DC Motor Speed Control Using Hybrid PID-Fuzzy with ITAE Polynomial Initiation

2019 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Hari Wibawa ◽  
Oyas Wahyunggoro ◽  
Adha Imam Cahyadi
Keyword(s):  
Transfer Function ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Industrial Sector ◽  
Pid Controllers ◽  
Motor Speed ◽  
Dc Motors ◽  
Working Parameters ◽  
Motor Operation ◽  
Load Simulation

DC motors are widely applied in industrial sector, especiallyprocesses of automation and robotics. Given its role in the sector, DC motor operation needs to be optimized. One of optimization steps is controlling speed using several control methods, for example conventional PID methods, PID Ziegler Nichols, PID based on ITAE polynomials, and Hybrid PID-Fuzzy. From these methods, Hybrid PID-Fuzzy was chosen as a method to be proposed in this paper because it can anticipate shortcomings of PID controllers and fuzzy controllers so as to produce system responses that are fast and adaptive to errors. This paper aimed to design a Hybrid PID-Fuzzy system based on ITAE polynomials (Hybrid-ITAE), to analyze its performance parameters values, and tp compare Hybrid-ITAE performance with conventional PID method. Working parameters being reviewed include overshoot, rise time, settling time, and ITAE. First of all, JGA25-370 DC motor was modeled in a form of a third order transfer function equation. Based on the transfer function, PID parameters were calculated using PID Output Feedback and ITAE polynomial methods. The best ITAE polynomial PID controllers were then be combined with a Fuzzy Logic Controller to form a Hybrid-ITAE system. Simulation and experimental stages were carried out in two conditions, namely no load and loaded. Simulation and experimental results showed that Hybrid-ITAE (l = 0.85) was the best controller for no-load simulation conditions. For loaded simulation Hybrid-ITAE (l=1) was a better controller. In no-loads experiment, the best controller was Hybrid PID-Ziegler Nichols, while for loaded condition the best controller was Hybrid PID-Ziegler Nichols.

scholarly journals Optimal Tuning of Fuzzy Logic Controller Based Speed Control of DC Motor

2021 ◽  
Vol 19 (1) ◽  
pp. 77-80
Author(s):  
Mohsin A. Koondhar ◽  
Muhammad U. Keerio ◽  
Rameez A. Talani ◽  
Kamran A. Samo ◽  
Muhammad S. Bajwa ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Speed Control ◽  
Dc Motor ◽  
Motor Speed ◽  
Dc Motors ◽  
Manual Adjustment ◽  
Control Application ◽  
Installation Time ◽  
Transient And Steady State

Fuzzy logic controller (FLC) has become popular in the speed control application of DC motors with automatic adjustment function. In this article, the performance of a specific FLC controlled DC motor is studied. The exceed speed is observed with a stabilization time, thus confirming the FLC behavior. Therefore, FLC must be set to obtain the required performance by applying appropriate expert rules, the minimum overshoot and installation time can be maintained within the required values. With the help of FLC, the manual adjustment function is gradually eliminated, and the intelligent adjustment function is at the center position, and the performance is satisfactory. FLC DC motor speed control is implemented in MATLAB environment. The results show that the FLC method has the smallest bypass, smallest transient and steady-state error, and shows higher FLC efficiency as compared with other conventional controllers.

scholarly journals DC Motor Speed Control using Internal Model Controller: Industrial Transformation Strategy

2020 ◽  
Vol 9 (5) ◽  
pp. 300-306
Keyword(s):  
Transfer Function ◽  
Pid Control ◽  
Pid Controller ◽  
Internal Model ◽  
Dc Motor ◽  
Motor Speed ◽  
Matlab Simulink ◽  
Dc Motors ◽  
Control Schemes ◽  
Motor Model

In developed nations, industries are made to function at control engineering costs via the use of appropriate control schemes for dc motors. This paper introduces the role played by dc motors in industries thereby necessitating the analysis and performance validation of dc motor in Internal Model Control (IMC) scheme as against the Proportional– Integral–Derivative (PID) control schemes that is widely used in most industries. Theories on dc motor model, PID and IMC controller were detailed to paved the way for the methodical approach of getting specifications and transfer function for a typical dc motor (model RMCS-3011). Matlab/Simulink software was then used to tune the PID controller for the purpose of finding the values of PID gains that meets the design requirements to achieve best performance, thereby enabling the simulation of the PID controller. Using Matlab m-file environment, IMC controller transfer function was generated and simulated. The IMC controller transfer function aimed at achieving a unity gain that tracks the set-point was approximately realized. In the realization process, it was obvious that a filter is required. The aim of this work is to evaluate the performance of the IMC controller over PID controller. Simulated plots in Matlab-Simulink using the PID gains for the PID controller, and time constants and filter order for the IMC were presented. The quantitative results of the IMC method when compared with that of PID control provides a commendable performance. However, the performance in terms of rise time is small and preferred with the use of Matlab-Simulink tuned PID controller. Conclusively, IMC controller would be the preferred controller where the robustness and accuracy of the dc motor speed control counts more than faster response

scholarly journals Speed control of DC motor using conventional and adaptive PID controllers

2019 ◽  
Vol 16 (3) ◽  
pp. 1221 ◽  
Author(s):  
Sarah N. Al-Bargothi ◽  
Ghazi M. Qaryouti ◽  
Qazem M. Jaber
Keyword(s):  
Tracking Error ◽  
Dc Motor ◽  
Operating Conditions ◽  
Least Square ◽  
Pid Controllers ◽  
Motor Speed ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Dc Motors ◽  
Practical Challenge

<p>Proportional Integral Derivative (PID) controllers are <a href="http://www.thesaurus.com/browse/extensively">extensively</a> used in practical industries to control the speed of DC Motors. The single weakness of PID controllers is their sensitivity to variation in parameters and operating conditions; thus, tuning the controller gains to adapt with these variations presents a practical challenge. In this paper, an adaptive mechanism that utilizes a Recursive Least Square (RLS) algorithm, with rate limiters, is implemented to perform an online self-adjusting of each of the PID gains in order to achieve Adaptive PID (APID) controller that will accommodate to system variations. MATLAB/ Simulink software is used to implement and simulate APID control of a Chopper-Fed DC motor. A conventional PID control system is also designed and simulated to obtain results that can be used to judge the performance of the APID controller. Results proved that the APID controller forced the motor speed to track the reference input with <a href="http://www.thesaurus.com/browse/insignificant">insignificant</a> tracking error, and also managed to attain the motor speed at its desired value, regardless of the load changes inflected on the motor. This enhances both transient and steady-state speed responses.</p>

Optimal Control of DC Motors Using PSO Algorithm for Tuning PID Controller

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Arnisa Myrtellari ◽  
Petrika Marango ◽  
Margarita Gjonaj
Keyword(s):  
Pid Controller ◽  
High Performance ◽  
Industrial Process ◽  
Speed Control ◽  
Pso Algorithm ◽  
Dc Motor ◽  
Step Response ◽  
Pid Controllers ◽  
Motor Speed ◽  
Dc Motors

The DC motors are widely used in the mechanisms that require control of speed. Different speed can be obtained by changing the field voltage and the armature voltage. The classic PID controllers are widely used in industrial process for speed control. But they aren’t suitable for high performance cases, because of the low robustness of PID controller. So many researchers have been studying various new control techniques in order to improve the system performance and tuning PID controllers. This paper presents particle swarm optimization (PSO) method for determining the optimal PID controller parameters to find the optimal parameters of DC Motor speed control system. The DC Motor system drive is modeled in MATLAB/SIMULINK and PSO algorithm is implemented using MATLAB toolbox. The results obtained through simulation show that the proposed controller can perform an efficient search for the optimal PID controller. Simulation results show performance improvement in time domain specifications for a step response (no overshoot, minimal rise time, steady state error = 0).

scholarly journals ATOM SEARCH OPTIMIZATION – NEURAL NETWORK FOR DRIVING DC MOTOR

SINERGI ◽  
2021 ◽  
Vol 25 (3) ◽  
pp. 259
Author(s):  
Widi Aribowo ◽  
Joko Joko ◽  
Subuh Isnur ◽  
Aditya Chandra Hermawan ◽  
Fendi Achmad ◽  
...  
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Dc Motor ◽  
Settling Time ◽  
Motor Speed ◽  
Constraint Force ◽  
Dc Motors ◽  
Search Optimization ◽  
The Stability ◽  
Motor Operation

DC motor applications are very widely used because DC motors are very suitable for applications, especially control. Thus, a proper DC motor controller design is required. DC motor speed control is very important to maintain the stability of motor operation. A recent type of metaheuristic algorithm that mimics the motion of atoms is introduced. Atom search optimization (ASO) is a mathematical model and duplicates the behavior of atoms in nature. Atoms intercommunicate with each other via the delivering contact force in the form of the Lennard-Jones potential and the constraint force produced from the potential bond length. The algorithm is simple and easy to be applied. In this study, the atomic search optimization (ASO) algorithm is proposed as a speed controller for the control dc motor. First, the ASO proposed by the algorithm is applied for the optimization of the neural network. Second, the ASO-NN proposal was the result compared to other algorithms. This paper compares the performance of two different control techniques applied to DC motors, namely the ASO-NN and proportional integral derivative (PID) methods. The results show that the proposed method has effectiveness. The calculation of the proposed ASO-NN control shows the best performance in the settling time. The ASO-NN method has the capability of settling time 0.04 seconds faster than the PID method.

Straight-Move Robot Control System with LabView-Based Proportional Integral Derivative (PID) Control

2019 ◽  
Vol 3 (2) ◽  
pp. 13-24
Author(s):  
Andrean George W
Keyword(s):  
Pid Control ◽  
Rotational Speed ◽  
Pid Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Labview Software ◽  
Dc Motors ◽  
Dc Motor Control ◽  
Process System ◽  
Interface Devices

Abstract - Control and monitoring of the rotational speed of a wheel (DC motor) in a process system is very important role in the implementation of the industry. PWM control and monitoring for wheel rotational speed on a pair of DC motors uses computer interface devices where in the industry this is needed to facilitate operators in controlling and monitoring motor speed. In order to obtain the best controller, tuning the Integral Derifative (PID) controller parameter is done. In this tuning we can know the value of proportional gain (Kp), integral time (Ti) and derivative time (Td). The PID controller will give action to the DC motor control based on the error obtained, the desired DC motor rotation value is called the set point. LabVIEW software is used as a PE monitor, motor speed control. Keyword : LabView, Motor DC, Arduino, LabView, PID.

scholarly journals Parameter Estimation of DC Motor using Adaptive Transfer Function Based on Nelder-Mead Optimisation

2018 ◽  
Vol 9 (3) ◽  
pp. 696 ◽  
Author(s):  
Byamakesh Nayak ◽  
Sangeeta Sahu ◽  
Tanmoy Roy Choudhury
Keyword(s):  
Experimental Data ◽  
Transfer Function ◽  
Adaptive Method ◽  
Dc Motor ◽  
Model Parameters ◽  
Current Response ◽  
Adaptive Model ◽  
Unknown Parameters ◽  
Dc Motors ◽  
Estimated Parameters

<p>This paper explains an adaptive method for estimation of unknown parameters of transfer function model of any system for finding the parameters. The transfer function of the model with unknown model parameters is considered as the adaptive model whose values are adapted with the experimental data. The minimization of error between the experimental data and the output of the adaptive model have been realised by choosing objective function based on different error criterions. Nelder-Mead optimisation Method is used for adaption algorithm. To prove the method robustness and for students learning, the simple system of separately excited dc motor is considered in this paper. The experimental data of speed response and corresponding current response are taken and transfer function parameters of  dc motors are adapted based on Nelder-Mead optimisation to match with the experimental data. The effectiveness of estimated parameters with different objective functions are compared and validated with machine specification parameters.</p>

scholarly journals Sensor-less Brushed DC Motor Speed Control with Intelligent Controllers

2021 ◽  
Vol 20 ◽  
pp. 140-148
Author(s):  
Amir Salmaninejad ◽  
Rene V. Mayorga
Keyword(s):  
Closed Loop ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Mechanical Load ◽  
Dc Motor ◽  
Closed Loop Control ◽  
Motor Speed ◽  
Motor Shaft ◽  
Loop Control ◽  
Intelligent Controllers

A Direct Current (DC) Motor is usually supposed to be operated at a desired speed even if the load on the shaft is exposed to changes. One of its applications is in automatic door controllers like elevator automatic door drivers. Initially, to achieve this aim, a closed loop control can be applied. The speed feedback is usually prepared by a sensor (encoder or tachometer) coupled to the motor shaft. Most of these sensors do not always perform well, especially in elevator systems, where high levels of noise, physical tensions of the mobile car, and maintenance technicians walking on the car, make this environment too noisy. This Paper presents a new approach for precise closed loop control of the DC motor speed without a feedback sensor, while the output load is variable. The speed here is estimated by the Back EMF (BEMF) voltage obtained from the armature current. First, it is shown that a PID controller cannot control this process alone, and then intelligent controllers, Fuzzy Logic Controller (FLC) and Adaptive Neuro Fuzzy Inference Systems (ANFIS), assisting PID are applied to control this process. Finally, these controllers’ performance subjected to a variable mechanical load on the motor shaft are compared.

scholarly journals DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

Jurnal Teknik ◽  
2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Sumardi Sadi
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Logic Control ◽  
Control Command ◽  
Arduino Uno

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

Dual DC Motor Speed Control Based on Two Independent Digital PWM Signals using PIC16F877A Microcontroller

2016 ◽  
Vol 2 (3) ◽  
pp. 592
Author(s):  
Ayman Y. Yousef ◽  
M. H. Mostafa
Keyword(s):  
Control System ◽  
Software Package ◽  
Speed Control ◽  
Dc Motor ◽  
Open Loop ◽  
Motor Speed ◽  
Dc Motors ◽  
Duty Cycles ◽  
Speed Control System ◽  
Control System Model

<p>In this paper a dual open loop speed control system based on two independent PWM signals of small permanent magnet DC (PMDC) motors using PIC16F877A microcontroller (MCU) has been designed and implemented. The Capture/Compare/PWM (CCP) modules of the MCU are configured as PWM mode and the MCU is programmed using flowcode software package to generate two PWM signals with various duty cycles at the same frequency. A dual H-bridge channel chip SN754410 is used to drive the motors. The variation of PWM duty cycles is related directly to controlling the DC motors terminal voltage which directly proportional with speed of each motor. The complete PWM control system model has been simulated using proteus design suite software package. The development of hardware and software of the dual DC motor speed control system has been explained and clarified.</p>

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