Adaptive control for a class of chemical reactor systems in discrete-time form

作者：Li Dong Juan^*; Tang Li

来源：Neural Computing & Applications, 2014, 24(7-8): 1807-1814.

DOI：10.1007/s00521-013-1420-0

• Discrete-time system
• CSTR control
• Adaptive predictive control
• The neural networks
• Nonlinear systems

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.

• 出版日期2014-6
• 单位辽宁工业大学