Abstract
An adaptive neural control approach based on backstepping is presented. Unmodelled dynamics or external disturbances are considered in the nonlinear models. Approximating nonlinear dynamic is one of the performances of multi-layer feedforward neural networks. By the Lyapunov’s stability theory, the NN weights are turned on-line with no prior training needed. An important feature of the presented approach is that by modifying a novel quasi-weighted Lyapunov function, the possible control singularity in the design of NN adaptive controller is avoided effectively. Finally, the approach is applied in CSTR system. The simulation result is given to demonstrate the feasibility and effectiveness of the proposed method.
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References
Kanellakopoulos, L., Kokotovic, P.V., Morse, A.S.: Systematic Design of Adaptive Controllers for Feedback Linearizable Systems. IEEE Transactions on Automatic Control 36, 1241–1253 (1991)
Krstic, M., Kanellakopoulos, I., Kokotovic, P.V.: Adaptive Nonlinear Control without Overparametrization. Systems & Control Letters 19, 177–185 (1992)
Polycarpou, M.M., Ioannou, P.A.: A Robust Adaptive Nonlinear Control Design. Automatica 32, 423–427 (1996)
Yao, B., Tomizuka, M.: Adaptive Robust Control of Nonlinear Systems in a Semi-Strict Feedback Form. Automatica 33, 893–900 (1997)
Zhang, T., Ge, S.S., Hang, C.C.: Adaptive Neural Network Control for Strict-Feedback Nonlinear Systems Using Backstepping Design. Automatica 36, 1835–1846 (2000)
Wang, D., Huang, J.: Adaptive Neural Network Control for a Class of Uncertain Nonlinear Systems in Pure-Feedback Form. Automatica 38, 1365–1372 (2002)
Hornik, K.: Approximation Capabilities of Multilayer Feed-Forward Networks. Neural Networks 4, 251–257 (1991)
Kwan, C., Lewis, F.L., Dawson, D.M.: Robust Neural-Network Control of Rigid-Link Electrically Driven Robots. IEEE Transactions on Neural Networks 9, 581–588 (1998)
Polycarpou, M.M.: Stable Adaptive Neural Control Scheme for Nonlinear Systems. IEEE Transactions on Automatic Control 41, 447–451 (1996)
Ge, S.S., Hang, C.C., Zhang, T.: A Direct Adaptive Controller for Dynamic Systems with a Class of Nonlinear Parameterizations. Automatica 35, 741–747 (1999)
Ge, S.S., Hang, C.C., Zhang, T.: Nonlinear Adaptive Control Using Neural Networks and its Application to CSTR Systems. Journal of Process Control 9, 313–323 (1998)
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© 2005 Springer-Verlag Berlin Heidelberg
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Yu, Z., Wang, Q. (2005). Neural Adaptive Singularity-Free Control by Backstepping for Uncertain Nonlinear Systems. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_15
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DOI: https://doi.org/10.1007/11427469_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
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