A robust neural fuzzy petri net controller for a temperature control system

Temperature control systems have the characteristics of non-linearity, large inertia, and time variance. It is difficult to overcome the effects of these factors and get a reasonable results with the use of conventional controllers such as PID. So, temperature control by a robust neural fuzzy Petri net (RNFPN) controller based on an indirect forward control structure is proposed in this paper. After offline learning to get the initial weights, the RNFPN is online constructed by concurrent structure/parameter learning. The RNFPN has many advantages when applied to temperature control plants such as: high learning ability which reduces the controller training time, no a priori knowledge of the plant is required which simplifies the design task, and lastly the high control performance. The simulation results showed that the RNFPN intelligent controller has a reasonable robustness against disturbance, rapidity and good dynamic performance.