Speed control of brushless DC motor using line to line back EMF by ANFIS controller

Kumar, Asok; Sami, K

doi:10.5958/0976-5506.2018.01241.x

Cited by 5 publications

(2 citation statements)

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“…Generally, if there is a very small error in the current measurement, According to the PID parameters, the proportional gain value should be smaller, and both the integral constant value and derivative gain value should be larger. This will reduce the overshoot of the output by reducing the fast response of the PID based system [15]. Tuning the PID controller parameters was accomplished via a MatlabSimulink block.…”

Section: Manipulator Control System For a Robotmentioning

confidence: 99%

Artificial Intelligence Techniques Applied to Control and Identify Parameters in variable DOF Robotic Manipulators for Industrial Automation

K.Rajavelu

2023

tjjpt

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An industrial robot manipulator must be able to translate its static collection of joint angles into position coordinates before calculating the end effect's appropriate positioning with forward kinematics.A robotic manipulator has now become an integral part of industrial automation, greatly reducing the need for human labour, increasing the accuracy of work as well as reducing the time required to accomplish tasks.Each rotation must be controlled using servomotor feedback. MATLAB® Robotics Toolbox is used to verify the kinematic model for the activation of three revolving joints.The purpose of this paper is to provide an overview of an end-effect-based DC servomotor platform with intelligent controller for five degrees of freedom (5-DOF). A DenavitHarterberg (DH) representation was used to model forward and inverse kinematics. PID controllers with fuzzy logic are used to implement various blurring strategies. Simulated results from MATLAB show that PID produces better transient parameters than a conventional PID.Compared to the Fuzzy logic controller, PID has a better overshoot performance than FLC and both controllers can achieve the desired output when subjected to steady state responses, although FLC outperforms PID. DOI: https://doi.org/10.52783/tjjpt.v44.i4.946

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Section: Manipulator Control System For a Robotmentioning

confidence: 99%

Artificial Intelligence Techniques Applied to Control and Identify Parameters in variable DOF Robotic Manipulators for Industrial Automation

K.Rajavelu

2023

tjjpt

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“…High speed helps to measure the resolution and to check the search area. The superior solution can be surpassed by particles when the value is extremely large and trapped in the optimal local value if the value is small [22][23][24] . The accurate fitness function is presented and shown in Fig.…”

Section: Robotic Systems and Dynamicsmentioning

confidence: 99%

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

Kumar¹,

Naveen

Vijayakumar

et al. 2023

Sci Rep

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The propensity of manufacturers to produce goods at affordable cost, with more accuracy, and at a faster rate force them to search for novel solutions, such as deploying robots in place of people in a sector that can accommodate their needs. Welding is one of the most crucial processes in the automotive industry. This process is time-consuming, subject to error, and demands skilled professionals. The robotic application can improve this area of production and quality. Other industries, such as painting and material handling, can also profit from the use of robots. This work describes the fuzzy DC linear servo controller, which functions as a robotic arm actuator. Robots have been widely employed in most productive sectors in recent years, including assembly plates, welding, tasks at higher temperatures, etc. Controlling a robot accurately is a difficult undertaking as a robot is very nonlinear with many joints that are often organized and unstructured. To carry out the effective task, an effective PID control based on fuzzy logic has been employed together with the method of Particle Swarm Optimization (PSO) approach for the estimate of the parameter. This offline technique determines the lowest number of optimal robotic arm control parameters. To verify the controller design with computer simulation, a comparative assessment of controllers is given by means of a fuzzy surveillance controller with PSO which improves the parameter gain to provide a rapid climb, a smaller overflow, no steady condition error signal, and effective torque control of the robot arm.

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RETRACTED ARTICLE: Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor

Gobinath

Madheswaran²

2019

Soft Comput

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Speed control of brushless DC motor using line to line back EMF by ANFIS controller

Cited by 5 publications

References 0 publications

Artificial Intelligence Techniques Applied to Control and Identify Parameters in variable DOF Robotic Manipulators for Industrial Automation

Artificial Intelligence Techniques Applied to Control and Identify Parameters in variable DOF Robotic Manipulators for Industrial Automation

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

RETRACTED ARTICLE: Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor

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