In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.
BLDC motors are used widely due to various advantages.
This slide includes construction, working, modes of operation and braking, and applications. We also compare it with Brushed DC motor and Induction Motor.
The document summarizes the working principle, construction, and types of single-phase induction motors. It describes:
1) The working principle is explained using double revolving field theory, where the pulsating magnetic field is divided into two fields rotating in opposite directions, generating opposing torques.
2) The construction is similar to three-phase induction motors, using a squirrel cage rotor. Stators have two windings placed 90 degrees apart connected in parallel to the single-phase supply.
3) Types include split-phase, capacitor-start, capacitor-start capacitor-run, two-value capacitor, and shaded-pole motors. Capacitor motors use capacitors in series with starting wind
This predefined speed control of BLDC motor runs a motor at user desired speed by using EEPROM for storing speed. It is an effective speed control method.
As the technology for the power semiconductor devices and integrated circuit develops, the potential for applications of power electronics become wider.
These slides introduce the design of lead-acid standby battery charger. Which would be used for 12 V batteries. including the schematic and layout designed using KiCad.
This document discusses the concept of dynamic or on-the-move charging of electric vehicles. It outlines some current limitations of electric vehicles like high costs, limited range, and long charging times. It then proposes using dynamic charging systems that would allow electric vehicles to charge while in motion or stopped briefly rather than requiring lengthy stationary charging sessions. Some key advantages noted are smaller batteries, more frequent smaller charges extending battery life, and reducing range anxiety. Two approaches - induction and overhead capacitive charging - currently being tested are described. The concept proposes a next-generation dynamic system using ultracapacitors charged by solar panels and discharged to vehicles' batteries while in motion. Key research areas are identified to develop such a system. Initial target markets like drive
DESIGN AND MODELLING OF STANDALONE HYBRID POWER SYSTEM WITH MATLAB/SIMULINKMichael George
This paper presents a standalone hybrid power system using wind/PV/diesel/Battery energy system with Matlab/Simulink environment. PV & Wind are the primary power sources of the system and battery is used as a backup for long run application. Here we have also compare the performance of PV/Wind/Battery connected system with PV/Wind/Diesel connected hybrid power system. The most frequently used renewable energy sources are those consisting of PV module and/or wind Turbine with/or Diesel generator, and some energy storage or backup system.
BLDC motors are used widely due to various advantages.
This slide includes construction, working, modes of operation and braking, and applications. We also compare it with Brushed DC motor and Induction Motor.
The document summarizes the working principle, construction, and types of single-phase induction motors. It describes:
1) The working principle is explained using double revolving field theory, where the pulsating magnetic field is divided into two fields rotating in opposite directions, generating opposing torques.
2) The construction is similar to three-phase induction motors, using a squirrel cage rotor. Stators have two windings placed 90 degrees apart connected in parallel to the single-phase supply.
3) Types include split-phase, capacitor-start, capacitor-start capacitor-run, two-value capacitor, and shaded-pole motors. Capacitor motors use capacitors in series with starting wind
This predefined speed control of BLDC motor runs a motor at user desired speed by using EEPROM for storing speed. It is an effective speed control method.
As the technology for the power semiconductor devices and integrated circuit develops, the potential for applications of power electronics become wider.
These slides introduce the design of lead-acid standby battery charger. Which would be used for 12 V batteries. including the schematic and layout designed using KiCad.
This document discusses the concept of dynamic or on-the-move charging of electric vehicles. It outlines some current limitations of electric vehicles like high costs, limited range, and long charging times. It then proposes using dynamic charging systems that would allow electric vehicles to charge while in motion or stopped briefly rather than requiring lengthy stationary charging sessions. Some key advantages noted are smaller batteries, more frequent smaller charges extending battery life, and reducing range anxiety. Two approaches - induction and overhead capacitive charging - currently being tested are described. The concept proposes a next-generation dynamic system using ultracapacitors charged by solar panels and discharged to vehicles' batteries while in motion. Key research areas are identified to develop such a system. Initial target markets like drive
DESIGN AND MODELLING OF STANDALONE HYBRID POWER SYSTEM WITH MATLAB/SIMULINKMichael George
This paper presents a standalone hybrid power system using wind/PV/diesel/Battery energy system with Matlab/Simulink environment. PV & Wind are the primary power sources of the system and battery is used as a backup for long run application. Here we have also compare the performance of PV/Wind/Battery connected system with PV/Wind/Diesel connected hybrid power system. The most frequently used renewable energy sources are those consisting of PV module and/or wind Turbine with/or Diesel generator, and some energy storage or backup system.
This document summarizes a student project analyzing and simulating multilevel inverter topologies using MATLAB/Simulink. The objectives are to conduct a literature review on multilevel inverter configurations, develop and simulate different two-level and multilevel inverter topologies using PWM techniques, and determine the characteristics and harmonics levels. The scope includes applications like motor drives, DC transmission, FACTS, and renewable energy integration. Literature discusses how multilevel inverters synthesize voltages from multiple DC levels and are suitable for industrial high power applications. Diode-clamped, flying capacitor, and cascaded/H-bridge topologies will be studied. The methodology involves designing simulation models and analyzing performance results.
This document provides an overview of an introductory course on the design of three-phase induction motors. It outlines the objectives of designing a motor with a 1HP output power rating, 1500 RPM speed, and meeting other efficiency and size criteria. It then covers key topics in the electromagnetic design process like motor nameplate parameters, efficiency classes, frame sizes, dimensions, analytic design approach, classification of design variables, output equations, and calculation of various motor components.
The traction motor shed was established in 1973 to meet the maintenance needs of electric locomotive motors for Indian Railways. It performs reclamation, rewinding, and manufacturing of traction motor armatures, staters, and auxiliary machines. The shed has different sections for auxiliary motors, testing, millwright work, armature winding, stator winding, and coil manufacturing. It overhauls traction motors which use dynamic braking to generate electricity during braking and supply auxiliary loads in electric locomotives.
This document proposes a system to wirelessly charge electric vehicles using solar energy. The system would use solar panels to generate electricity, which would be stored in batteries and converted to AC power using an inverter. Coils placed in the vehicle and road would transmit the AC power wirelessly through electromagnetic induction to charge the vehicle battery as it drives. The objectives are to develop a renewable energy charging system and enable "charge as you drive" capability. Hardware components, a block diagram, voltage measurements between coils at different distances, and advantages/disadvantages are described.
This document summarizes different types of excitation systems for alternators. It discusses the function of excitation systems to supply direct current to the field winding and control the voltage and reactive power of alternators. The three main types covered are DC excitation systems, AC excitation systems, and static excitation systems. DC excitation systems use two small DC generators as exciters but are not commonly used for large alternators now. AC excitation systems include brushless and rotating thyristor types and have advantages like eliminating brushes. Static excitation systems have no rotating parts, are suitable for medium and high capacity alternators, and have benefits like smaller size and no windage losses. The document concludes that the selection of an excitation system depends on factors like the altern
Device Modeling and Simulation of DC Motor using LTspiceTsuyoshi Horigome
This document describes the simulation of a DC motor control circuit using LTspice. It includes models of a DC motor, timer IC, and specific Mabuchi motor. It details the manufacturer specifications, calculations to determine torque and back EMF constants, and transient simulations at no load and various load conditions. Simulation results are compared to measurement data to validate the motor model.
Wireless charging of Electric Vehicles (IEEE Paper 2017)Georget Eldhose
This document discusses wireless power transmission applied to electric vehicles. It begins with an introduction to electric vehicles and the need to reduce charging times. It then describes different charging systems and compares wireless to plug-in charging. The document outlines the typical components of a wireless charging system including power inverters, resonant tanks, and induction coils. It presents an experimental model of a small-scale wireless charging track for electric cars. Key advantages include reduced operating costs, lower maintenance than gas vehicles, and the ability to charge multiple vehicles simultaneously. However, initial installation costs are high and power transmission is limited by range. In conclusion, wireless charging is well-suited for electric vehicles by reducing recharging times and allowing charging on the go.
This document describes a power supply/battery charger with a full bridge resonant converter. The charger has an output of 26.4VDC at 33A maximum. It operates in constant current and constant voltage modes with protections against overvoltage, overtemperature, and undervoltage. The charger uses a pulse width modulated full bridge converter with a power factor correction boost converter on the input side and a full bridge ZVS resonant converter on the output side. Key components and operations of the power factor correction, DC-DC conversion, output rectification, and control sections are explained.
This document describes the modeling and simulation of a brushless DC motor drive system in MATLAB/Simulink. It includes models of the BLDC motor, three-phase inverter, current controller, speed controller and reference current generator. The motor is modeled using a three-phase variable model. A PI controller is used to control the current and an outer speed loop controls the rotor speed. Simulation results can be used to predict system behavior and determine controller parameters.
This seminar presentation summarizes the key aspects of linear induction motors (LIMs). It discusses the construction of LIMs, including their stator and rotor components. It describes the different types of LIMs according to their core shape, including iron core, ironless core, and slot-less designs. The presentation also covers the principles of operation of LIMs, the different forces involved, and various effects such as end effects and gap effects. It compares LIMs to conventional induction motors and rotary induction motors, outlines the advantages and disadvantages of LIMs, and discusses applications such as transportation and material handling where LIMs are commonly used.
star delta auto starter with forward reverse and motor protectionBHUPATI PRADHAN
It is a project on pure electrical engineering. Here three phase motor is starting from star to delta automatically by using some components.In other hand it also provide protection to the motor.
The document discusses simulation, analysis and open loop control of a multilevel inverter fed induction motor. It introduces multilevel inverters and their topologies like cascaded H-bridge, diode clamped and flying capacitor. It describes sinusoidal PWM techniques like in-phase disposition, phase opposition disposition and alternate phase opposition disposition. It models the phase disposition modulation technique by generating modulating sine waves, triangular carrier waves and firing pulses for 3-level and 5-level diode clamped inverters. Simulation results are presented for induction motor performance with 2-level and multilevel inverters under different operating conditions.
This document discusses transformer sizing using ETAP software. It explains that ETAP takes into account factors like ambient temperature, altitude, cooling type and expected future growth to determine the proper transformer size. The document provides details on ETAP's 2-winding transformer sizing module, which calculates the rated MVA, maximum MVA and impedance based on loading, installation factors and short circuit requirements. It also discusses how ETAP can be used to check transformer regulation during motor starts. The document concludes that transformer sizing calculations can be standardized using ETAP due to the harmony between ETAP's sizing module and manual calculations using formulas.
Power electronics refers to controlling and converting electrical power using semiconductor switches like silicon-controlled rectifiers. This allows control of power for applications like motor drives, power supplies, and inverters. Key devices include rectifiers, inverters, and choppers. Rectifiers convert AC to DC, inverters convert DC to AC, and choppers convert DC voltages. Power electronics is used widely in applications like heating/lighting control, motor drives, battery chargers, and electric vehicles.
Electric vehicle batteries have evolved from early lead-acid batteries to current lithium-ion batteries that provide over 300 km of range. Different battery types include lead-acid, nickel-metal hydride, sodium-nickel chloride, and lithium-ion. Charging can be conducted through conductive coupling to charge ports or inductively. Future battery innovations aim to further increase range and reduce charging times through technologies like carbon nanotubes and battery swapping systems.
The document provides information about the Electric Loco Shed in Ghaziabad, including:
1) It was established in 1976 and maintains locomotives such as WAP1, WAP4, WAP5, and WAP7.
2) It has an initial cost of 1.5 crores and currently holds 184 locomotives.
3) It describes the various sections in the shed such as the mechanical, electrical, and painting workshops which perform maintenance on locomotives.
The document discusses the history and components of battery electric vehicles (BEVs). It notes that the first human-carrying electric vehicle was tested in Paris in 1881. BEVs use electricity from batteries to power an electric motor rather than an internal combustion engine. The key components of BEVs are the battery charger, traction batteries, power converters, electric motor, motor controller, transmission system, and differential system. BEVs are further classified based on their energy storage sources into pure electric vehicles (PEVs/BEVs), fuel cell electric vehicles, ultracapacitor electric vehicles, and ultraflywheel electric vehicles.
The document summarizes information about Diesel Shed Ratlam, located in Madhya Pradesh, India. It was established in 1967 and maintains diesel locomotives. It discusses the types of locomotives - steam, diesel-electric, and electric. Diesel-electric locomotives became widely used because they don't produce smoke and have higher efficiency than steam. Traction motors, the main components of locomotives, are also described in terms of their construction, ratings, and operating principle.
This document discusses the design and analysis of a Single-Ended Primary-Inductor Converter (SEPIC) circuit. It provides an overview of SEPIC converters and how they allow the output voltage to be greater than, less than, or equal to the input voltage. The document then describes the methodology for analyzing a SEPIC circuit operating in continuous mode. It includes calculations for determining the output voltage, inductor and capacitor values, voltage ripple, current stresses, and MOSFET selection. Simulation results are presented and disadvantages of SEPIC converters are noted.
Nonlinear control of WECS based on PMSG for optimal power extraction IJECEIAES
This document presents a study that proposes an integral backstepping control strategy to optimize power extraction from a wind energy conversion system based on a permanent magnet synchronous generator. The control regulates the generator's rotational speed to track the optimal speed for maximum power point tracking. Simulation results show that the integral backstepping control achieves better performance than a conventional PI vector control in regulating speed during stochastic and step changes in wind speed, allowing for more effective maximum power point tracking.
Fuzzy logic Technique Based Speed Control of a Permanent Magnet Brushless DC...IJMER
This document summarizes a research paper that analyzes speed control of a permanent magnet brushless DC motor using fuzzy logic techniques. It begins with an introduction to brushless DC motors and their applications. It then describes the dynamics and modeling of a brushless DC motor drive system. Next, it discusses conventional PID speed control and introduces fuzzy logic control as an alternative method. The next sections provide details on fuzzy logic controller design and simulations comparing the performance of PID and fuzzy logic controllers under different operating conditions. The conclusions indicate that the fuzzy logic controller provides better damping and disturbance rejection compared to the PID controller.
This document summarizes a student project analyzing and simulating multilevel inverter topologies using MATLAB/Simulink. The objectives are to conduct a literature review on multilevel inverter configurations, develop and simulate different two-level and multilevel inverter topologies using PWM techniques, and determine the characteristics and harmonics levels. The scope includes applications like motor drives, DC transmission, FACTS, and renewable energy integration. Literature discusses how multilevel inverters synthesize voltages from multiple DC levels and are suitable for industrial high power applications. Diode-clamped, flying capacitor, and cascaded/H-bridge topologies will be studied. The methodology involves designing simulation models and analyzing performance results.
This document provides an overview of an introductory course on the design of three-phase induction motors. It outlines the objectives of designing a motor with a 1HP output power rating, 1500 RPM speed, and meeting other efficiency and size criteria. It then covers key topics in the electromagnetic design process like motor nameplate parameters, efficiency classes, frame sizes, dimensions, analytic design approach, classification of design variables, output equations, and calculation of various motor components.
The traction motor shed was established in 1973 to meet the maintenance needs of electric locomotive motors for Indian Railways. It performs reclamation, rewinding, and manufacturing of traction motor armatures, staters, and auxiliary machines. The shed has different sections for auxiliary motors, testing, millwright work, armature winding, stator winding, and coil manufacturing. It overhauls traction motors which use dynamic braking to generate electricity during braking and supply auxiliary loads in electric locomotives.
This document proposes a system to wirelessly charge electric vehicles using solar energy. The system would use solar panels to generate electricity, which would be stored in batteries and converted to AC power using an inverter. Coils placed in the vehicle and road would transmit the AC power wirelessly through electromagnetic induction to charge the vehicle battery as it drives. The objectives are to develop a renewable energy charging system and enable "charge as you drive" capability. Hardware components, a block diagram, voltage measurements between coils at different distances, and advantages/disadvantages are described.
This document summarizes different types of excitation systems for alternators. It discusses the function of excitation systems to supply direct current to the field winding and control the voltage and reactive power of alternators. The three main types covered are DC excitation systems, AC excitation systems, and static excitation systems. DC excitation systems use two small DC generators as exciters but are not commonly used for large alternators now. AC excitation systems include brushless and rotating thyristor types and have advantages like eliminating brushes. Static excitation systems have no rotating parts, are suitable for medium and high capacity alternators, and have benefits like smaller size and no windage losses. The document concludes that the selection of an excitation system depends on factors like the altern
Device Modeling and Simulation of DC Motor using LTspiceTsuyoshi Horigome
This document describes the simulation of a DC motor control circuit using LTspice. It includes models of a DC motor, timer IC, and specific Mabuchi motor. It details the manufacturer specifications, calculations to determine torque and back EMF constants, and transient simulations at no load and various load conditions. Simulation results are compared to measurement data to validate the motor model.
Wireless charging of Electric Vehicles (IEEE Paper 2017)Georget Eldhose
This document discusses wireless power transmission applied to electric vehicles. It begins with an introduction to electric vehicles and the need to reduce charging times. It then describes different charging systems and compares wireless to plug-in charging. The document outlines the typical components of a wireless charging system including power inverters, resonant tanks, and induction coils. It presents an experimental model of a small-scale wireless charging track for electric cars. Key advantages include reduced operating costs, lower maintenance than gas vehicles, and the ability to charge multiple vehicles simultaneously. However, initial installation costs are high and power transmission is limited by range. In conclusion, wireless charging is well-suited for electric vehicles by reducing recharging times and allowing charging on the go.
This document describes a power supply/battery charger with a full bridge resonant converter. The charger has an output of 26.4VDC at 33A maximum. It operates in constant current and constant voltage modes with protections against overvoltage, overtemperature, and undervoltage. The charger uses a pulse width modulated full bridge converter with a power factor correction boost converter on the input side and a full bridge ZVS resonant converter on the output side. Key components and operations of the power factor correction, DC-DC conversion, output rectification, and control sections are explained.
This document describes the modeling and simulation of a brushless DC motor drive system in MATLAB/Simulink. It includes models of the BLDC motor, three-phase inverter, current controller, speed controller and reference current generator. The motor is modeled using a three-phase variable model. A PI controller is used to control the current and an outer speed loop controls the rotor speed. Simulation results can be used to predict system behavior and determine controller parameters.
This seminar presentation summarizes the key aspects of linear induction motors (LIMs). It discusses the construction of LIMs, including their stator and rotor components. It describes the different types of LIMs according to their core shape, including iron core, ironless core, and slot-less designs. The presentation also covers the principles of operation of LIMs, the different forces involved, and various effects such as end effects and gap effects. It compares LIMs to conventional induction motors and rotary induction motors, outlines the advantages and disadvantages of LIMs, and discusses applications such as transportation and material handling where LIMs are commonly used.
star delta auto starter with forward reverse and motor protectionBHUPATI PRADHAN
It is a project on pure electrical engineering. Here three phase motor is starting from star to delta automatically by using some components.In other hand it also provide protection to the motor.
The document discusses simulation, analysis and open loop control of a multilevel inverter fed induction motor. It introduces multilevel inverters and their topologies like cascaded H-bridge, diode clamped and flying capacitor. It describes sinusoidal PWM techniques like in-phase disposition, phase opposition disposition and alternate phase opposition disposition. It models the phase disposition modulation technique by generating modulating sine waves, triangular carrier waves and firing pulses for 3-level and 5-level diode clamped inverters. Simulation results are presented for induction motor performance with 2-level and multilevel inverters under different operating conditions.
This document discusses transformer sizing using ETAP software. It explains that ETAP takes into account factors like ambient temperature, altitude, cooling type and expected future growth to determine the proper transformer size. The document provides details on ETAP's 2-winding transformer sizing module, which calculates the rated MVA, maximum MVA and impedance based on loading, installation factors and short circuit requirements. It also discusses how ETAP can be used to check transformer regulation during motor starts. The document concludes that transformer sizing calculations can be standardized using ETAP due to the harmony between ETAP's sizing module and manual calculations using formulas.
Power electronics refers to controlling and converting electrical power using semiconductor switches like silicon-controlled rectifiers. This allows control of power for applications like motor drives, power supplies, and inverters. Key devices include rectifiers, inverters, and choppers. Rectifiers convert AC to DC, inverters convert DC to AC, and choppers convert DC voltages. Power electronics is used widely in applications like heating/lighting control, motor drives, battery chargers, and electric vehicles.
Electric vehicle batteries have evolved from early lead-acid batteries to current lithium-ion batteries that provide over 300 km of range. Different battery types include lead-acid, nickel-metal hydride, sodium-nickel chloride, and lithium-ion. Charging can be conducted through conductive coupling to charge ports or inductively. Future battery innovations aim to further increase range and reduce charging times through technologies like carbon nanotubes and battery swapping systems.
The document provides information about the Electric Loco Shed in Ghaziabad, including:
1) It was established in 1976 and maintains locomotives such as WAP1, WAP4, WAP5, and WAP7.
2) It has an initial cost of 1.5 crores and currently holds 184 locomotives.
3) It describes the various sections in the shed such as the mechanical, electrical, and painting workshops which perform maintenance on locomotives.
The document discusses the history and components of battery electric vehicles (BEVs). It notes that the first human-carrying electric vehicle was tested in Paris in 1881. BEVs use electricity from batteries to power an electric motor rather than an internal combustion engine. The key components of BEVs are the battery charger, traction batteries, power converters, electric motor, motor controller, transmission system, and differential system. BEVs are further classified based on their energy storage sources into pure electric vehicles (PEVs/BEVs), fuel cell electric vehicles, ultracapacitor electric vehicles, and ultraflywheel electric vehicles.
The document summarizes information about Diesel Shed Ratlam, located in Madhya Pradesh, India. It was established in 1967 and maintains diesel locomotives. It discusses the types of locomotives - steam, diesel-electric, and electric. Diesel-electric locomotives became widely used because they don't produce smoke and have higher efficiency than steam. Traction motors, the main components of locomotives, are also described in terms of their construction, ratings, and operating principle.
This document discusses the design and analysis of a Single-Ended Primary-Inductor Converter (SEPIC) circuit. It provides an overview of SEPIC converters and how they allow the output voltage to be greater than, less than, or equal to the input voltage. The document then describes the methodology for analyzing a SEPIC circuit operating in continuous mode. It includes calculations for determining the output voltage, inductor and capacitor values, voltage ripple, current stresses, and MOSFET selection. Simulation results are presented and disadvantages of SEPIC converters are noted.
Nonlinear control of WECS based on PMSG for optimal power extraction IJECEIAES
This document presents a study that proposes an integral backstepping control strategy to optimize power extraction from a wind energy conversion system based on a permanent magnet synchronous generator. The control regulates the generator's rotational speed to track the optimal speed for maximum power point tracking. Simulation results show that the integral backstepping control achieves better performance than a conventional PI vector control in regulating speed during stochastic and step changes in wind speed, allowing for more effective maximum power point tracking.
Fuzzy logic Technique Based Speed Control of a Permanent Magnet Brushless DC...IJMER
This document summarizes a research paper that analyzes speed control of a permanent magnet brushless DC motor using fuzzy logic techniques. It begins with an introduction to brushless DC motors and their applications. It then describes the dynamics and modeling of a brushless DC motor drive system. Next, it discusses conventional PID speed control and introduces fuzzy logic control as an alternative method. The next sections provide details on fuzzy logic controller design and simulations comparing the performance of PID and fuzzy logic controllers under different operating conditions. The conclusions indicate that the fuzzy logic controller provides better damping and disturbance rejection compared to the PID controller.
Model Validation and Control of an In-Wheel DC Motor Prototype for Hybrid El...Scientific Review SR
In this paper, a mathematical model and a controller for a DC motor are developed for the
construction of an in-wheel motor. In-wheel motors can be used in hybrid electric vehicles to provide traction
force of front or rear wheels. The model identification is achieved using a simple and low cost data acquisition
system. An Arduino Uno embedded board system is used to collect data from sensors to a computer and for
control purposes. Data processing is performed using Matlab/Simulink. Validations of the devel oped
mathematical model and controller performance are carried out by comparing simulation and experimental results.
The results obtained show that the mathematical model is accurate enough to assist in speed controller design and
implementation.
Estimating the parameters of a geared DC motor is crucial in terms of its non-linear features. In this paper, parameters of a geared DC motor are estimated genetically. Mathematical model of the DC motor is determined by Kirchhoff’s law and dynamic model of its shafts and gearbox. Parameters of the geared DC motor are initially estimated by MATLAB/SIMULINK. The estimated parameters are defined as initial values for Genetic Algorithm (GA) to minimize the error of the simulated and actual angular trajectory captured by an encoder. The optimal estimated model of the geared DC motor is validated by different voltages as the input of the actual DC motor and its mathematical model. The results and numerical analysis illustrate it can be ascertained that GA is appropriate to estimate the parameters of platforms with non linear characteristics.
For Induction motor is a system that works at their speed, nevertheless there are applications at which the speed operations are needed. The control of range of speed of induction motor techniques is available. The robust control is used with induction motor and the performance of the system with the controller will be improved. The mathematical model to the controller, which were coded in MATLAB. The modeling and controller will be shown by the conditions of robustness of be less than one.
This document presents a method for tuning the parameters of a PID controller for a brushless DC motor using particle swarm optimization. PSO is used to find the optimal proportional, integral and derivative gains to minimize error metrics for the motor's step response. The BLDC motor is modeled in Simulink. PSO searches through potential solutions in a multi-dimensional space to determine PID parameters that produce the best step response with minimal overshoot, rise time, settling time and steady state error. The results show the PSO-tuned PID controller achieves better dynamic performance than other methods.
This paper presents a novel structure combining the port-controlled Hamiltonian (PCH) and Backstepping (BS) nonlinear control for the vector control of the six-phase induction motor (SPIM). In this new scheme, to improve the outer loop’s robustness, the BS technique using the integral tracking errors action is proposed in the speed and flux controllers design. The advantage of this proposed control law is not to increase the complexity of differential equation resolution due to being not increased system states numbers. To enhance more the performance of SPIM drives (SPIMD), port-controlled Hamiltonian (PCH) scheme is used in the inner current loop controllers. In this proposed PCH current controller, the stabilization of controller is achieved via system passivity. In that, the interconnection and damping matrix functions of PCH system are shaped so that the physical (Hamiltonian) system structure is preserved at the closed loop level and the closed loop energy function is equal to the difference between the physical energy of the system and the energy supplied by the controller. The proposed control design is based on combination PCH and BS techniques improve significantly performance and robustness. The proposed speed control scheme is validated by Matlab-Simulink software.
Speed and Torque Control of Mechanically Coupled Permanent Magnet Direct Curr...IDES Editor
A new controller is designed for speed and torque
control of a Permanent Magnet DC motor based on
measurements of speed and current. This research work
focuses on investigating the effects of control of the speed and
torque of two brushless dc motors that are mechanically
coupled. Two controller design methods: the Root Locus
method and Bode Plot method as well as two controllers:
Proportional-Integral-Derivative (PID) and Proportional-
Integral (PI) are used to obtain the control objectives of speed
control and torque control. The simulation is performed using
MATLAB/SIMULINK software. The effects of varying the
controller gains on the system performance is studied and
quantified. The simulation results show that the speed control
objectives of the motor are satisfied even in the case of torque
disturbance from the other motor.
Power Optimization and Control in Wind Energy Conversion Systems using Fracti...IRJET Journal
This document describes a proposed method for optimizing power extraction from a wind energy conversion system (WECS) using fractional order extremum seeking control (FOESC). The WECS uses an induction generator connected to the grid through a matrix converter. An inner loop nonlinear controller based on field oriented control is used to control the generator along with an outer loop maximum power point tracking controller. The proposed outer loop controller is a FOESC, which is a non-model based optimization method that can track the maximum power point faster than conventional extremum seeking control methods. Simulation results are presented to demonstrate the effectiveness of using FOESC for maximum power point tracking in a WECS.
This document summarizes a research paper that proposes an adaptive PID speed controller for a brushless DC motor. The paper begins with an introduction to brushless DC motors and common speed control methods like PI, PID, fuzzy logic and PWM controllers. It then discusses developing an adaptive PID controller that combines a PID controller with an auto-tuning method. This allows the controller to adapt to changing system parameters. The paper describes modeling the BLDC motor and speed control systems in MATLAB/Simulink. Simulation results are presented and analyzed to verify the adaptive PID controller's performance. The adaptive PID controller is found to improve system adaptability compared to other control methods.
Brushless Dc Motor Speed Control Using Proportional-Integral And Fuzzy Contro...IOSR Journals
This document summarizes and compares proportional-integral (PI) control and fuzzy logic control for speed regulation of a permanent magnet brushless DC motor. It first provides background on brushless DC motors and reviews previous literature on speed control techniques. It then describes implementing PI control in closed loop simulations, comparing three tuning methods. Fuzzy logic control is also implemented using two inputs (speed and current errors) and simulation results are presented. Both controllers are able to regulate speed under varying load torques and reference speeds, with fuzzy logic control having better performance in terms of overshoot, settling time and speed drop compared to PI control.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
In this paper, we first write a description of the operation of DC motors taking into account which parameters the speed depends on thereof. The PID (Proportional-Integral-Derivative) controllers are then briefly described, and then applied to the motor speed control already described , that is, as an electronic controller (PID), which is often referred to as a DC motor. The closed loop speed control of a Brush DC motor is developed applying the well-known PID control algorithm. The objective of this work is to designed and simulate a new control system to keep the speed of the DC motor constant before variations of the load (disturbances), automatically depending to the PID controller. The system was designed and implementation by using MATLAB/SIMULINK and DC motor.
Improved Rotor Speed Brushless DC Motor Using Fuzzy Controllerijeei-iaes
This document describes research on improving the rotor speed of a brushless DC motor using a fuzzy controller. It begins with background on BLDC motors and their advantages over brushed DC motors. It then provides the mathematical model of a BLDC motor. Next, it discusses implementing a PID fuzzy controller for a BLDC motor in Simulink and the design procedure, which involves first designing a conventional PID controller, then a linear fuzzy PID controller with the same performance, and finally adjusting the fuzzy inference system to create a nonlinear fuzzy PID controller.
This document describes a study on modeling and simulating a permanent magnet brushless DC (PMBLDC) motor drive system in MATLAB/Simulink. The authors develop a model of a PMBLDC motor with a 120-degree inverter system and implement closed-loop speed control using a PI controller. Simulation results confirm the validity of the model and controller. The paper examines the effects of load and inertia changes on the motor's speed performance.
A Novel Technique for Tuning PI-controller in Switched Reluctance Motor Drive...IJECEIAES
This paper presents, an optimal basic speed controller for switched reluctance motor (SRM) based on ant colony optimization (ACO) with the presence of good accuracies and performances. The control mechanism consists of proportional-integral (PI) speed controller in the outer loop and hysteresis current controller in the inner loop for the three phases, 6/4 switched reluctance motor. Because of nonlinear characteristics of a SRM, ACO algorithm is employed to tune coefficients of PI speed controller by minimizing the time domain objective function. Simulations of ACO based control of SRM are carried out using MATLAB /SIMULINK software. The behavior of the proposed ACO has been estimated with the classical Ziegler- Nichols (ZN) method in order to prove the proposed approach is able to improve the parameters of PI chosen by ZN method. Simulations results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on classical Ziegler-Nichols method.
IRJET - Flyback Converter based BLDC Motor Drives for Power Device ApplicationsIRJET Journal
This document discusses a brushless DC (BLDC) motor drive system using a flyback converter and fuzzy logic controller. BLDC motors have advantages over brushed DC motors like higher efficiency, higher power density, and less maintenance. A literature review covers different sensorless control methods for BLDC motors. A MATLAB/Simulink model is developed for a BLDC motor using a fuzzy logic speed controller. Simulation results show the motor speed is controlled to within 5 rpm of the reference speed using this method. The fuzzy logic controller provides better performance than traditional PID control. In conclusion, fuzzy logic control is effective for BLDC motor drives and this method can be implemented at low cost.
Simulation and Analysis of Modified DTC of PMSMIJECEIAES
This research paper describes the simulation and analysis of the modified DTC for Surface mounted Permanent Magnet Synchronous Motor (SPMSM) using PI controller. Among all of the various drive systems,PMSM is widely used for accurate speed and torque control, with greater efficiency, superior torque to inertia and high power density.The Conventional DTC secheme widely used for this purpose but it is failed to achieve desirable performance of the system for which the modified DTC secheme is propsed.The modified DTC algorithm controls the voltage vectors, directly from a simple look up table depending on outcome of the torque and indirectly flux controllers.The overall drive system can be implemented in SIMULINK/MATLAB environment.The modified DTC is validated with loading conditions.The simulated results are focused on the speed, settling time at loaded conditions, torque and flux linkages ripple and THD in the phase current for modified DTC applied to SPMSM.
We focus a modern methodology in this paper for adding the fuzzy logic control as well as sliding model control. This combination can enhance the MLS position control robustness and enhanced performance of it.In the start, for an application in an area to control the loops placement and position for the synchronous motor what has permanent magnetic linearity we tend to control the fuzzy sliding mode control. To resolve the chattering issues a designed controller is investigated and, in this way, steady state motion in sliding with higher accuracy is obtained. In this case, method of online tuning with the help of fuzzy logic is used in order to adjust the thickness of boundary layer and switching gains.For the suggested scheme technique, the outcomes of simulation suggest that with the classical SMC the accurate state and good dynamic performance is compared due to force chattering resistance, response by quick dynamic force and external disturbance elements and robustness against them.
Mainly the DC motors are employed in most of the application. The main objective is to Regulate the DC motor system. A motor which displays the appearances of a DC motor but there is no commutator and brushes is called as brushless DC motor. These motors are widespread to their compensations than other motors in relationships of dependability, sound, efficiency, preliminary torque and longevity. To achieve the operation more reliable and less noisy, brushless dc motors are employed. In the proposed work, dissimilar methods of speed control are analysed. In real time submission of speed control of BLDC motor, numerous strategies are executed for the speed control singularity. The modified approaches are the employment of PI controller, use of PID controller and proposed current controller.
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Fossil fuel consumption increased quickly, contributing to climate change
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Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
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CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
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governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
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The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
john krisinger-the science and history of the alcoholic beverage.pptx
Comparison of cascade P-PI controller tuning methods for PMDC motor based on intelligence techniques
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 1, February 2022, pp. 1~11
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i1.pp1-11 1
Journal homepage: http://ijece.iaescore.com
Comparison of cascade P-PI controller tuning methods for
PMDC motor based on intelligence techniques
Kareem Ghazi Abdulhussein, Naseer Majeed Yasin, Ihsan Jabbar Hasan
Department of Electrical Power Engineering Techniques, Middle Technical University, Babylon, Iraq
Article Info ABSTRACT
Article history:
Received Jan 21, 2020
Revised Jun 28, 2021
Accepted Jul 20, 2021
In this paper, there are two contributions: The first contribution is to design a
robust cascade P-PI controller to control the speed and position of the
permanent magnet DC motor (PMDC). The second contribution is to use
three methods to tuning the parameter values for this cascade controller by
making a comparison between them to obtain the best results to ensure
accurate tracking trajectory on the axis to reach the desired position. These
methods are the classical method (CM) and it requires some assumptions,
the genetic algorithm (GA), and the particle swarm optimization algorithm
(PSO). The simulation results show the system becomes unstable after
applying the load when using the classical method because it assumes
cancellation of the load effect. Also, an overshoot of about 3.763% is
observed, and a deviation from the desired position of about 12.03 degrees is
observed when using the GA algorithm, while no deviation or overshoot is
observed when using the PSO algorithm. Therefore, the PSO algorithm has
superiority as compared to the other two methods in improving the
performance of the PMDC motor by extracting the best parameters for the
cascade P-PI controller to reach the desired position at a regular speed.
Keywords:
Cascade P-PI controller
Classical method
Genetic algorithm
Particle swarm optimization
PMDC
This is an open access article under the CC BY-SA license.
Corresponding Author:
Kareem Ghazi Abdulhussein
Department of Electrical Power Engineering Techniques, Middle Technical University
Babylon, Iraq
Email: engkarim1984@gmail.com
1. INTRODUCTION
A permanent magnet DC motor (PMDC) is a simple type of DC motor where field windings have
been replaced by permanent magnets. This motor works with the same basic principle as a shunt-connected
motor but the difference between them is that the permanent magnet generates the required flux instead of the
field windings [1], [2]. The power of these motors is less than that of the shunt-connected motors because the
flux generated by the permanent magnet is less than the flux generated by the field windings. Therefore, most
types of small DC motors are PMDC motors and these motors operate at high speed and low torque [1], [3].
There are many applications for the PMDC motor, such as moving windows in cars and front areas of cars. It
is also used in children's toys and household appliances like food mixers and others [4], [5] and its important
use in computer numerical control (CNC) machines as an actuator, electric vehicles, and robotics [6], [7].
The main problem statement in this paper is how to get the best parameter values for the
proportional integral derivative (PID) cascade controller to give the most accurate results for tracking the
trajectory of the reference position to reaching the desired position at a regular speed. To address this
problem, a comparison was made between the three methods classical method (CM), genetic algorithm (GA),
and particle swarm optimization algorithm (PSO) to extract the best parameters of the cascading controller, in
addition to using the objective function, integral time absolute error (ITAE) to reduce the error between the
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input and output of this system. The cascade P-PI controller used in this paper consists of three controllers:
the current controller as an inner loop, the speed controller, and the position controller as outer loops [8]. P,
PI, and PI controllers are used for position, speed, and current respectively [9].
The purpose of using the cascade P-PI controller is due to many reasons, the most important of
which is robustness to reduce or reject external disturbances and back to the steady-state in a short time [10]-
[12]. There are several methods to tuning the parameters of the cascade P-PI controllers such as the ziegler-
nichols (Z-N) method, cohen-coon method, neural network, and fuzzy logic [13], [14]. Particle swarm
optimization (PSO) algorithm [15], [16] and genetic algorithm [17].
To compare this work with other researcher's work in the same field, the following literature review
was made: In 2014, Mustafa et al. [18] presented a brushless DC (BLDC) motor speed control system using
GA. In 2015, Taha et al. [9] used three methods to control the cascade control system. In 2018, Wisam et al.
[19] presented a system for controlling PMDC speed using GA and direct search (DS) algorithms. In 2019,
Fadhel et al. [20] used a fractional PID controller to control PMDC speed based on PSO. In 2021, Ahmed et
al. [21] presented a system to control the position and speed of a servo motor. This paper is organized as: The
second section contains the mathematical model of the PMDC motor and explaining the general structure of
the system, in the third section, the three tuning methods are explained with the objective function (ITAE).
The fourth section contains the results and comparison, and the final section contains the conclusion.
2. MATHEMATICAL MODEL AND GENERAL STRUCTURE
2.1. Mathematical model of PMDC motor
Figure 1 shows the equivalent circuit of a PMDC motor consisting of an armature resistance (Ra)
and inductance (La) connected in series. The back emf (Ea) is generated when the flux lines generated by the
permanent magnet are cutting and its direction opposite to the direction of the applied voltage. While the
mechanical part consists of coefficient of friction ( Bm) and moment of inertia ( Jm). In addition to other
parameters are the back emf constant (Kv) and torque constant (Kt). All components or parameters of this
PMDC motor and their values are shown in Table 1 dna Figure 2 shows the block diagram of PMDC motor.
Figure 1. Equivalent circuit of PMDC motor [22]
Table 1. PMDC parameters [9]
Motor parameters Value
Torque constant Kt =2.35 Nm/A
Armature inductance La=2.61*10-10
H
Armature resistance Ra=2.61 Ω
Inertia of the motor Jm=0.068 kg.m2
Friction constant Bm=0.008 Nms/rad
Back emf constant Kv=2.35 Vs/rad
Nominal load T1=17.6 Nm
Nominal voltage Va=230 v
Figure 2. The block diagram of PMDC motor [22]
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The electrical and the mechanical equations corresponding to block diagram from Figure 2 are [22].
𝑣𝑎 (𝑡) = 𝑒𝑎 (𝑡) + 𝑅𝑎 𝑖𝑎 (t) + 𝐿𝑎
𝑑
𝑑𝑡
𝑖𝑎 (𝑡) (1)
𝑒𝑎 (𝑡) = 𝑘𝑒 𝜔𝑚(𝑡) (2)
𝑇𝑚 (𝑡) − 𝑇𝐿 = 𝐽𝑚
𝑑
𝑑𝑡
𝜔𝑚(𝑡) + 𝐵𝑚𝜔𝑚(𝑡) (3)
𝑇𝑚 (𝑡) = 𝐾𝑡𝑖𝑎 (t) (4)
By using Laplace transformation for the (1)-(4) we obtain;
𝑉
𝑎 (𝑠) = 𝐸𝑎(𝑠) + 𝑅𝑎 𝐼𝑎(𝑠) + 𝑆𝐿𝑎𝐼𝑎(𝑠) (5)
𝐸𝑎(𝑠) = 𝑘𝑒 𝜔𝑚(𝑠) (6)
𝑇𝑚 (𝑠) − 𝑇𝐿 = 𝑆 𝐽𝑚𝜔𝑚(𝑠) + 𝐵𝑚𝜔𝑚(𝑠) (7)
𝑇𝑚 (𝑠) = 𝐾𝑡 𝐼𝑎(s) (8)
The overall transfer functions are defined for speed and position control of PMDC motor, respectively.
𝜔𝑚(𝑠)
𝑉𝑎 (𝑠)
=
𝑘𝑒
𝐽 𝐿𝑎𝑆2 +( 𝐽𝑅𝑎 +𝐵𝐿𝑎 )𝑆+𝐵𝑅𝑎 + 𝑘𝑒
2 (9)
𝜃(𝑠)
𝑉𝑎 (𝑠)
=
𝑘𝑒
𝐽 𝐿𝑎𝑆3 +( 𝐽𝑅𝑎 +𝐵𝐿𝑎 )𝑆2 +( 𝐵𝑅𝑎 + 𝑘𝑒
2
)𝑆
(10)
Where 𝑘𝑒 is equal to 𝐾𝑡 [23] and
Ra = armature resistance (Ω)
La = armayure inductance (H)
Ea = electromotive force or back emf (v)
θ(s) = actual position (rad)
Va = nominal voltage (v)
Jm = moment inertia (kg.m2
)
TL = nominal load torque (Nm)
Kt = torque constant (Nm/A)
Kv = back emf constant (v.sec/rad)
Bm = friction coefficient (Nm.s/rad)
ωm = motor velocity (rad/sec)
2.2. General structure of the system
The general structure of the system consists of a cascade P-PI controller as shown in Figure 3. This
controller consists of three (P, PI) controllers for current, speed, and position. The output of the position
controller represents the reference speed, the output of the speed controller represents the reference current,
and the output of the current controller represents the control voltage (VC).
Figure 3. General structure of cascade PID control system for PMDC
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An advantage of the cascade control system, in addition to the other advantages, is the ability to set
limits in order to protect the PMDC motor and the power electronic converter. In this paper, limits are placed
on the speed reference by an amount not exceeding the motor's rated speed. As well as the limit, placed on
the reference voltage exiting the PI current not exceeding the motor's applied voltage [23].
3. TUNING METHODS
3.1. Classical method (CM)
Figure 4 represents the first control loop or the inner control loop. Since this method assumes some
assumptions to simplify the circuit, the effect of the torque load is neglected, and the effect of Ea is neglected
because the value of Jm is high [9], [23]. As a result, the simplified current control loop as shown in Figure 5.
Figure 4. Inner current control loop
Figure 5. Simplified inner current control loop
From Figure 5 the transfer function of the current control loop, 𝐺𝐼 𝑜𝑙(s), and it can be written like in
(11) where, 𝑘𝑖𝐼 is the integral gain, 𝑘𝑝𝐼 is the proportional gain of the controller, and the electrical time
constant 𝜏𝑒, is calculated using (12).
𝐺𝐼 𝑜𝑙(s) =
𝑘𝑖𝐼
𝑆
(1 +
𝑆
𝑘𝑖𝐼
𝑘𝑝𝐼
⁄
) ∗ (
1
𝑅𝑎
⁄
1+𝑠𝜏𝑒
) (11)
𝜏𝑒 =
𝐿𝑎
𝑅𝑎
(12)
The (13) is used to cancel the motor pole, which is formulated in (11).
𝑘𝑖𝐼
𝑘𝑝𝐼
=
1
𝜏𝑒
(13)
The cancellation of the pole in the motor transfer function can be illustrated as:
𝐺𝐼 𝑜𝐼 =
𝑘𝑖𝐼
𝑆
(1
𝑆
𝑘𝑖𝐼
𝑘𝑝𝐼
⁄
) ∗ (
1
𝑅𝑎
⁄
1+𝑠𝜏𝑒
) 𝑂𝑅 𝐺𝐼 𝑜𝐼 =
𝑘𝑖𝐼
𝑆
(1 + 𝑠𝜏𝑒) ∗ (
1
𝑅𝑎
⁄
1+𝑠𝜏𝑒
)
Finally, in the open-loop transfer function, the bandwidth (crossover) frequency (𝜔𝑐𝑖) It is represented by
(14).
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ωci =
𝒌𝒊𝑰
𝑹𝒂
(14)
Also, the crossover frequency of the current open-loop can be calculated from the following
relationship 𝜔𝑐𝑖 = 2𝜋𝑓𝑐𝑖 and its value about ten times smaller than the switching frequency for DC-DC
converter [9]. So, the parameter values of the PI current controller can be found from (13), (14). To find the
speed loop parameters it is assumed that the closed current loop is ideal for design purposes and is
represented by the unity [23] as shown in Figure 6.
Figure 6. Outer speed loop
From Figure 6, the transfer function of this loop 𝐺𝑠 𝑜𝑙(s), can be represented in (15).
𝐺𝑆 𝑜𝑙(s) =
𝑘𝑖𝑆
𝑆
(1 +
𝑆
𝑘𝑖𝑆
𝑘𝑝𝑆
⁄
) ∗ (
1
𝐵𝑚
⁄
1+𝑠𝜏𝑚
) (15)
Where
𝜏𝑚= mechanical time constant =
𝐽𝑚
𝐵𝑚
(16)
Just like the inner current control loop the pole in the mechanical part of the motor will be canceled as show
in (17). Here, 𝑘𝑖𝑆 is the integral gain and, 𝑘𝑝𝑆 is the proportional gain of the controller.
𝑘𝑖𝑆
𝑘𝑝𝑆
=
1
𝜏𝑚
(17)
The bandwidth (crossover) frequency of the speed control loop, 𝜔𝑐𝑠 can be chosen to be ten times lower than
ωci and it can be represented by (18).
ωcs =
𝑘𝑖𝑆 .𝑘𝑡
𝐵𝑚
(18)
Thus, 𝑘𝑖𝑆 and 𝑘𝑝𝑆 can be calculated using (16) and (18).
Finally, the last outer loop is the position control loop it can be illustrated in Figure 7. To find the
k_pP parameter of the position loop it is assumed that the speed loop is perfect and is represented by unity
[23] as shown in Figure 7 the open-loop transfer function for position control is given in the (19). The value
of the position parameter k_pP can be calculated from (20) where the bandwidth frequency (𝜔𝑝𝑠) is chosen
to be ten times smaller than (ωcs).
Figure 7. Outer position loop
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𝐺𝑃 𝑜𝑙(s) =
𝑘𝑝𝑃
𝑆
(19)
ωps = 𝑘𝑝𝑃 (20)
3.2. Particle swarm optimization (PSO)
Particle swarm algorithm (PSO) is proposed by Kennedy and Eberhart and it was modified to improve
its performance by adding a new parameter called inertia weight [24]. The PSO algorithm bases on swarm
intelligence techniques for the observation of social behavior of moving organisms such as a gathering of fish or
birds. This algorithm is related to the computational method to solve the problem, for example, bird flocks aim
to find eating behaviors and use repeated steps to reach the best solutions. This means that the candidate
solutions are particles that move in the search space based on a specific formula above the particle position. Each
particle's movement is affected by its local value, and its objective is to reach the best-known positions in the
search space by updating better positions found by other particles.
The algorithm is begun by establishing the starting position and speed vectors. At each iteration, the
best value is determined by evaluating position and speed vectors. Every particle has variables and dimensions,
and these variables are problems that need to be solved. If the problem consists of five different variables, the
particles’ dimension should be chosen as five. Each particle’s best value is called a local best value and recorded
into the P best matrix. After each iteration, the best value for each particle is updated if the best new value is
found to control each current particle and the previous positions. Besides, the position is affirmed as the global
best after controlling for the best matrix values at each iteration which continues until it reaches the specified
number, as the last updated value for the best position represents the optimal value. This algorithm depends
mainly on finding the position of each particle with the best local value, as well as finding the best general
swarm position in each iteration. The position and speed are updated at each iteration based on (21) and (22)
[20], [25].
𝑉𝑖,𝑗 (𝑡 + 1) = 𝑊. 𝑉𝑖,𝑗 (𝑡 ) + 𝑟1𝑐1[𝑃𝑏𝑒𝑠𝑡𝑖,𝑗(𝑡) − 𝑋𝑖,𝑗 (𝑡)]
+ 𝑟2𝑐2[𝐺𝑏𝑒𝑠𝑡𝑖,𝑗(𝑡) − 𝑋𝑖,𝑗 (𝑡)] (21)
𝑋𝑖,𝑗(𝑡 + 1) = 𝑉𝑖,𝑗 (𝑡 + 1) + 𝑋𝑖,𝑗 (𝑡 ) (22)
Where
i = Particle index
j = Dimension number
t = Iteration
𝑉𝑖,𝑗(𝑡) = Current velocity
𝑋𝑖,𝑗 (𝑡) = Current position
𝑉𝑖,𝑗(𝑡 + 1) = Velocity updated or new velocity
𝑋𝑖,𝑗 (𝑡 + 1) = Position updated or new position
W = Weighted inertia its value between 0 and 1
𝑟1 , 𝑟2 = Random coefficients there values between 0 and 1
𝑐1 , 𝑐2 = Acceleration coefficients there values between 0 and 2
In Table 2 are the parameters of the PSO used in this work and Figure 8 shows the flowchart of the
particle swarm optimization algorithm.
Table 2. PSO parameters
PSO Parameters Value
Iteration 100
Swarm size 20
No. dimension 5
Weighted inertia 0.9
C1 2
C2 1.5
LU, UB 0, 300
3.3. Genetic algorithm
In 1970, Holland introduced the genetic algorithm (GA). This method is based on the principle of
evolution and natural selection. As an optimizer, this algorithm begins by selecting a specific number of
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chromosomes from a primary set, and each chromosome represents a solution to a specific problem and its
performance is evaluated by the fitness function [18]. The present parents produce new offspring called the
new generation, as this new generation is supposed to reach a better solution than previous parents, due to the
process of evolution. This mechanism is repeated for several successive generations until one of them obtains
the optimal solution for the system. Since the process is associated with many phases, Figure 9 shows a
flowchart of the genetic algorithm [26]. Table 3 represents the parameters of the GA that were used in this
work.
Figure 8. PSO flowchart [27] Figure 9. Genetic algorithm process flowchart [26]
Table 3. GA parameters
GA Parameters Value
Generation 100
Population size 20
No. dimension 5
Crossover 0.6
LU, UB 0, 300
When designing any control system for a particular physical process, certain criteria must be chosen
to provide the best performance. These criteria are known as objective functions. These functions reduce the
error between the input and output and have several types. In this work, a performance indicator was used to
reduce the error in the position signal called an integral absolute time error (ITAE) and can be represented by
(23).
𝐼𝑇𝐴𝐸 = ∫ 𝑡 | 𝑒 (𝑡) |𝑑𝑡
∞
0
(23)
4. RESULTS AND COMPARISON
The simulation of the system was performed using MATLAB 2019, and the system was tested after
entering the cascade P-PI parameters extracted from the three tuning methods. Table 4 shows the values of
these parameters extracted from each method. The system has also been tested in other cases, such as the load
condition, no-load, and single or multiple reference position to ensure the PMDC motor rotates 360 degrees
or several revolutions. Table 5 shows the performance parameters for the system at load case, which were
obtained from each tuning method.
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Table 4. PID parameters values
PID Parameters CM GA PSO
KP. Position 125.6637 244.3101 173.1189
KP. Speed 36.3623 98.7314 269.2364
KI. Speed 4.2779 291.317 3.6981
KP. current 32.7982 101.5191 216.5567
KI. current 32798 268.9725 23.8736
Table 5. The values of the performance criteria for each tuning method
Performance criteria CM GA PSO
Rise time 2.129 sec 2.033 sec 2.053 sec
Settling time 0.3 sec 0.2 sec 0.2 sec
overshoot 7.558% 3.763% 0
Through the simulation results attached in the appendix, and for the purpose of obtaining the best
results, the following comparison was made between the three tuning methods:
a) At no-load case, high overshoot is observed in speed when using the CM method as shown in
Figures 10(a) and 10(b), and after applying the full load at the fifth second, the system becomes unstable
because this method assumes neglecting the load effect to simplify the system as shown in Figures 11(a)
and 11(b).
b) When using the GA algorithm and at the no-load case, an overshoot was observed about 3.646 %, and a
deviation of 12.03° was observed from the desired position, as well as a high overshoot was observed in
the speed as shown in Figures 12(a) and 12(b). After applying full load, a very small deviation of about
0.0003 rad was observed. This is an indication of the robustness of the cascade P-PI controller in
rejecting external disturbances as shown in Figures 13(a) and 13(b).
(a) (b)
Figure 10. These figures are: (a) position control at no load case, and (b) speed control at no load case
(a) (b)
Figure 11. These figures are: (a) position control at load case, and (b) speed control at load case
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(a) (b)
Figure 12. These figures are: (a) position control at no load case and (b) speed control at no load case
(a) (b)
Figure 13. These figures are: (a) position control at load case, and (b) speed control at load case
c) When using the PSO algorithm, there was no deviation from the position or overshoot in speed or
position in both cases load and no-load in addition to the accurate tracking trajectory to reach the
position with high accuracy as shown in Figures 14(a), (b) and Figures 15(a), (b). The negative speed
means that the PMDC motor is rotating in the opposite direction. Therefore, the simulation results and
the comparison proved that the particle swarm optimization algorithm gave better results than the other
two methods.
(a) (b)
Figure 14. These figures are: (a) position control at no load case, and (b) speed control at no load case
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(a) (b)
Figure 15. These figures are: (a) position control at load case, and (b) speed control at load case
5. CONCLUSION
This paper presents a comparison between two intelligence methods which are PSO and GA
algorithms in addition to the traditional method CM. These methods are used to extract the best parameters of
the cascade P-PI controller to control the speed and position of the PMDC motor to reach the desired
position. The simulation results prove the effectiveness of the PSO algorithm in terms of no overshoot as
compared with CM and GA methods which have an overshoot about 7.558% and 3.763%, respectively. Also,
there is no deviation in position when using the PSO algorithm while a deviation about 12.03 degrees is
observed when using GA algorithm. So, the PSO algorithm has superiority in improving the overall
performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach
the desired position as compared to the other two methods.
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