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This study proposes an adaptive neuro-fuzzy inference system (ANFIS)-based rotor position controller for brushless direct current (BLDC) motors to improve the control performance of the drive under transient and steady-state conditions. The dynamic response of a BLDC motor to the proposed ANFIS controller is considered as standard reference input. The effectiveness of the proposed controller is compared with that of the proportional integral derivative (PID) controller and fuzzy PID controller. The proposed controller solves the problem of nonlinearities and uncertainties caused by the reference input changes of BLDC motors and guarantees a fast and accurate dynamic response with an outstanding steady-state performance. Furthermore, the ANFIS controller provides low torque ripples and high starting torque. The detailed study includes a MATLAB-based simulation and an experimental prototype to illustrate the feasibility of the proposed topology.

In this paper, a robust control system with the sliding mode fuzzy controller and the additional PID compensator (PIDSMFC) is presented. The additional compensator relaying on the sliding mode theory is used to improve the dynamical characteristics of the drive system. Sliding mode fuzzy controller is studied because of its model free, stable and high performance. In this method, using high control gain to overcome uncertainties lead to chattering phenomena in control law which can excite unmodeled dynamics and may harm the plant. Diff erent approaches, such as intelligent methods are used to abate these drawbacks. In order to enhance the sliding mode controller performance, fuzzy logic is used here. For this purpose, a PID outer loop is used in the control law then the gains of the sliding term and PID term are tuned online by afuzzy system. Hence, the chattering is avoided and response of the system is improved against external load torque here. Presented simulation results confirm the above claims and demonstrate the performance improvement in this case.

This paper presents the development and performance analysis of intelligent control techniques such as Sliding Mode controller and Fuzzy logic Controller for Brushless DC (BLDC)motor drives. Today, strong mathematical tools used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance. One of the best nonlinear robust controller which can be used in uncertainty nonlinear systems, are sliding mode controller but pure sliding mode controller has some disadvantages such as nonlinear dynamic uncertainties therefore to design model free sliding mode controller this research focuses on applied fuzzy logic controller in sliding mode controller. One of the most important challenging in pure sliding mode controller and sliding mode fuzzy controller is sliding surface slope coefficient therefore the second target in this research is design a supervisory controller to adjusting the sliding surface slope in sliding mode fuzzy controller. General TermsIntelligent Control, Mathematical Model,IGBT power inverter, voltage and speed sensing circuits, and digital signal processor,sliding surface slope.