Embedded Indirect Adaptive Fuzzy Controller Based on T S Fuzzy Inverse Model
The present paper is a trial to shed further light on the Indirect Adaptive Fuzzy Logic Controller (IDAFLC) In this concern, the proposed technique is predestined from two levels, where the lower level is based on Mamdani fuzzy controller. On the other hand, the upper level is an inverse model based on a Takagi Sugeno method, in which its output is used to adapt the parameters of the fuzzy controller in the lower level. Moreover, the upper level contains learning mechanism to adapt model identification parameters. The proposed IDAFLC is implemented using an Arduino DUE kit. From the practical results, it is proved that the proposed adaptive controller has the ability to adapt the model identification parameters and improves, successfully, both the performance response and the disturbance due to the load and also measurement error of sensor in the speed control of the DC motor.