Adaptive Fuzzy Neural Network PID Algorithm for BLDCM Speed Control System

Abstract

Because of its simple structure, high efficiency, low noise, and high reliability, the brushless direct current motor (BLDCM) has an irreplaceable role compared with other types of motors in many aspects. The traditional proportional integral derivative (PID) control algorithm has been widely used in practical engineering because of its simple structure and convenient adjustment, but it has many shortcomings in control accuracy and other aspects. Therefore, in this paper, a fuzzy single neuron neural network (FSNNN) PID algorithm based on an automatic speed regulator (ASR) is designed and applied to a BLDCM control system. This paper introduces a BLDCM mathematical model and its control system and designs an FSNNN PID algorithm that takes speed deviation e at different sampling times as inputs of a neural network to adjust the PID parameters, and then it uses a fuzzy system to adjust gain K of the neural network. In addition, the frequency domain stability of a double closed loop PID control system is analyzed, and the control effect of traditional PID, fuzzy PID, and FSNNN PID algorithms are compared by setting different reference speeds, as well as the change rules of three-phase current, back electromotive force (EMF), electromagnetic torque, and rotor angle position. Finally, results show that a motor controlled by the FSNNN PID algorithm has certain superiority compared with traditional PID and fuzzy PID algorithms and also has better control effects.

BLDC Motor Speed Control with Digital Adaptive PID-Fuzzy Controller and Reduced Harmonic Content

“…To mitigate this problem, the Fuzzy controller is combined with a traditional proportionally integrally differential (PID) control algorithm. A similar idea has been analyzed in the scientific works presented in [6,21] with an additional current control loop. In this case, additional control method will be implemented to face the electromagnetic interference which is challenging when six step control method with Hall-effect-based rotor position sensors [22].…”

“…Considering that microcontroller performance is increasing today, it is possible to create more sophisticated control algorithms, potentially improving motor reaction speeds. A comparison of the different regulators is provided in [4][5][6]. The results show that adaptive controllers, such as neuron networks, genetic algorithms, Fuzzy logic regulator and others, allow improve the dynamics of the motor even they do not require a precise motor model to create such a controller.…”

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

“…By combining ANFIS with traditional PID controller the adaptive fuzzy neural PID control can be implemented in a PV inverter system to address the issues of system instability and long response times [94,95]. The ANFIS-based PID control in the PV inverter system is given in Figure 13.…”

3-DOF Position and Orientation Control of an Air Flotation Platform for Spacecraft Ground Microgravity Simulation by Using Double Closed-Loop Cascade PIDnn

“…The input of the PID neural network system has two parts, which are the expected input and the real-time output of the system. After each iteration, the neural network outputs new values of P, I, and D as three parameters of the International Journal of Aerospace Engineering PID neural network controller [27,28]. After processing by the dynamic model, as the new input of the system continues to iterate, the system tends to stabilize after the number of sampling N approaches a larger value [29].…”