Neural dynamics for control of industrial agitator tank with rapid convergence and perturbations rejection

Abstract

The industrial agitator tank is a widely used equipment in the chemical industry for the production of the chemical reagents. The high-performance agitator tank controller is critical to increase its productivity. In this paper, we propose an agitator tank controller based on a neural dynamics method with a shorter error-converging time in comparison with the existing methods. In addition, the controller also has a strong capability to reject perturbations. Furthermore, the superiority of the proposed agitator tank controller is theoretically analyzed. Ultimately, computer simulations synthesized by the proposed agitator tank controller are conducted. The numerical results validate the superior performance of the proposed controller.