Abstract
An adaptive control algorithm is applied to controlling a class of SISO continuous stirred tank reactor (CSTR) system in discrete-time. The considered systems belong to pure-feedback form where the unknown dead-zone and it is first to control this class of systems. Radial basis function neural networks (RBFNN) are used to approximate the unknown functions and the mean value theorem is exploited in the design. Based on the Lyapunov analysis method, it is proven that all the signals of the resulting closed-loop system are guaranteed to be semi-global uniformly ultimately bounded (SGUUB) and the tracking error can be reduced to a small compact set. A simulation example is studied to verify the effectiveness of the approach.
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References
Aguilar, R.: Sliding-mode observer for uncertainty estimation in a class of chemical reactor: A differential-algebraic approach. Chemical Product and Process Modeling 2, 1934–2689 (2007)
Ge, S.S., Wang, C.: Adaptive NN of uncertain nonlinear pure-feedback systems. Automatica 38, 671–682 (2002)
Liu, Y.J., Wang, W.: Adaptive Fuzzy Control for a Class of Uncertain Nonaffine Nonlinear Systems. Information Sciences 177, 3901–3917 (2007)
Zhang, H.G., Luo, Y.H., Liu, D.R.: Neural network-based near-optimal control for a class of discrete-time affine nonlinear systems with control constraint. IEEE Transactions on Neural Networks 20, 1490–1503 (2009)
Liu, Y.J., Wang, W., Tong, S.C., Liu, Y.S.: Robust Adaptive Tracking Control for Nonlinear Systems Based on Bounds of Fuzzy Approximation Parameters. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 40, 170–184 (2010)
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 (1999)
Zhang, H.G., Cai, L.L.: Nonlinear adaptive control using the Fourier integral and its application to CSTR systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 32, 367–372 (2002)
Salehi, S., Shahrokhi, M.: Adaptive fuzzy backstepping approach for temperature control of continuous stirred tank reactors. Fuzzy Sets and Systems 160, 1804–1818 (2009)
Ge, S.S., Yang, C.G., Lee, T.H.: Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time. IEEE Trans. Neural Netw. 19, 1599–1614 (2008)
Chen, M., Ge, S.S., Ren, B.B.: Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems with Input Nonlinearities. IEEE Transactions on Neural Networks 21, 796–812 (2010)
Tong, S.C., Li, Y.M.: Adaptive Fuzzy Output Feedback Tracking Backstepping Control of Strict-Feedback Nonlinear Systems With Unknown Dead Zones. IEEE Transactions on Fuzzy Systems 20, 168–180 (2012)
Deolia, V.K., Purwar, S., Sharma, T.N.: Backstepping Control of Discrete-Time Nonlinear System Under Unknown Dead-zone Constraint. In: International Conference on Communication Systems and Network Technologies, pp. 344–349 (2011)
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Li, DJ., Tang, L. (2013). Adaptive NN Control for a Class of Chemical Reactor Systems. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_20
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DOI: https://doi.org/10.1007/978-3-642-39068-5_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39067-8
Online ISBN: 978-3-642-39068-5
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