Multiple models and neural networks based adaptive PID decoupling control of mine main fan switchover system

Abstract

Mine main fan switchover system (MMFSS) consists of two main fans, which are equipped with a horizontal air door and a vertical air door, respectively. The purpose of operating MMFSS is to ensure small fluctuation of the underground airflow quantity and to guarantee safe operations of both fans simultaneously by controlling the four air doors during switchover from working to standby fan. MMFSS has time-varying dynamics under different operating conditions, strong coupling, high non-linearities and uncertainty in character, applying conventional control methods can not lead to satisfactory performances. In this study, an adaptive intelligent decoupling proportional–integral–derivative (PID) control method is proposed, where the unmodelled dynamics are estimated and compensated by neural networks, the coupling effect is eliminated by the designed decoupling compensator, and a switching mechanism among multiple models is employed to deal with the effect of time-varying dynamics. By inspiring from the generalised minimum variance control law concept, the parameters of the developed controller are determined. The stability of the close-loop system and the convergence of tracking error are examined. Finally, simulations of MMFSS show the feasibility and effectiveness of the obtained results.