Position Sensorless Control of BLDC Motors Based on Global Fast Terminal Sliding Mode Observer

Abstract

The brushless DC motor (BLDCM) has many advantages. As a result, it is widely used in electric vehicle (EV) drive systems. To improve the reliability of the motor control system, a position sensorless control strategy based on a sliding mode observer (SMO) is proposed. The global fast terminal sliding mode observer (GFTSMO) is proposed to enhance the control performance of the SMO control system. The advantages of the linear sliding mode and the nonsingular terminal sliding mode (NTSM) are combined in the control strategy. The convergence speed of the system state is enhanced. The motor commutation point is obtained with the observation of the back EMF, and the instantaneous torque value of the motor is calculated. Therefore, the position sensorless control of the BLDCM is realized. Experimental results show that the proposed control strategy can improve the convergence speed, dynamic characteristics and robustness of the system.

Experimental study into optimal interdependence of energy-time costs for emptying a dry dock

“…In addition, it is seen than attenuation time of the transition process depends mainly on time constant of the converter filter. It practically does not exceed the value ( ) 6 8 .…”

“…As a consequence, a closed electrical drive usually has backward linkages by these coordinates rather than the motor supply voltage. This statement is equally true both in relation to electrical drives with the DC motor drives, and BL DC motor drives, as can be seen, for example, turning to articles [5,6]. Such systems do not need any precise regulation of voltage of power supply of the electric motor in dynamics, as required by specificity of the physical model of the pumping station of a dry dock, due to covering the PWM converter with negative feedback.…”

Sensorless Control for Joint Drive Unit of Lower Extremity Exoskeleton with Cascade Feedback Observer

“…In [17], considering the nonlinear effect of power inverter, an adaptive second-order SMO is designed, which reduces observation error effectively. A global fast terminal SMO based on linear SMO and fast terminal SMO is proposed in [18], which made full use of the advantages of linear SMO and fast terminal SMO.…”

Research on BP Neural Network Motor Control Algorithm Based on Frog Leaping Algorithm

“…The traditional PID control method is mainly used to solve the linear time-invariant problem, in which the parameters need to be set in advance and cannot be changed with the change of the control object. However, the brushless DC motor usually has a large nonlinearity, so the brushless DC motor control system based on the traditional PID control method has the problems of poor anti-interference ability and low precision of speed control [3]. In order to improve the performance of brushless DC motor control, researchers proposed many intelligent control methods, such as neural network control and fuzzy control [4].…”