Abstract
In this paper, the control problem for Continuously Stirred Tank Reactor (CSTR) systems with input nonlinearity and unknown disturbances is addressed. To ensure constraints satisfaction on the input and the output of CSTR we employ a system transformation technique to transform the original constrained model of CSTR into an equivalent unconstrained model, whose stability is sufficient to achieve the tracking control of the original CSTR system with a priori prescribed performance. In the proposed control methods, NNs are employed to approximate the unknown dynamics of CSTR and additional adaptive compensators are introduced to cope with NNs approximation errors and external disturbance; a robust control term is introduced to overcome the effects of the unknown input dead-zone. The proposed adaptive neural tracking controllers are designed with only one adaptive parameter by using the second Lyapunov stability method. In comparison with the traditional back-stepping based techniques, usually used in CSTR control, the structures of the proposed controllers are much simpler with few design parameters since the causes for the problem of complexity growing are completely eliminated. Simulation results are presented to show the effectiveness of the proposed controllers.
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Nassira, Z., Mohamed, C. (2021). Adaptive Neural-Network Control Design for Uncertain CSTR System with Unknown Input Dead-Zone and Output Constraint. In: Bououden, S., Chadli, M., Ziani, S., Zelinka, I. (eds) Proceedings of the 4th International Conference on Electrical Engineering and Control Applications. ICEECA 2019. Lecture Notes in Electrical Engineering, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-15-6403-1_33
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DOI: https://doi.org/10.1007/978-981-15-6403-1_33
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