Abstract
This paper focuses on the design of a real-time adaptive Takagi–Sugeno (T–S) fuzzy-based dynamic feedback tracking controller to deal with the metallic sphere position control of a magnetic levitation system (MLS), which is an intricate and highly nonlinear system involving plant uncertainties and external disturbances. The dynamic model of this MLS is first constructed based on the concepts of geometry and motion dynamics. The objective of this proposed control strategies is to design a real-time adaptive controller with the help of Takagi–Sugeno type fuzzy-based output feedback techniques and to directly ensure the asymptotic stability of the closed-loop controlled system by Lyapunov stability theorem without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers. Proposed adaptive tracking controller is developed in such a way such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error is as small as possible. In this paper, the controller consists of adaptive and robustifying components whose role is to nullify the effect of uncertainties and achieve a desired tracking performance. Here, separate adaptive control laws have been proposed to automatically take care of external disturbance and uncertainties by designing a two-port controller. The first part stabilizes the nominal plant; without modeling uncertainties. The second part of the controller is to reject modeling uncertainties. The good transient control performance and robustness to uncertainties of the proposed adaptive control scheme for the MLS is verified by numerical simulations and real-time experimental results. These results demonstrate that, the proposed adaptive controller yields favorable control performance superior to that of PID and and Neuro-fuzzy network controller in terms of overshoot, settling time, mean square error and steady-state error and also it can guarantee the system stability and parameter convergence with a pole placement algorithm.
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References
Anderson SR, Lepora NF, Porrill J, Dean P (2010) Nonlinear dynamic modeling of isometric force production in primate eye muscle. IEEE Trans Biomed Eng 57(7):1554
Astrom KJ, Wittenmark B (1995) Adaptive control. Addison-Wesley, Reading
Chen MY, Wang MJ, Fu LC (2003a) Modeling and controller design of a maglev guiding system for application in precision positioning. IEEE Trans Ind Electr 50(3):493–506
Chen MY, Wang MJ, Fu CL (2003b) A novel dual-axis repulsive maglev guiding system with permanent magnet: modeling and controller design. IEEE/ASME Trans Mechatron 8(1):77–86
Chen S, Liu B, Li Y, Liang Y (2012) Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks. Neurocomputing 91:1–10
Chen S, Liu B, Li Y, Liang Y (2013) Decentralized control of collaborative redundant manipulators with partial command coverage via locally connected recurrent neural networks. Neural Comput Appl 23:1051–1060
Conlery WG, Raman A, Krousgrill CM (2005) Nonlinear dynamics in Tomlinson’s model for atomic-scale friction and friction force microscopy. J Appl Phys 98
Cui S, Soh YC (2010) An accurate separation estimation algorithm for the casimir oscillator. J Microelectromech Syst 19(5):1153–1161
Feng G (2006) A survey on analysis and design of model-based fuzzy control systems. IEEE Trans Fuzzy Syst 14(5)
Filippou FC, D’Ambrisi A, Issa A (1992) Nonlinear static and dynamic analysis of reinforced concrete subassemblages. Report No. UCB/EERC-92/08 Earthquake Engineering Research Center College of Engineering University of California, Berkeley
Gao Y, Er MJ (2003) Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems. IEEE Trans Fuzzy Syst 11(4):462–477
Gutierrez HM, Ro PI (2005) Magnetic servo levitation by sliding-mode control of nonaffine systems with algebraic input invertibility. IEEE Trans Ind Electron 52(5):1449–1455
Hajjaji AE, Ouladsine M (Aug. 2001) Modeling and nonlinear control of MLSs. IEEE Trans Ind Electron 48(4):831–838
Hedrick JK, Girard A (2010) Control of nonlinear dynamic systems: theory and applications
HornsteinS, Gottlieb O (2012) Nonlinear multimode dynamics and internal resonances of the scam process in noncontactingatomic force microscopy. J Appl Phys 112(7)
Huang CM, Yen JY, Chen MS (2000) Adaptive nonlinear control of repulsive maglev suspension systems. Control Eng Pract 8(12):1357–1367
Hui S, Zak SH (1993) Robust output feedback stabilization of uncertain dynamic systems with bounded controllers. Int J Robust Nonlinear Control 3:115–132
Hui S, Stanislaw H (1993) Robust output feedback stabilization of uncertain dynamic systems with bounded controllers. Int J Robust Nonlinear Control 3(2):115–132
Hurley WG, Hynes M, Wolfle WH (2004) PWM control of a magnetic suspension system. IEEE Trans Educ 47(2):165–173
Hurley WG, Wolfle WH (1997) Electromagnetic design of a magnetic suspension system. IEEE Trans Educ 40(2):124–130
Jie X, Kulakowski BT (2003) Sliding mode control of active suspension for transit buses based on a novel air-spring model. Proc IEEE Am Control Conf 5:3768–3773
Joo J, Seo JH (1997) Design and analysis of the nonlinear feedback linearizing control for an electromagnetic suspension system. IEEE Trans Control Syst Technol 5(1):135–144
Kaloust J, Ham C, Siehling J, Jongekryg E, Han Q (2004) Nonlinear robust control design for levitation and propulsion of a maglev system. In: Proceedings of institue of electrical and engineering (control theory application), vol 151, no 4, pp 460–464
Kemin K, Tekkouk O (2006) Constrained generalised predictive control with estimation by genetic algorithm for a MLS. Int J Innov Comput Inf Adv Control 2(3):543
Kim SJ, Lee CW (1999) On-line identification of current and position stiffness by LMS algorithm in active magnetic bearing system equipped with force transducers. Mech Syst Signal Process 13(5):681–690
Kole A, Mondal AK (2008) Neural network based predictive control of non-linear process with fast optimization algorithm. J Inst Eng (India) Comput Eng 89:32–37
Li JH (2005) DSP based control of a PWM-driven MLS. In: IEEE ICSS2005 international conference on systems and signals
Li S (2013) Neural processing letters 37:411–424
Li Y, Liu B, Murray T (2012) Model-free control of Lorenz chaos using an approximate optimal control strategy. Commun Nonlinear Sci Numer Simul 17(12):4891–4900
Li T, Feng G, Zou Z, Liu Y (2009) Robust adaptive fuzzy tracking control for a class of MIMO systems: a minimal-learning-parameters algorithm. In: American control conference hyatt regency riverfront, St. Louis, pp 10–12
Li S, Li Y (2013) Nonlinearly activated neural network for solving time-varying complex sylvester equation. IEEE Trans Cybern
Li S, Li Y, Wang Z (2013) A class of finite-time dual neural networks for solving quadratic programming problems and its k-winners-take-all application. Neural Netw 39:27–39
Lin CT, Lee CSG (1996) Neural fuzzy systems. Prentice-Hall, UpperSaddle River
Lin FJ, Fung RF, Wai RJ (1998) Comparison of sliding mode and fuzzy neural network control for motor-toggle servomechanism. IEEE Trans Mechatron 3(4):302–318
Lin FJ, Hwang WJ, Wai RJ (1999) A supervisory fuzzy neural network control system for tracking periodic inputs. IEEE Trans Fuzzy Syst 7(1):41–52
Lin FJ, Wai RJ (2001) A hybrid computed torque controller using fuzzy neural network for motor-quick-return servomechanism. IEEE Trans Mechatron 6(1):75–89
Liu YJ, Wang W (2007) Adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems. Inf Sci 177:3901–3917
Li S, Wang Z, Liu Y (2012) Using Laplacian eigenmap as heuristic information to solve nonlinear constraints defined on a graph and its application in distributed range-free localization of wireless sensor networks. Springer, Berlin
Luo Y, Tang G (1995) Hybrid approach for solving systems of nonlinear equations using chaos optimization and quasi-Newton method. Wiley Inc. International Journal of Intelligent Systems 03/2007 10(1):5–13
Omidvar O, Elliott DL (1997) Neural systems for control. Academic Press, New York
Ono M, Koga S, Ohtsuki H (2002) Japan’s superconducting maglev train. IEEE Trans Instrum Meas Mag 5(1):9–15
Park CW, Cho YW (2004) T-S model based indirect adaptive fuzzy control using online parameter estimation. IEEE Trans Syst Man Cybern Part B Cybern 34(6):2293–2302
Park CW, Lee CH, Kim JH, Park M (2001) Design of an adaptive fuzzy controller and its applications to controlling uncertain chaotic systems. Trans Control Autom Syst Eng 3(2):95–105
Qi R, Brdys MA (2005) Adaptive fuzzy modelling and control for discrete-time nonlinear uncertain systems. In: Proceedings of the 2005 American control conference, pp 1108–1113
Qi R, Brdys MA (2006) T–S model based indirect adaptive fuzzy control for a class of MIMO uncertain nonlinear systems. In: Proceedings of the 6th world congress on intelligent control and automation, Dalian
Queiroz MS, Dawson DM (1996) Nonlinear control of active magnetic bearings: a backstepping approach. IEEE Trans Control Syst Technol 4(5):545–552
Rhoads JF, Shaw SW, Turner KL (2010) Nonlinear dynamics and its applications in micro- and nanoresonators. J Dyn Syst Meas Control 132(3)
Rote DM, Cai Y (2002) Review of dynamic stability of repulsiveforce maglev suspension systems. IEEE Trans Magn 38(2):1383–1390
Schweitzer G, Siegwart R, Lösch F, Berksun R (2000) Proceedings of the seventh international symposium on magnetic bearings, ETH-Zurich Swiss Federal Institute of Technology, Zurich
Shakir H, Kim WJ (2006) Nanoscale path planning and motion control with maglev positioners. IEEE/ASME Trans Mechatron 11(5):625–633
Shiao YS (2001) Design and implementation of a controller for a MLS. Proc Natl Sci Counc 11(2):88–94
Skricka N, Markert R (2002) Improvements of the integration of active magnetic bearings. Mechatronics 12:1059–1068
Slotine JJE, Li W (1991) Applied nonlinear control. Prentice-Hall, Upper Saddle River
Somnath AG (2012) Analysis of disctrete time sliding model for a magnetic levitation system. Special issue of international journal of computer applications (0975–8887) on issues and challenges in networking, intelligence and computing technologies (ICNICT 2012)
Strogatz SH (2012) Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering (studies in nonlinearity). 1st edn. Communications in nonlinear science and numerical simulation, science direct, vol 17, no 12, pp 4517–5296, 4891–4900
Tonoli A, Bornemann HJ (1998) Analysis of losses due to rotor vibrations in a high-Tc superconducting flywheel system. J Sound Vib 212(4):649–662
Torres LHS, Schnitman L (2012) Fuzzy control: an adaptive approach using fuzzy estimators and feedback linearization, fuzzy logic - controls, concepts, theories and applications. In: Dadios E (ed) ISBN: 978-953-51-0396-7. InTech. Available from http://www.intechopen.com/books/fuzzy-logiccontrols-concepts-theories-and-applications
Trumper DL, Olson SM, Zubrahmanyan PK (2007) Linearizing control of magnetic suspension systems. IEEE Trans Control Syst Technol 5(4):427–438. 1762 IEEE Trans on Industrial Electronics, vol 54, no 3 June 2007
Trumper DL, Olson SM, Subrahmanyan PK (1997) Linearizing control of magnetic suspension systems. IEEE Trans Control Syst Technol 5(4):427–438
Tseng C, Chen B (2001) Fuzzy tracking control design for nonlinear dynamic systems via T-S fuzzy model. IEEE Trans Fuzzy Syst 9(3)
Wai RJ, Lee JD (2005) Performance comparisons of model free control strategies for hybrid MLS. In: Proceedings of institue of electrical and engineering (electrical power application), vol 152, no 6, pp 1556–1564
Wai RJ, Lin FJ (1999) Fuzzy neural network sliding mode position controller for induction servo motor drive. Proc Inst Elect Eng Electr Power Appl 146(3):297–308
Wai RJ, Lee JD (2008) Adaptive fuzzy-neural-network control for maglev transportation system. IEEE Trans Neural Netw 19(1):54–70
Wang LX (1997) A course in fuzzy systems and control. Prentice-Hall, Upper Saddle River
Wang WY, Leu YG, Hsu CC (2001) Robust adaptive fuzzy-neural control of nonlinear dynamical systems using generalized projection update law and variable structure controller. IEEE Trans Syst Man Cybern 31(1):140–147
Wang CH, Liu HL, Lin TC (2002) Direct adaptive fuzzy-neural control with state observer and supervisory controller for unknown nonlinear dynamical systems. IEEE Trans Fuzzy Syst 10(1):39–49
Yang ZJ, Tateishi M (July 2001) Adaptive robust nonlinear control of a MLS. Automatica 37(7):1125–1131
Zergeroglu E, Dawson DM, Walker I, Behal A (2000) Nonlinear tracking control of kinematically redundant robot manipulators. In: Proceedings of the American control conference, vol 4
Zhang TP, Ge SS (2006) Improved direct adaptive fuzzy control for a class of MIMO nonlinear systems. In: Proceedings of the world congress on intelligent control and automation, vol 1, pp 3857–3861
Zhang TP, Yi Y (2007) Adaptive fuzzy control for a class of MIMO nonlinear systems with unknown dead-zones. Acta Autom Sinica 33:96–99
Acknowledgments
The author would like to acknowledge the financial support of the MITS, Rajasthan, India for the experimental testing of the MLS and express his gratitude to the reviewers for their valuable comments.
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Appendices
Appendix A
Figures 17 and 18 shows the parameters adaptation of the state matrix for the first rule with the initial conditions [0.002 0 0].
Appendix B
Figures 19 and 20 shows the parameters adaptation of the input matrix for the first rule with the initial conditions [0.002 0 0].
From the Figs. 17, 18, 19 and 20, it is observed that if state matrix \(A\) and input matrix \(B\) are adapted at time \(t\), then the feedback gain matrix \(F\) is also adapted and consequently estimated state track the measured state as \(t\). The convergence of the adaptive parameters of matrices \(A\) and \(B\) for Adaptive Control are shown in these plots.
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Kole, A. Design and stability analysis of adaptive fuzzy feedback controller for nonlinear systems by Takagi–Sugeno model-based adaptation scheme. Soft Comput 19, 1747–1763 (2015). https://doi.org/10.1007/s00500-014-1362-1
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DOI: https://doi.org/10.1007/s00500-014-1362-1