Data-driven neuro-optimal tracking control of ozone generation process based on adaptive dynamic programming

Abstract

Ozone is considered as one of the strongest oxidizing agent, yet it leaves no residues that are harmful to global environment. In this paper, the close loop control of ozone generator has been studied. The main concern of this issue is to achieve desired ozone concentration. Due to the ozone generation process is a complex nonlinear multivariable system, which is difficult to model and regulate, thus a date-driven neuro-control method is adopted to construct the dynamics of the system, and the adaptive dynamic programming algorithm(ADP) is used for controller design and optimization. According to the hardware-in-loop simulation, the ozone generation process can be effectively approximated by the neuro-network model, and the concentration and flow rate of ozone can be tracked by the ADP controller.