Experimental Implementation of Fuzzy Controller of Switched Reluctance Motor on FPGA

Experimental Implementation of Fuzzy Controller of Switched Reluctance Motor on FPGA

Hari Madhava Reddy.Y1, Dr. A. K. Parvathy2, V. Narsi Reddy3

1M.Tech Student, Power Electronics and Drives, Department of Electrical and Electronics Engineering, Hindustan University, Chennai, TamilNadu, India.

2Professor, Head of the Department, Department of Electrical and Electronics Engineering, Hindustan University, Chennai,TamilNadu,India

3Assistant Professor, Department of Electrical & Electronics Engineering, Malineni Perumallu Educational Societys Group of Institutions, Guntur, A.P, India

Abstract This paper presents modeling, simulation and analysis of switched reluctance motor (SRM). Switched reluctance motors are used in applications such as electric vehicles, washers, dryers and aerospace applications as the machine is brushless, maintenance free and has rugged and simple construction. MATLAB/SIMULINK is used to simulate linear model of SRM. A 1HP machine is designed for possible applications such as direct drive in washing machines and electric vehicles. Conventional PI and fuzzy logic controllers are used to control the speed of SRM. Simulation results verify considerable reduction in torque ripple and speed settling time when fuzzy controller is employed compared to PI controller. Field programmed gate array (FPGA) control fabricated with Xilinx Spartan 3E developed board for SRM is presented. The experimental results of SRM with PI and fuzzy control verify the simulation results.

Keywords: Field programmed gate array, Fuzzy controller, PI control, Switched Reluctance motor.

1. INTRODUCTION

   The electrical drive plays an important role on productivity to any industry. The drive requirement is based on available mains and load characteristics. Switched reluctance motor (SRM) is a singly excited, doubly salient machine in which electromagnetic torque is developed due to variable reluctance principle. SRM have advantages of low manufacturing cost compare to other motors, rugged & simple construction, lesser switches in drive circuit, no windings or permanent magnets on rotor side and high efficiency [1] – [2]. The advent of modern control technology and power electronics has enabled SRM drive to becoming increasingly popular. Switched reluctance machines are used in electric vehicles, washers, dryers and aerospace applications. However, some of the limitations are noise, torque ripple and low torque to volume ratio [1]

   [2]. Noise and low torque to volume ratio can be rectified by changing the geometry of SRM [3] [4].Segmented switched reluctance motor proposed in [4] shows mitigation of noise and low torque to volume ratio.

   Research is being done in various subjects such as different motor shapes [3]-[4], control strategies and

   converter types [5] – [6], but the studies are not completed. On the other hand, considering simulation of SRM, the electrical and mechanical circuit equations are realized by using the MATLAB/Simulink program [7],[8]. The hardware implementation can be done after analyzing the dynamic behaviour of SRM using Simulink model [7] – [9].

   This paper presents a simplified linear model for closed loop control of SRM for simulation studies, which uses the fundamental mathematical functions to describe the saturation of the flux linkage depending on the winding current and rotor position. The theme of the paper is to reduce torque ripples using fuzzy controller. SRM is modeled using PI and fuzzy controllers and simulated using MATLAB/SIMULINK. Hardware implementation of SRM is done using FPGA. The simulation results verify that there is a considerable reduction in torque ripple and speed settling time when fuzzy controller is employed when compared to PI controller. The experimental results verify that speed response is very smooth and ripple is reduced to small amount by using fuzzy logic controller in comparison with conventional PI controller.

   In section 2, modeling of SRM with PI and fuzzy controllers is given using MATLAB/Simulink. Simulation results are presented in section 3. Hardware implementation is explained in section 4; experimental results are presented and analyzed in section 5. In section 6, the paper is concluded.

2. MODELING OF SRM WITH PI AND FUZZY

   CONTROLLERS

   The model for simulation is developed by assuming a linear model for SRM [7]. Fig.1 shows the MATLAB/Simulink model of SRM with PI controller. It consists of four phase blocks. Fig.2 shows the construction of one phase block. To be more complete, the block named phase1 is described with details that follow. It contains four other blocks, each one associated with a specific MATLAB function. They are the following.

   1. Switch

      Switch block permits to assure the power converter commutations at angles theta on, theta off.

      Fig. 1 Simulink model for 8/6 SRM Motor with PI controller

      Fig. 2 Expansion of one of the phase

      1. Inductance

         Inductance block computes the current on the respective phase inductance according to rotor Position theta and phase flux. Therefore, one gets phase current I as its output signal, by output block 3 named current1.

      2. Torque

         Torque block computes the torque produced in this phase according to the rotor position theta and the current value I.

      3. Modulo pi/2

      Fig. 3 Simulink model for 8/6 SRM Motor with fuzzy controller.

      Fig. 4 Fuzzy controller sub block

      Fig.3 shows the MATLAB/Simulink model of SRM with Fuzzy controller. The fuzzy logic controller block has error and change in error (del) as inputs and one output. Fuzzy controller sub block is shown in Fig. 4. The rules developed are based on IF-THEN methodology. The membership functions of fuzzy logic controller for error, change in error and output are shown in Fig.5

      Each phase inductance has a periodicity of

      2Nr

      degrees. Therefore, it is appropriate to transform the rotor position angle coming from the mechanical equation to

      Fig.5 Membership functions of Fuzzy controller.

      modulo 2

      Nr .

      The membership functions are triangle ones having labels of NB(Negative Big), NM(Negative Medium), Z(Zero), PM(Positive Medium), PB(Positive Big). Table 1 shows the rule base of fuzzy controller. The rule base consists of

      25 IF-THEN rules. The ANN used for self-tuning is feed forward with four layers.

      TABLE 1: RULE BASE OF FUZZY LOGIC CONTROLLER

      	Change in error(del)
      Error(e)		NB	NM	Z	PM	PB
      	NB	NB	NB	NB	NM	Z
      	NM	NB	NB	NM	Z	PM
      	Z	NB	NM	Z	PM	PB
      	PM	NM	Z	PM	PB	PB
      	PB	Z	PM	PB	PB	PB

3. SIMULATION RESULTS

   The performance of switched reluctance motor and SSRM was analyzed by using PI and Fuzzy controllers with the help of simulation.

   The system was operated at a reference speed of 1000 rpm when motor shaft is under no load are observed from the wave forms.

   Fig. 6 Phase A inductace, current and torque in SRM.

   Fig. 6 shows the one phase inductance, current and torque of SRM.

   Fig.7 shows the speed responses of SRM with PI and Fuzzy controllers respectively. Comparison of proposed controllers with PI and Fuzzy for SRM shows great reduction in torque ripples and settling speed. To reach the desired speed (1000 rpm) it takes 0.15sec and the ripple contact in speed was compared to PI controller shown reduced with this method.

   The detailed comparison for torque ripple and settling time at steady state are presented in Table 2.

   Fig. 7 Speed response of SRM with PI & fuzzy controllers with 1000 rpm as reference speed

   TABLE 2 COMPARISON RESULTS BETWEEN PI AND FUZZY CONTROLLERS

   Controller	Torque Ripple	Speed Settling time (sec)
   Classical PI controller	1.06	0.23
   Fuzzy controller	0.36	0.15

   It is obvious that fuzzy controller has better results than PI controller.

4. HARDWARE IMPLEMENTATION

   The controllers for SRM can be implemented using Field Programmed Gate Array (FPGA) as it has key components in implementing high performance processors [10], [11]. The speed, size and the number of inputs and outputs of a modern FPGA far exceeds that of a microprocessor or DSP processor. The drive system of SRM consists of SRM motor, FPGA controller, power circuit, driver circuit, converter circuit and starter kit. Fig.

   8 presents the structure of SRM drive system. The modules of drive system are: a 1 hp , 8/6 SRM, FPGA Spartan-3E Starter kit, a driver circuit, power circuit and multi output transformer. The rated current is 2.2 A per phase, rated speed is 4000 rpm, Supply voltage 110 to 350 volts DC, Air Gap is 0.3 mm, No. of turns per phase is

   100, Cross sectional area of conductor 0.4 mm2 and Stack length is 130 mm. Here power supply is single phase ac 230V, power circuit with four IGBTs is used to convert ac to dc at ±100-300V.Power circuit is used to energize the each phase of motor by receiving the switching pulses from Field Programmable Gate Array(FPGA)Controller. The FPGA Controller generates PWM pluses based on motor rotor position angle and speed signals. For these signals position sensor is used.

   .

   Fig. 8 Snapshot of SRM drive system

5. EXPERIMENTAL RESULTS

   The proposed control techniques were performed in laboratory when the motor was operated under different load conditions.

   Figures 9 and 10 shows the speed response of the motor with PI controller and fuzzy logic controller respectively. It is observed that speed response is very smooth and ripple is reduced to a small value with fuzzy logic controller in comparison with conventional PI controller.

   The practical results agree with the simulation results.

   Fig. 9 Speed response with PI controller

   Fig. 10 Speed response with fuzzy controller

6. CONCLUSION

Modeling, simulation and analysis of switched reluctance machine (SRM) is done. Simulation results of SRM with PI and fuzzy control shows considerable reduction in torque ripple and speed settling time when fuzzy controller is employed in comparison with PI controller.

Simulation helps to get exact switching angles. The speed of the motor increases with decrease in switching ON time i.e. as switching frequency increases, the speed of the motor increases.SRM control was fabricated by using FPGA (field-programmable gate array). By comparison of simulation results and hardware results, the time response for fuzzy controller to reach desired speed is improved and ripple contents are lesser than the PI controller.

More accurate results can be obtained by linearization of SRM machine model. Research work is already done in this field by the authors. The linearization method proposed in [12], [13] can be extended to SRM model as well.

REFERENCES

1. T.J.E Miller, Switched reluctance motors and their control

   (Magna Physics Publications/Oxford university press, 1993).

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3. Naresh Vattikuti, Vandana Rallabandi and B. G. Fernandes,A Novel High Torque, Low Weight Segmented Switched Reluctance Motor", Issue 15-19, Page 1223-1228, Jun.8 2008, Rhodes, Greece.

4. Susmitha Javvadi and D.Srinivas Rao, Design, modeling and simulation of full pitched winding segmented switched reluctance motor, International Journal Of Engineering Science and Technology (IJEST), ISSN:0975-5462,Vol.3 No.6 June 2011.

5. V.F.Ray, P.J.Lawrenson, R.M.Davis, J.M. Stephenson, N.N.Fulton, R.J.Blake, High Performance Switched Reluctance Brushless Drivers, IEEE Trans.Ind. Appl.,Vol.1A- 22, No.4,1986, pp.722-729.

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    Authors Information

    Hari Madhava Reddy.Y was born in june 1987, in Guntur, India. He received the B.Tech degree in Electrical and Electronics Engineering from JNTU, Hyderabad in 2011 and completed M.Tech in Power Electronics and Drives from Hindustan University, Chennai.

    His areas of interest are power systems and control, electrical drives and renewable energy sources.

    Mr. Reddy is a member of IEEE Industrial Applications Society.

    1. K. Parvathy received her B Tech in Electrical and Electronics Engineering and M Tech in Power Systems from Calicut University, India in 1993 and 1998 respectively. She received PhD in Electrical faculty from Anna University, Chennai, India in 2013.

She is serving as Professor & Head of the Department, Department of EEE, HITS, and Chennai. She has published around 16 papers in international and national conference proceedings and journals. Her research areas

of interest include linearization and advanced control of machines. Dr. Parvathy is member of IEEE Power Electronics society.

VAJRALA.NARSI REDDY received his Bachelor of Technology degree in Electrical & Electronics Engineering and Master of Technology in Power Electronics from JNTU Kakinada, A.P. in 2010 and 2012 respectivey.

Currently, working as an Assistant Professor in Malineni Perumallu Educational Societys Group of Institutions, Guntur, A.P.

His areas of interests are in Power Systems, Power Electronics and FACTS.