Control of system's temperature is one of the active research areas in field of energy consumption. In this research we have following objectives: temperature data collection from system, intelligent system identification based on neuro-fuzzy Auto Regressive eXternal model input (ARX) methodology and design a nonlinear controller to fixed a temperature and improve the energy efficiency. To control the system's temperature, data collection and data analysis are two most important factors. After data collection, system identification plays an important role to control systems. Neuro-fuzzy ARX is one of the significant method to system modeling. In this research, the number of training data is less than 100 samples. To control of system's temperature, Pulse Width Modulator (PWM) is used by the nonlinear model free controller to fixed system's temperature. However tuning the system's temperature is extremely important but the other important factors is the rate of tuning the temperature. Nonlinear model free robust functional based is used in this research.
System identification is one of the main challenges in real time control. To design the best controller for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. The second important challenge in the field of control theory is, design high-performance controller. To improve the performance of controller, two factors are very important: 1) high performance mathematical or intelligent modeling, 2) chose the best controller for the system. This paper has two main objectives: after data collection from position motor from industry the first objective is modeling and system identification based on Auto-Regressive with eXternal model input (ARX) and defined Z-function and Sfunction and the second objective is; design the high-performance controller to have the minimum rise time and error.
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