Intelligent fuzzy sliding mode controller based on FPGA for the speed control of a BLDC motor

Abstract

Brushless DC (BLDC) motors are one of the most widely used motors for various industrial applications due to their high efficiency, high torque to weight ratio and elimination of mechanical commutator. These motors operate in wide range of speeds and necessitate precise speed control techniques, for their nonlinear model, insenseitive to parameter variations and external disturbances, when used in various sensitive applications. Conventional PI and other existing controllers produce high overshoot and increased rise time and settling time. The performance of BLDC motor is enhanced using a Fuzzy Sliding Mode Controller (FSMC) whose gain is intelligently varied with the help of a Fuzzy Inference System (FIS). For this purpose, a suitable FSMC is designed, simulated and implemented using FPGA. The simulation results are validated using Hardware in the loop (HIL) simulation as well as actual hardware implementation. Great improvement in the transient performance is achieved when compared to chatter free SMC, Fuzzy PI and conventional PI controller.

Development of position tracking electric drive system to control BLDC motor working in very low mode for industrial machine application

“…The first two terms of (3) determine the unvalued property of the function 𝐹 𝐶𝑇 (𝜔) Figure 2(a). the third term takes into account the characteristic of 𝐹 𝐶.𝑇3 (𝜔), it depends on direction and speed of the actuating motor shaft [6], [7]. When the motor shaft stops (𝜔 = 0) the resistance torque in (2) is determined by the static frictional torque, which takes the value in the range:…”

“…Change characteristics of 𝑀 𝐶.𝑇 (0) Figure 2(b). When changing the direction of motion through the value 0 specified by the (7).…”

“…The tracking electric drive system works in slow mode (the engine rotation speed is very low and corresponds to a small setting angle, there is always a different variation) this is a complex system that requires a traction output signal. closely follow the input signal with a small amount of error, when the input signal has variable error [5]- [7]. We always want this error to be as small as possible, depending on the requirements of the control device of the electric drive system [8], [9].…”

Adaptive fuzzy sliding mode controller design for PMLSM position control

“…The most known solutions are those which propose to replace the signum function by a saturation function or a hyperbolic tangent [18]. However, we must find a compromise between the tracking error and the thickness of the boundary layer [6] , [20], [25], [26]. In order to overcome the disadvantages of the SMC controller and to ensure good responses in closed loop control with high possible attenuation of the chattering phenomenon, we propose in this section a combination approach between the Fuzzy logic and SMC.…”

Research on the Control Method of a Brushless DC Motor Based on Second-Order Active Disturbance Rejection Control

“…Moreover, [15] demonstrated the combination of fuzzy logic and fuzzy logic to improve response speed and disturbance resistance. The approach in [16] presented the use of a fuzzy control strategy to adjust the parameters of a sliding mode controller based on an FPGA controller, thereby improving the control performance of BLDC motors.…”