Adaptive neural network control for a two continuously stirred tank reactor with output constraints

An adaptive control scheme is studied for a class of continuous stirred tank reactors (CSTR). The considered reactors can be viewed as a class of MIMO systems with unknown functions and interconnections as well as the output constraints. These properties of the reactors will lead to a completed task for designing a stable control algorithm. To this end, several unknown functions are approximated based on the neural approximation, a novel recursive design method is used to remove the interconnection term, and Barrier Lyapunov function is introduced to avoid the violation of the output constraints. The stability of the proposed scheme is proved based on the Lyapunov analysis method. A simulation example for continuous stirred tank reactor is illustrated to verify the validity of the algorithm.