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Michail, K; Zolotas, A C; Goodall, R M; Whidborne, J F
Publisher: Taylor & Francis
Languages: English
Types: Article
Subjects: T, TA0174, TJ0212
For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system.
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    • Anderson, B.D.O., and Moore, J.B., Optimal control: Linear quadratic methods, Upper Saddle River, NJ, USA: Prentice-Hall, Inc. (1990).
    • Blanke, M., Izadi-Zamanabadi, R., Bogh, S.A., and Lunau, C.P. (1997), “Fault-tolerant control systems - a holistic view,” Control Engineering Practice, 5(5), 693-702.
    • Blanke, M., Kinnaert, M., Lunze, J., and Staroswiecki, M., Diagnosis and Fault-Tolerant Control, Secaucus, NJ, USA: Springer-Verlag New York, Inc. (2003).
    • Coello, C.A.C. (2002), “Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art,” Computer Methods in Applied Mechanics and Engineering, 191(11-12), 1245-1287.
    • Dakev, N.V., Whidborne, J.F., Chipperfield, A.J., and Fleming, P.J. (1997), “Evolutionary H∞ design of an electromagnetic suspension control system for a maglev vehicle,” Proceedings of the Institution of Mechanical Engineers.Part I, Journal of Systems & Control Engineering, 211(5), 345-355.
    • Deb, K., Multi-objective Optimization using Evolutionary Algorithms, New York, NY, USA: John Wiley & Sons, Inc. (2001).
    • Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002), “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, 6(2), 182- 197.
    • Dreo, J., Siarry, P., Petrowski, A., and Taillard, E., Metaheuristics for Hard Optimization, New York: Springer-Verlg Berlin Heidelberg (2006).
    • Fleming, P.J., and Purshouse, R.C. (2002), “Evolutionary algorithms in control systems engineering: A survey,” Control Engineering Practice, 10(11), 1223-1241.
    • Frank, P.M. (1990), “Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-a survey and some new results,” Automatica, 26(3), 459-474.
    • Franklin, G.F., Powell, J.D., and Emami-Naeini, A., Feedback Control of Dynamic System, 4th Edition, Upper Saddle River, NJ, USA: Prentice Hall, Inc. (2002).
    • Friedland, B., Control System Design - an introduction to state space methods, McGraw Hill (1986).
    • Friedland, B., Advanced Control System Design, Upper Saddle River, NJ, USA: Prentice-Hall Inc. (1996).
    • Goldberg, D.E., Genetic algorithm in Search, Optimisation and Machine Learning., Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc. (1989).
    • Goodall, R.M. (1994), “Dynamic characteristics in the design of MAGLEV suspensions,” Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 208(1), 33-41.
    • Goodall, R.M. (2000), “On the robustness of flux feedback control for electro-magnetic MAGLEV Controllers,” in Proceedings of 16th International Conference on MAGLEV Systems and Linear Drives, pp. 197-202.
    • Goodall, R.M. (2004), “Dynamics and control requirements for EMS Maglev suspensions,” in Proceedings on international conference on Maglev, pp. 926-934.
    • Goodall, R.M. (2008), “Generalised Design Models for EMS Maglev,” in Proceedings of MAGLEV 2008 - The 20th International Conference on Magnetically Levitated Systems and Linear Drives.
    • Goodall, R.M. (Sep 1985), “The theory of electromagnetic levitation,” Physics in Technology, 16(5), 207-213.
    • Isermann, R. (1997), “Supervision, fault-detection and fault-diagnosis methods-an introduction,” Control Engineering Practice, 5(5), 639-652.
    • Isermann, R., and Ball, P. (1997), “Trends in the application of model-based fault detection and diagnosis of technical processes,” Control Engineering Practice, 5(5), 709-719.
    • Kalman, R.E., and Bucy, R.S. (1961), “New Results in Linear Filtering and Prediction Theory,” Random processes, 83(1), 95-108.
    • Konak, A., Coit, D.W., and Smith, A.E. (2006), “Multi-objective optimization using genetic algorithms: A tutorial,” Reliability Engineering and System Safety, 91(9), 992-1007.
    • Lee, H.W., Kim, K.C., and Lee, J. (2006), “Review of Maglev train technologies,” IEEE Transactions on Magnetics, 42(7), 1917-1925.
    • Maciejowski, J.M., Multivariable Feedback Design, Boston, MA: Addison-Wesley Longman Publishing Co. (1990).
    • Michail, K., “Optimised Configuration of Sensing Elements For Control And Fault Tolerance Applied To An Electro-Magnetic Suspension System,” PhD Dessartation, Loughborough University, Department of Electronic and Electrical Engineering.http://hdl.handle.net/2134/5806 (2009).
    • Paddison, J.E., “Advanced Control Strategies for Maglev Suspension Systems,” PhD Dissertation, Loughborough Univeristy, Department of Electronic and Electrical Engineering (1995).
    • Panagopoulos, H., Astrom, K.J., and Hagglund, T. (2002), “Design of PID controllers based on constrained optimisation,” IEE Proceedings Control Theory and Applications, 149(1), 32-40.
    • Patton, R.J. (1997), “Fault-Tolerant Control: The 1997 Situation,” in IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, Vol. 3, pp. 1029-1052.
    • Skogestad, S., and Postlethwaite, I., Multivariable Feedback Control Analysis and Design, New York: John Wiley & Sons Ltd, 2nd Edition (2005).
    • Wal, M.V.D., and Jager, B.D. (2001), “A review of methods for input/output selection,” Automatica, 37(4), 487-510.
    • Zou, X., Chen, Y., Liu, M., and Kang, L. (2008), “A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 38(5), 1402-1412.
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