Adaptive neural network control for continuous stirred tank reactor process

In this paper, an adaptive control algorithm is proposed for a two continuous stirred tank reactor (CSTR) with unknown functions based on the approximation property of the neural networks. Because the considered reactor contains the nonlinear property and the unknown functions are included in the subsystem, it is a completed system and is very difficult to be controlled. In order to avoid the difficulties, novel recursive design method is used to remove the interconnection term and special approximated functions are defined to be approximated by using the neural networks. Using the Lyapunov stability analysis method, the proposed algorithm ensures that all the signals in the closed-loop system are bounded and the system output can converge to a neighborhood of zero.