Abstract
In the chapter, a number of intelligent control approaches have been investigated. First, the modeling and control method using the Least Squares Support Vector Machine (LS-SVM) have been utilized to design efficient model free control. Then, we further study the universal functional approximation of fuzzy logic and neural networks. All these intelligent control methods employ a systematic online adaptation mechanism without prepared off line learning.
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References
Anupoju, C.M., Su, C.Y., Oya, M.: Adaptive motion tracking control of uncertain nonholonomic mechanical systems including actuator dynamics. IEE Proc., Control Theory Appl. 152(5), 575–580 (2005)
Bi, D., Li, Y.F., Tso, S.K., Wang, G.L.: Friction modeling and compensation for haptic display based on support vector machine. IEEE Trans. Ind. Electron. 51(2), 491–500 (2004)
Chang, Y.C., Chen, B.S.: Robust tracking designs for both holonomic and nonholonomic constrained mechanical systems: adaptive fuzzy approach. IEEE Trans. Fuzzy Syst. 8, 46–66 (2000)
Dong, W., Xu, Y., Huo, W.: Trajectory tracking control of dynamics nonholonomic systems with unknown dynamics. Int. J. Robust Nonlinear Control 9, 905–922 (1999)
Ge, S.S., Wang, C.: Adaptive neural control of uncertain MIMO nonlinear systems. IEEE Trans. Neural Netw. 15(3), 674–692 (2004)
Ge, S.S., Zhang, J.: Neural network control of nonaffine nonlinear system with zero dynamics by state and output feedback. IEEE Trans. Neural Netw. 14(4), 900–918 (2003)
Ge, S.S., Lee, T.H., Harris, C.J.: Adaptive Neural Network Control of Robot Manipulators. World Scientific, London (1998)
Ge, S.S., Hang, C.C., Zhang, T.: Adaptive neural network control of nonlinear systems by state and output feedback. IEEE Trans. Syst. Man Cybern., Part B 29(6), 818–828 (1999)
Ge, S.S., Hang, C.C., Lee, T.H., Zhang, T.: Stable Adaptive Neural Network Control. Kluwer Academic, Norwell (2001)
Ge, S.S., Yang, C., Lee, T.H.: Adaptive predictive control using neural network for a class of pure-feedback systems in discrete-time. IEEE Trans. Neural Netw. 19(9), 1599–1614 (2008)
Hahn, W.: Stability of Motion. Springer, Berlin (1967)
Han, H., Su, C., Stepanenko, Y.: Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators. IEEE Trans. Fuzzy Syst. 9(2), 315–323 (2001)
Hovakimyan, N., Nardi, F., Calise, A.J.: A novel error observer-based adaptive output feedback approach for control of uncertain systems. IEEE Trans. Autom. Control 47(8), 1310–1314 (2002)
Karnopp, D.: Computer simulation of stick-slip friction in mechanical dynamic systems. ASME J. Dyn. Syst. Meas. Control 107, 100–103 (1985)
Lewis, F.L., Yesildirek, A., Liu, K.: Multilayer neural network robot controller with guaranteed tracking performance. IEEE Trans. Neural Netw. 7(2), 388–399 (1996)
Li, Z., Gu, J., Ming, A., Xu, C., Shimojo, M.: Intelligent complaint force/motion control of nonholonomic mobile manipulator working on the non-rigid surface. Neural Comput. Appl. 15(3–4), 204–216 (2006)
Li, Z., Yang, C., Gu, J.: Neuro-adaptive compliant force/motion control for uncertain constrained wheeled mobile manipulator. Int. J. Robot. Autom. 22(3), 206–214 (2007)
Li, Z., Chen, W., Luo, J.: Adaptive compliant force–motion control of coordinated nonholonomic mobile manipulators interacting with unknown non-rigid environments. Neurocomputing 71(7–9), 1330–1344 (2008)
Lin, W., Qian, C.: Adding one power integrator: a tool for global stabilization of high-order cascade nonlinear systems. Syst. Control Lett. 39, 339–351 (2000)
Loreto, G., Garrido, R.: Stable neurovisual servoing for robot manipulators. IEEE Trans. Neural Netw. 17(4), 953–965 (2006)
Lu, G., Song, J., Hua, L., Sun, C.: Inverse system control of nonlinear systems using LS-SVM. In: Proceedings of the 26th Chinese Control Conference, China, 2007, pp. 233–236 (2007)
Munkres, J.R.: Analysis on Manifolds. Addison-Wesley, Reading (1991)
Suykens, J.A.K., Vandewalle, J., Moor, B.D.: Optimal control by least squares support vector machines. Neural Netw. 14(1), 23–35 (2001)
Vapnik, V.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 955–999 (1999)
Vapnik, V.N.: Statistical Learning Theory. Springer, New York (1998)
Wang, G.L., Li, Y.F., Bi, D.X.: Support vector machine networks for friction modeling. IEEE/ASME Trans. Mechatron. 9(3), 601–606 (2004)
Wang, J., Chen, Q., Chen, Y.: RBF kernel based support vector machine with universal approximation and its application. In: Support Vector Machines, Part III. Lecture Notes in Computer Science, vol. 3173, pp. 512–517 (2004)
Wang, L.: Adaptive Fuzzy Systems and Control, Design, and Stability Analysis. Prentice Hall, Englewood Cliffs (1994)
Xu, J., Chen, S.: Adaptive control of a class of nonlinear discrete-time systems using support vector machine. In: Proceedings of the 5th World Congress on Intelligent Control and Automation, China, 2004, pp. 440–443 (2004)
Yang, C., Ge, S.S., Xiang, C., Chai, T.Y., Lee, T.H.: Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach. IEEE Trans. Neural Netw. 19(11), 873–1886 (2008)
Zhang, H.R., Wang, X.D., Zhang, C.J., Cai, X.S.: Robust identification of non-linear dynamic systems using support vector machine. IEE Proc. Sci. Meas. Technol. 153(3), 125–129 (2006)
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Li, Z., Yang, C., Fan, L. (2013). Intelligent Control. In: Advanced Control of Wheeled Inverted Pendulum Systems. Springer, London. https://doi.org/10.1007/978-1-4471-2963-9_7
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DOI: https://doi.org/10.1007/978-1-4471-2963-9_7
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