Neural network control for a class of continuous stirred tank reactor process with dead-zone input

An adaptive control scheme is studied for a class of continuous stirred tank reactors (CSTR) with unknown functions. Because the nonlinear property and the unknown functions are included in the considered reactor, it leads to a completed task for designing the controller. Based on the approximation property of the neural networks, several unknown functions are approximated. The main contribution of this paper is that a more general class of CSTR is controlled. A novel recursive design method is used to remove the interconnection term. It is proven that the proposed algorithm can guarantee that all the signals in the closed-loop system are bounded and the system output can converge to a neighborhood of zero based on the Lyapunov analysis method. A simulation example for continuous stirred tank reactor is illustrated to verify the validity of the algorithm.