Andrew, and John C. Doyle. Robust Control of Multivariable and Large Scale Systems. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada194250.

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7

Doyle, J. D., and T. B. Cunningham. Robust Control of Multivariable and Large Space Systems. Fort Belvoir, VA: Defense Technical Information Center, March 1985. http://dx.doi.org/10.21236/ada155117.

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Wolovich, W. A., and C. Cometta. Practical Methods for the Compensation and Control of Multivariable Systems. Fort Belvoir, VA: Defense Technical Information Center, January 1985. http://dx.doi.org/10.21236/ada151047.

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Shieh, Leang S. Structure Theories and Applications of Multivariable Control Systems Described by Block Canonical Forms. Fort Belvoir, VA: Defense Technical Information Center, January 1987. http://dx.doi.org/10.21236/ada176482.

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Khorrami, Farshad, Joseph J. Bongiorno, and Jr. Robust Wiener-Hopf Design for Multivariable Control Systems and Applications to Vibration Suppression on a Weapon System. Fort Belvoir, VA: Defense Technical Information Center, April 1997. http://dx.doi.org/10.21236/ada324822.

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