In this chapter, two schemes for trajectory tracking based on the backstepping and the block control techniques, respectively, are proposed, using an RHONO. This observer is based on a discrete-time recurrent high-order neural network (RHONN), which estimates the state of the unknown plant dynamics. The learning algorithm for the RHONN is based on an EKF. Once the neural network structure is determined, the backstepping and the block control techniques are used to develop the corresponding trajectory tracking controllers. The respective stability analyzes, using the Lyapunov approach, for the neural observer trained with the EKF and the controllers are included. Finally, the applicability of the proposed design is illustrated by an example: output trajectory tracking for an induction motor.
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References
G. Chen and J. L. Moiola, An overview of bifurcation, chaos and nonlinear dynamics in control systems, The Franklin Institute Journal, 331B, 819–858, 1994
R. A. Felix, E. N. Sanchez, and A. G. Loukianov, Avoiding controller singularities in adaptive recurrent neural control, Proceedings IFAC’05, Prague, Czech Republic, July 2005
S. S. Ge, T. H. Lee, and C. J. Harris, Adaptive Neural Network Control for Robotic Manipulators, World Scientific, Singapore, 1998
H. Khalil, Nonlinear Systems, 2nd ed., Prentice Hall, Upper Saddle River, NJ, USA, 1996
F. Khorrami, P. Krishnamurthy, and H. Melkote, Modeling and Adaptive Nonlinear Control of Electric Motors, Springer, Berlin Hiedelberg New York, 2003
M. Krstic, I. Kanellakopoulos, and P. Kokotovic, Nonlinear and Adaptive Control Design, Wiley, New York, USA, 1995
W. Lin and C. I. Byrnes, Design of discrete-time nonlinear control systems via smooth feedback, IEEE Transactions on Automatic Control, 39(11), 2340–2346, 1994
A. G. Loukianov, J. Rivera, and J. M. Cañedo, Discrete-time sliding mode control of an induction motor, Proceedings IFAC’02, Barcelone, Spain, July 2002
E. N. Sanchez, A. Y. Alanis, and G. Chen, Recurrent neural networks trained with Kalman filtering for discrete chaos reconstruction, Dynamics of Continuous, Discrete and Impulsive Systems Series B, 13, 1–18, 2006
E. N. Sanchez and L. J. Ricalde, Trajectory tracking via adaptive recurrent neural control with input saturation, Proceedings of International Joint Conference on Neural Networks’03, Portland, Oregon, USA, July 2003
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(2008). Discrete-Time Output Trajectory Tracking. In: Discrete-Time High Order Neural Control. Studies in Computational Intelligence, vol 112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78289-6_6
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DOI: https://doi.org/10.1007/978-3-540-78289-6_6
Publisher Name: Springer, Berlin, Heidelberg
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