Adaptive NN Control for a Class of Chemical Reactor Systems
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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.
KeywordsDiscrete-time system CSTR control adaptive predictive control the neural networks nonlinear systems
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- 12.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)Google Scholar