In this study, fuzzy supervised online coactive neuro-fuzzy inference system (CANFIS)-based rotor position controller is presented for brushless DC (BLDC) motor. An online learning algorithm is employed for updating premises and consequent parameters of the CANFIS controller. The rotor position control of BLDC motor is simulated using MATLAB/Simulink Toolbox. The dynamic response of the BLDC motor with proposed controller is measured for standard sinusoidal reference input. The effectiveness of the proposed controller performance is compared with fuzzy proportional-integral derivative controller, adaptive neuro-fuzzy inference system controller and supervised recurrent fuzzy neural network controller. The proposed controller is able to solve the problem of non-linearities and uncertainty due to reference input changes of BLDC motor and guarantees fast and accurate dynamic response to a remarkable steady-state performance. Also, experimental hardware results are presented to demonstrate the validity and effectiveness of the proposed control scheme using field programmable gate array chip. Experimental results show that the proposed control scheme can achieve a more favourable tracking performance without the chattering phenomena in the control effort.
Continuous stirred tank reactor (CSTR) is a highly nonlinear process particularly when chemical reaction takes place. The heat energy will be either liberate or absorbed by the reactor due to the reaction. The control of temperature for this process is a real challenge due to nonlinear temperature changes during reaction. In this paper a mathematical model of CSTR with its transfer function is taken for controller design and analysis. The temperature inside the reactor is controlled by altering the coolant jacket temperature. This paper compares the performances of the proportional integral derivative controller (PID) controller, PID-based nonlinear autoregressive moving average (NARMA) controller and fuzzy-based PID controller. The proposed PID-based NARMA controller shows better control of temperature than the conventional PID controller. The fuzzy-based PID controller also shows a reasonable optimal performance.
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