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Frontiers of Electrical and Electronic Engineering

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ISSN 2095-2740(Online)

CN 10-1028/TM

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Front Elect Electr Eng    2012, Vol. 7 Issue (4) : 477-489    https://doi.org/10.1007/s11460-012-0211-1
RESEARCH ARTICLE
Experimental verification of chopper fed DC series motor with ANN controller
M. MURUGANANDAM(), M. MADHESWARAN
Centre for Advanced Research, Muthayammal Engineering College, Rasipuram 637408, Tamilnadu, India
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Abstract

In this article an artificial neural network (ANN) has been designed for the control of DC series motor through a DC chopper (DC-DC buck converter). The proportional-integral-derivative (PID)-ANN speed controller controls the motor voltage by controlling the duty cycle of the chopper thereby the motor speed is regulated. The PID-ANN controller performances are analyzed in both steady-state and dynamic operating condition with various set speeds and various load torques. The rise time, maximum overshoot, settling time, steady-state error, and speed drops are taken for comparison with conventional PID controller and existing work. The training samples for the neuron controller are acquired from the conventional PID controller. The PID-ANN controller performances are analyzed in respect of various load torques and various speeds using MATLAB simulation. Then the designed controllers were experimentally verified using an NXP 80C51 based microcontroller (P89V51RD2BN). It was found that the hybrid PID-ANN controller with DC chopper can have better control compared with conventional PID controller.
Keywords DC series motor      proportional-integral-derivative (PID) controller      artificial neural network (ANN) controller      DC chopper      speed control      MATLAB simulink     
Corresponding Author(s): MURUGANANDAM M.,Email:murugan_m1@yahoo.com   
Issue Date: 05 December 2012
 Cite this article:   
M. MURUGANANDAM,M. MADHESWARAN. Experimental verification of chopper fed DC series motor with ANN controller[J]. Front Elect Electr Eng, 2012, 7(4): 477-489.
 URL:  
https://academic.hep.com.cn/fee/EN/10.1007/s11460-012-0211-1
https://academic.hep.com.cn/fee/EN/Y2012/V7/I4/477
Fig.1  Flow chart for the proposed hybrid PID-ANN controller
Fig.1  Flow chart for the proposed hybrid PID-ANN controller
Fig.1  Flow chart for the proposed hybrid PID-ANN controller
Fig.1  Flow chart for the proposed hybrid PID-ANN controller
Fig.1  Flow chart for the proposed hybrid PID-ANN controller
Fig.2  Block diagram of hybrid PID-ANN controller based DC drive
Fig.2  Block diagram of hybrid PID-ANN controller based DC drive
Fig.2  Block diagram of hybrid PID-ANN controller based DC drive
Fig.2  Block diagram of hybrid PID-ANN controller based DC drive
Fig.2  Block diagram of hybrid PID-ANN controller based DC drive
Fig.3  Equivalent circuit of DC series motor
Fig.3  Equivalent circuit of DC series motor
Fig.3  Equivalent circuit of DC series motor
Fig.3  Equivalent circuit of DC series motor
Fig.3  Equivalent circuit of DC series motor
Fig.4  DC-DC converter circuit and waveform
Fig.4  DC-DC converter circuit and waveform
Fig.4  DC-DC converter circuit and waveform
Fig.4  DC-DC converter circuit and waveform
Fig.4  DC-DC converter circuit and waveform
operating modeswitch positionconverter output voltageVoload currentio
motoring (Mode 1)freewheeling (Mode 2)Mode 1Mode 2
forward motoringMOSFET (Q) ONdiode (DF) ONVs0+ ve
Tab.1  DC-DC converter switching operation
Fig.5  Simulink model of DC series motor
Fig.5  Simulink model of DC series motor
Fig.5  Simulink model of DC series motor
Fig.5  Simulink model of DC series motor
Fig.5  Simulink model of DC series motor
Fig.6  Simulink model of the system with conventional PID controller
Fig.6  Simulink model of the system with conventional PID controller
Fig.6  Simulink model of the system with conventional PID controller
Fig.6  Simulink model of the system with conventional PID controller
Fig.6  Simulink model of the system with conventional PID controller
input datatarget data
errorchange in errorcorresponds to δ
1.0000-0.0005200020
0.8573-0.0004-19160
0.7334-0.0004-10409
0.6271-0.0003-4932
0.5356-0.0003-7337
0.4572-0.0002-6190.2
0.3898-0.0002239.08
Tab.2  Sample data from PID controller
Fig.7  Structure of trained neural network
Fig.7  Structure of trained neural network
Fig.7  Structure of trained neural network
Fig.7  Structure of trained neural network
Fig.7  Structure of trained neural network
Fig.8  ANN parameter variation during training
Fig.8  ANN parameter variation during training
Fig.8  ANN parameter variation during training
Fig.8  ANN parameter variation during training
Fig.8  ANN parameter variation during training
Fig.9  Structure of the artificial neuron controller using MATLAB
Fig.9  Structure of the artificial neuron controller using MATLAB
Fig.9  Structure of the artificial neuron controller using MATLAB
Fig.9  Structure of the artificial neuron controller using MATLAB
Fig.9  Structure of the artificial neuron controller using MATLAB
Fig.10  Simulink model of the proposed system with ANN controller
Fig.10  Simulink model of the proposed system with ANN controller
Fig.10  Simulink model of the proposed system with ANN controller
Fig.10  Simulink model of the proposed system with ANN controller
Fig.10  Simulink model of the proposed system with ANN controller
DC motor parametersvalue
motor ratingDC supply voltagemotor rated currentinertia constant Jdamping constant Barmature resistance Raarmature inductance Lamotor speedarmature voltage constant Kafresidual magnetism voltage constant Kres5 HP220 V18 A0.0465 kg·m20.005 N·m·s/rad1 Ω0.032 H1800 rpm0.027 H0.027 V·s/rad
Tab.3  DC motor specifications
Fig.11  Pulse, output voltage, motor current, and speed with respect to time
Fig.11  Pulse, output voltage, motor current, and speed with respect to time
Fig.11  Pulse, output voltage, motor current, and speed with respect to time
Fig.11  Pulse, output voltage, motor current, and speed with respect to time
Fig.11  Pulse, output voltage, motor current, and speed with respect to time
Fig.12  Performance of controller for speed variation from 750 to 1250 rpm at 4 s and from 1250 to 1800 rpm at 7 s.
Fig.12  Performance of controller for speed variation from 750 to 1250 rpm at 4 s and from 1250 to 1800 rpm at 7 s.
Fig.12  Performance of controller for speed variation from 750 to 1250 rpm at 4 s and from 1250 to 1800 rpm at 7 s.
Fig.12  Performance of controller for speed variation from 750 to 1250 rpm at 4 s and from 1250 to 1800 rpm at 7 s.
Fig.12  Performance of controller for speed variation from 750 to 1250 rpm at 4 s and from 1250 to 1800 rpm at 7 s.
time domain specificationsset speed change from 0 to 750 rpmset speed change from 750 to 1250 rpmset speed change from 1250 to 1800 rpm
conventional PIDPID-neuro (proposed system)conventional PIDPID-neuro (proposed system)conventional PIDPID-neuro (proposed system)
max. over shoot/%8.70.315.50.194.30.15
settling time/s2.20.41.40.31.20.28
Tab.4  Time domain specifications of ANN and PID controllers for different set speed changes with 10% load
Fig.13  Performance of controller for load variation from 10% to 25%, 25% to 50%, and 50% to 100% at 3, 5.5, and 8 s, respectively
Fig.13  Performance of controller for load variation from 10% to 25%, 25% to 50%, and 50% to 100% at 3, 5.5, and 8 s, respectively
Fig.13  Performance of controller for load variation from 10% to 25%, 25% to 50%, and 50% to 100% at 3, 5.5, and 8 s, respectively
Fig.13  Performance of controller for load variation from 10% to 25%, 25% to 50%, and 50% to 100% at 3, 5.5, and 8 s, respectively
Fig.13  Performance of controller for load variation from 10% to 25%, 25% to 50%, and 50% to 100% at 3, 5.5, and 8 s, respectively
time domain specificationsload change from 10% to 25%load change from 25% to 50%load change from 50% to 100%
conventional PIDPID-ANN (proposed System)conventional PIDPID-ANN (proposed System)conventional PIDPID-ANN (proposed System)
max. speed drop/%0.66—1.10.022.80.2
recovery time/s0.17—0.680.0041.30.035
steady-state error/rpm-15±0.5-20±0.4-36±0.3
Tab.5  Time domain specifications of ANN and PID controllers for different load changes with rated speed
Fig.14  Load variation form 10% to 80% at 4 s for both controllers
Fig.14  Load variation form 10% to 80% at 4 s for both controllers
Fig.14  Load variation form 10% to 80% at 4 s for both controllers
Fig.14  Load variation form 10% to 80% at 4 s for both controllers
Fig.14  Load variation form 10% to 80% at 4 s for both controllers
Fig.15  Controller’s performance for speed variation at 4 s and load disturbance at 7 s
Fig.15  Controller’s performance for speed variation at 4 s and load disturbance at 7 s
Fig.15  Controller’s performance for speed variation at 4 s and load disturbance at 7 s
Fig.15  Controller’s performance for speed variation at 4 s and load disturbance at 7 s
Fig.15  Controller’s performance for speed variation at 4 s and load disturbance at 7 s
Fig.16  Deflecting torque with respect to motor current load disturbance
Fig.16  Deflecting torque with respect to motor current load disturbance
Fig.16  Deflecting torque with respect to motor current load disturbance
Fig.16  Deflecting torque with respect to motor current load disturbance
Fig.16  Deflecting torque with respect to motor current load disturbance
Fig.17  Experimental graph of speed variation for the step change in reference speed using the conventional PID controller
Fig.17  Experimental graph of speed variation for the step change in reference speed using the conventional PID controller
Fig.17  Experimental graph of speed variation for the step change in reference speed using the conventional PID controller
Fig.17  Experimental graph of speed variation for the step change in reference speed using the conventional PID controller
Fig.17  Experimental graph of speed variation for the step change in reference speed using the conventional PID controller
Fig.18  Experimental graph of speed variation for the step change in reference speed using PID-ANN controller
Fig.18  Experimental graph of speed variation for the step change in reference speed using PID-ANN controller
Fig.18  Experimental graph of speed variation for the step change in reference speed using PID-ANN controller
Fig.18  Experimental graph of speed variation for the step change in reference speed using PID-ANN controller
Fig.18  Experimental graph of speed variation for the step change in reference speed using PID-ANN controller
controllerPIDPID-ANN
simulationhardwaresimulationhardware
settling time/s0.7510.250.54
max. over shoot/%4.25no over shootno over shoot
steady-state error/rpm-15-30±0.5—
Tab.6  Hardware performance comparison of the proposed system with conventional PID controller for the speed = 1800 rpm and Δ = 10%
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