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Adaptive Fuzzy Logic Control for a Class of Unknown Nonlinear Dynamic Systems with Guaranteed Stability

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Abstract

In this paper, a stability analysis is suggested for adaptive fuzzy logic systems (FLSs) without the requirement of states measurement or estimation. Fuzzy logic is viewed as a powerful tool in providing accurate approximation of systems with uncertainties. The proposed methodology exploits the power of adaptive control theory to find a Lyapunov-based adaptation law for FLSs. As such, both stability and tracking problems are addressed for a class of nonlinear dynamic systems. The proposed method yields reduced complexity with respect to many adaptive FLSs available in the literature. In addition, the use of an observer to estimate immeasurable states is not required as in other methods. First, a stability analysis is presented for adaptive control. Then, results are extended to adaptive FLSs with unknown dynamics. A numeric illustrative example highlights the implementation details and the performance of the suggested scheme.

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References

  • Ali, M. H., Murata, T., & Tamura, J. (2008). Influence of communication delay on the performance of fuzzy logic-controlled braking resistor against transient stability. IEEE Transactions on Control Systems Technology, 16(6), 1232–1241.

    Article  Google Scholar 

  • Aliev, R. A., & Pedrycz, W. (2009). Fundamentals of a fuzzy-logic-based generalized theory of stability. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 39(4), 971–988.

    Article  Google Scholar 

  • Armstrong, B., & de Wit, C. C. (1996). Friction modeling and compensation. In W. S. Levine (Ed.), The control handbook. Electrical engineering handbook (Vol. 77, pp. 1369–1382). Boca Raton, FL: CRC Press.

  • Barkat, S., Tlemcani, A., & Nouri, H. (2011). Noninteracting adaptive control of PMSM using interval type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems, 19(5), 925–936.

    Article  Google Scholar 

  • Biglarbegian, M., Melek, W. W., & Mendel, J. M. (2010). On the stability of interval type-2 TSK fuzzy logic control systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 40(3), 798–818.

    Article  Google Scholar 

  • Boulkroune, A., M’Saad, M., & Farza, M. (2011). Adaptive fuzzy controller for multivariable nonlinear state time-varying delay systems subject to input nonlinearities. Fuzzy Sets and Systems, 164(1), 45–65.

    Article  MATH  MathSciNet  Google Scholar 

  • Chang, Y. H., Chang, C. W., Taur, J. S., & Tao, C. W. (2009). Fuzzy swing-up and fuzzy sliding-mode balance control for a planetary-gear-type inverted pendulum. IEEE Transactions on Industrial Electronics, 59(9), 3751–3761.

    Article  Google Scholar 

  • Chaoui, H., & Gueaieb, W. (2008). Type-2 fuzzy logic control of a flexible-joint manipulator. Journal of Intelligent and Robotic Systems, 51(2), 159–186.

    Article  Google Scholar 

  • Chaoui, H., Gueaieb, W., Biglarbegian, M., & Yagoub, M. (2013). Computationally efficient adaptive type-2 fuzzy control of flexible-joint manipulators. Robotics, 2(2), 66–91.

    Article  Google Scholar 

  • Chaoui, H., & Sicard, P. (2012). Adaptive control of permanent magnet synchronous machines with disturbance estimation. Journal of Control Theory and Applications, 10(3), 337–343.

    Article  MathSciNet  Google Scholar 

  • Chaoui, H., Sicard, P., & Gueaieb, W. (2009). ANN-based adaptive control of robotic manipulators with friction and joint elasticity. IEEE Transactions on Industrial Electronics. doi:10.1109/TIE.2009.2024657.

  • Chaoui, H., Sicard, P., & Ndjana, H. (2010) Adaptive state of charge (SOC) estimation for batteries with parametric uncertainties. In IEEE/ASME Advanced Intelligent Mechatronics International Conference.

  • Chen, C. H., Lin, C. J., & Lin, C. T. (2009). Nonlinear system control using adaptive neural fuzzy networks based on a modified differential evolution. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 39(4), 459–473.

    Article  Google Scholar 

  • de Silva, C. W. (1995). Intelligent Control Fuzzy Logic Applications. Boca Raton: CRC Press.

    MATH  Google Scholar 

  • El-Hawwary, M., Elshafei, A., Emara, H., & Fattah, H. (2006). Adaptive fuzzy control of the inverted pendulum problem. IEEE Transactions on Control Systems Technology, 14(6), 1135–1144.

    Article  Google Scholar 

  • Fukushima, H., Kakue, M., Kon, K., & Matsuno, F. (2013). Transformation control to an inverted pendulum for a mobile robot with wheel-arms using partial linearization and polytopic model set. IEEE Transactions on Robotics, 29(3), 774–783.

    Article  Google Scholar 

  • Ho, T. H., & Ahn, K. K. (2012). Speed control of a hydraulic pressure coupling drive using an adaptive fuzzy sliding-mode control. IEEE/ASME Transactions on Mechatronics, 17(5), 976–986.

    Article  Google Scholar 

  • Hsueh, Y. C., Su, S. F., Tao, C. W., & Hsiao, C. C. (2010). Robust L2-gain compensative control for direct-adaptive fuzzy-control-system Design. IEEE Transactions on Fuzzy Systems, 18(4), 661–673.

    Article  Google Scholar 

  • Huang, C. H., Wang, W. J., & Chiu, C. H. (2011). Design and implementation of fuzzy control on a two-wheel inverted pendulum. IEEE Transactions on Industrial Electronics, 58(7), 2988–3001.

    Article  Google Scholar 

  • Huang, J., Ding, F., Fukuda, T., & Matsuno, T. (2013). Modeling and velocity control for a novel narrow vehicle based on mobile wheeled inverted pendulum. IEEE Transactions on Control Systems Technology, 21(5), 1607–1617.

    Article  Google Scholar 

  • Huang, J., Ri, S., Liu, L., Wang, Y., Kim, J., & Pak, G. (2015). Nonlinear disturbance observer-based dynamic surface control of mobile wheeled inverted pendulum. IEEE Transactions on Control Systems Technology, 23(6), 2400–2407.

    Article  Google Scholar 

  • Jung, S., & Kim, S. (2008). Control experiment of a wheel-driven mobile inverted pendulum using neural network. IEEE Transactions on Control Systems Technology, 16(2), 297–303.

    Article  Google Scholar 

  • Karray, F., & de Silva, C. W. (2004). Soft computing and intelligent systems design, theory, tools and applications. Essex: Addison-Wesley, Pearson Education Limited.

    Google Scholar 

  • Kwon, S., Kim, S., & Yu, J. (2015). Tilting-type balancing mobile robot platform for enhancing lateral stability. IEEE/ASME Transactions on Mechatronics, 20(3), 1470–1481.

    Article  Google Scholar 

  • Li, Z., & Luo, J. (2009). Adaptive robust dynamic balance and motion controls of mobile wheeled inverted pendulums. IEEE Transactions on Control Systems Technology, 17(1), 233–241.

    Article  MathSciNet  Google Scholar 

  • Li, Z., & Yang, C. (2012). Neural-adaptive output feedback control of a class of transportation vehicles based on wheeled inverted pendulum models. IEEE Transactions on Control Systems Technology, 20(6), 1583–1591.

    Article  MathSciNet  Google Scholar 

  • Lin, C. T., & Lee, C. S. G. (1996). Neural fuzzy systems: A neuro-fuzzy synergism to intelligent systems. Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Liu, Y. J., Tong, S., & Chen, C. L. P. (2013). Adaptive fuzzy control via observer design for uncertain nonlinear systems with unmodeled dynamics. IEEE Transactions on Fuzzy Systems, 21(2), 275–288.

    Article  Google Scholar 

  • Muralidharan, V., & Mahindrakar, A. D. (2014). Position stabilization and waypoint tracking control of mobile inverted pendulum robot. IEEE Transactions on Control Systems Technology, 22(6), 2360–2367.

    Article  Google Scholar 

  • Nekoukar, V., & Erfanian, A. (2011). Adaptive fuzzy terminal sliding mode control for a class of MIMO uncertain nonlinear systems. Fuzzy Sets and Systems, 179(1), 34–49.

    Article  MATH  MathSciNet  Google Scholar 

  • Orozco, L. M. L., Lomeli, G. R., Moreno, J. G. R., & Perea, M. T. (2015). Identification inverted pendulum system using multilayer and polynomial neural networks. IEEE Latin America Transactions (Revista IEEE America Latina), 13(5), 1569–1576.

    Article  Google Scholar 

  • Pan, Y., & Er, M. J. (2013). Enhanced adaptive fuzzy control with optimal approximation error convergence. IEEE Transactions on Fuzzy Systems, 21(6), 1123–1132.

    Article  Google Scholar 

  • Pan, Y., Er, M. J., Huang, D., & Wang, Q. (2011). Adaptive fuzzy control with guaranteed convergence of optimal approximation error. IEEE Transactions on Fuzzy Systems, 19(5), 807–818.

    Article  Google Scholar 

  • Park, M. S., & Chwa, D. (2009). Orbital stabilization of inverted-pendulum systems via coupled sliding-mode control. IEEE Transactions on Industrial Electronics, 56(9), 3556–3570.

    Article  Google Scholar 

  • Pathak, K., Franch, J., & Agrawal, S. (2005). Velocity and position control of a wheeled inverted pendulum by partial feedback linearization. IEEE Transactions on Robotics, 21(3), 505–513.

    Article  Google Scholar 

  • Phan, P. A., & Gale, T. (2007). Two-mode adaptive fuzzy control with approximation error estimator. IEEE Transactions on Fuzzy Systems, 15(5), 943–955.

    Article  Google Scholar 

  • Saghafinia, A., Ping, H. W., Uddin, M. N., & Gaeid, K. S. (2015). Adaptive fuzzy sliding-mode control into chattering-free IM drive. IEEE Transactions on Industry Applications, 51(1), 692–701.

    Article  Google Scholar 

  • Santiesteban, R., Floquet, T., Orlov, Y., Riachy, S., & Richard, J. (2007). Second order sliding mode control of underactuated mechanical system II: orbital stabilization of an inverted pendulum with application to swing up/balacing control. International Journal of Robust and Nonlinear Control, 18(4/5), 544–556.

    MATH  Google Scholar 

  • Sharma, K. D., Chatterjee, A., & Rakshit, A. (2010). Design of a hybrid stable adaptive fuzzy controller employing Lyapunov theory and harmony search algorithm. IEEE Transactions on Control Systems Technology, 18(6), 1440–1447.

    Google Scholar 

  • Tao, C., Taur, J., Hsieh, T., & Tsai, C. (2008). Design of a fuzzy controller with fuzzy swing-up and parallel distributed pole assignment schemes for an inverted pendulum and cart system. IEEE Transactions on Control Systems Technology, 16(6), 1277–1288.

    Article  Google Scholar 

  • Tong, S., Wang, T., Li, Y., & Chen, B. (2013). A combined backstepping and stochastic small-gain approach to robust adaptive fuzzy output feedback control. IEEE Transactions on Fuzzy Systems, 21(2), 314–327.

    Article  Google Scholar 

  • Uyanik, I., Morgul, O., & Saranli, U. (2015). Experimental validation of a feed-forward predictor for the spring-loaded inverted pendulum template. IEEE Transactions on robotics, 31(1), 208–216.

    Article  Google Scholar 

  • Wai, R. J., Kuo, M. A., & Lee, J. D. (2008). Design of cascade adaptive fuzzy sliding-mode control for nonlinear two-axis inverted-pendulum servomechanism. IEEE Transactions on Fuzzy Systems, 16(5), 1232–1244.

    Article  Google Scholar 

  • Wang, L. X. (1994). Adaptive fuzzy systems and control: Design and stability analysis. Englewood Cliffs, NJ: PTR Prentice Hall.

    Google Scholar 

  • Xu, J. X., Guo, Z. Q., & Lee, T. H. (2014). Design and implementation of integral sliding-mode control on an underactuated two-wheeled mobile robot. IEEE Transactions on Industrial Electronics, 61(7), 3671–3681.

    Article  Google Scholar 

  • Yang, C., Li, Z., Cui, R., & Xu, B. (2014). Neural network-based motion control of underactuated wheeled inverted pendulum models. IEEE Transactions on Neural Networks and Learning Systems, 25(11), 2004–2016.

    Article  Google Scholar 

  • Yang, C., Li, Z., & Li, J. (2013). Trajectory planning and optimized adaptive control for a class of wheeled inverted pendulum vehicle models. IEEE Transactions on Cybernetics, 43(1), 24–36.

    Article  Google Scholar 

  • Zhang, X. X., Li, H. X., & Li, S. Y. (2008). Analytical study and stability design of a 3-D fuzzy logic controller for spatially distributed dynamic systems. IEEE Transactions on Fuzzy Systems, 16(6), 1613–1625.

    Article  Google Scholar 

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Correspondence to Hicham Chaoui.

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Chaoui, H., Gualous, H. Adaptive Fuzzy Logic Control for a Class of Unknown Nonlinear Dynamic Systems with Guaranteed Stability. J Control Autom Electr Syst 28, 727–736 (2017). https://doi.org/10.1007/s40313-017-0342-y

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