Data-Driven Neuro-Optimal Tracking Control of Ozone production Based on Adaptive Dynamic Programming

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 production. Due to the ozone generation process is a complex nonlinear multivariable system, which is different to model and regulate, thus a date-driven neuro-control method is adopted to construct the dynamics of the system, Adaptive dynamic programming (ADP) with input constraints for controller design and optimization. According to the hardwarein-loop simulation, the ozone generation process can be effectively approximated by the neuro-network model, and production of ozone can be tracked by the ADP controller. The simulation results show that it is superior to the control without input constraints.