Research and simulation on PID control method for Brushless DC Motor based on genetic algorithm and BP neural network

2008 ◽  
Author(s):  
Xiu-juan Ma ◽  
Yi Liu ◽  
Ling Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Pid Control ◽  
Control Method ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Brushless Dc

scholarly journals PID Control System on Brushless DC Motor for Quadcopter Balance

Jurnal INFORM ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 110-114
Author(s):  
Mochamad Mobed Bachtiar ◽  
Fernando Ardilla ◽  
Muhammad Faiz Hasbi ◽  
Iwan Kurnianto Wibowo
Keyword(s):  
Pid Control ◽  
Control Method ◽  
Dc Motor ◽  
Unmanned Aircraft ◽  
Integrated System ◽  
Brushless Dc Motor ◽  
Test Results ◽  
Brushless Dc ◽  
Aerial Vehicle ◽  
Aircraft System

Unmanned Aerial Vehicle (UAV) is an unmanned aircraft system that is no longer a special need but has become a general need for the community, and one example is used to capture everyday moments through photos or videos from the air. Among the models of UAV aircraft is the quadcopter, where there is a flight controller that functions to fly the quadcopter by adjusting the speed of each motor. The flight controller that is often used today is the Pixhawk manufacturer. The Pixhawk module is an integrated system that the factory has provided, so it cannot be modified in terms of control and I/O. This research focuses on making an independent flight controller that can be used to fly a quadcopter. The control method that is implanted is Proportional Integral Derivative or commonly known as PID. The flight controller uses the PID control method to adjust each Brushless DC Motor (BLDC) speed to maintain stability while flying. From the test results, the quadcopter can fly stably with KP parameters of 2.5, KI of 0.6, and KD of 1.0. The response time in processing feedback is 3s.

Single Neuron PID Adaptive Control for BLDCM Based on Algebraic Algorithm

2012 ◽  
Vol 220-223 ◽  
pp. 851-854
Author(s):  
Yan Diao ◽  
Hong Ping Jia ◽  
Tian Jun Geng
Keyword(s):  
Adaptive Control ◽  
Motor Control ◽  
Control System ◽  
Pid Control ◽  
Control Method ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Brushless Dc ◽  
Dc Motor Control ◽  
Motor Control System

The brushless DC motor control system often adopts the classic PID control, the advantages of which are as follows: simple to control, easy to adjust the parameter and a certain degree of control precision. But it relies on accurate mathematical model. The permanent magnet brushless DC motor control system is a multi-variable and nonlinear. As to the deficiencies of the classic PID control method, this thesis proposes a method called artificial neural network PID adaptive control method, which is based on algebraic algorithm and overcomes the shortcomings such as the slow convergence of BP algorithm, easy to trap in local minimum, and etc.

scholarly journals An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

2021 ◽  
Vol 11 (6) ◽  
pp. 2685
Author(s):  
Guojin Pei ◽  
Ming Yu ◽  
Yaohui Xu ◽  
Cui Ma ◽  
Houhu Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Experimental Validation ◽  
Control Method ◽  
Smith Predictor ◽  
Constant Force ◽  
Matlab Simulation ◽  
Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

Optimal core shape design for cogging torque reduction of brushless DC motor using genetic algorithm

2000 ◽  
Vol 36 (4) ◽  
pp. 1927-1931 ◽  
Author(s):  
Ki-Jin Han ◽  
Han-Sam Cho ◽  
Dong-Hyeok Cho ◽  
Hyun-Kyo Jung
Keyword(s):  
Genetic Algorithm ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Shape Design ◽  
Cogging Torque ◽  
Brushless Dc

scholarly journals Execution of BLDC Motor using Fuzzy Logic Controller on Propulsion Application for Hybrid Vehicle System

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3989-3993
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Torque Ripple ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Rotor Speed ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Sensor Control

This research Paper proposes the Brushless DC motors control (BLDC) could accomplish higher execution looking into effectiveness in examination for old brushed DC motor controlling which is difficult to control because it requires a phase for switching circuit. This work proposes a fuzzy logic control for brushless DC motor for axis based on Hall Effect by applying sensor control system and also it produces brushless motor for rearranging the three phase conduction mode model. At long last this paper may be with create efficient control methodologies on enhance driving dynamics on the mechanical dynamic consider of propulsion method. The recommended control method stabilizes those controls services (speeds) done by controller of brushless DC motor drive (BLDC). On behalf of settling 2 wheels also physical favorable circumstances of BLDC motors are associated straight forwardly of the tires by improving the rotor speed. The parameters such as power factor, rotor speed, torque ripple, EMF is compensated & simulation results are tabulated.

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