Adaptive control for a class of chemical reactor systems in discrete-time form
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In this paper, an adaptive predictive control algorithm is applied to control a class of SISO continuous stirred tank reactor (CSTR) system in discrete time. The main contribution of the paper is that the considered systems belong to pure-feedback form where the unknown dead-zone is considered in the in-fan, and dead-zone is nonsymmetric, and it is first to control this class of systems. Radial basis function neural networks 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, and the tracking error can be reduced to a small compact set. A simulation example for CSTR systems is studied to verify the effectiveness of the proposed approach.
KeywordsDiscrete-time system CSTR control Adaptive predictive control The neural networks Nonlinear systems
This work was supported by the Natural Science Foundation of China [Grant Nos. 61071014 and 61104017] and Program for Liaoning Innovative Research Team in University [Grant No. LT2012013]; the Program for Liaoning Excellent Talents in University [Grant No. LJQ2011064]; Liaoning Bai QianWan Talent Program [Grant No. 2012921055]; The Foundation of Educational Department of Liaoning Province [Grant No. L2013].
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