Fuzzy Logic Intelligent Control System of Magnetic Bearings

Shuliang Lei, Alan Palazzolo and Albert Kascak

Abstract-This paper presents a fuzzy logic based intelligentcontrol system applied to magnetic bearings. The core in theexpert system is fuzzy logic controllers with Mamdaniarchitecture. The fuzzy logic controllers for rub detection andautomatic gain scheduling were implemented. The expertsystem not only provides a means to capture the run time dataof the magnetic bearings, to process and monitor theparameters, and to diagnose malfunctions, but also protects themagnetic bearings from rub anomaly and implements thecontrol on a real time basis.

I. INTRODUCTION

R ENT development in the study of magnetic suspensioncontrol exhibits a variety of non-linear strategies in

theory and applications. Among them, fuzzy logic basedcontrol and expert system has drawn much attention toresearchers in this area. Due to the nonlinear property andknowledge-based rules, fuzzy control has its advantages inapplications to magnetic suspension system, which itself is ahighly nonlinear electromechanical plant. Fuzzy logic can beapplied to diagnosis of magnetic bearing systems withknowledge-based expert system. In motion control area suchas speed control of DC motor, induction motor control andwaveform estimation, fuzzy logic has found its applicationsas expert systems [1]. Fuzzy control is also employed toimprove the performance of magnetic bearings [2] or toovercome the position dependent non-linearity of magneticbearings [3]. Fault detection and supervision can apply fuzzylogic control concept to abnormal operating conditions [4],[5].Fuzzy controller applied to magnetic bearings (MB) on

real time basis has also gained acceptance in the field ofintelligent control. In [6][7], fuzzy controllers with hardwarein the loop implementations were described. In [8],implementation of fuzzy expert system without intelligentcontrol was briefly introduced. With an expert system, themagnetic bearings can operate more reliably and efficientlyto adapt to anomaly occurrences and operating conditions.

This fuzzy logic intelligent control system, also referred toas fuzzy logic expert system, is developed for the magneticbearing control and fault diagnosis. The expert system notonly provides a means to capture the run time data of themagnetic bearings, to process and monitor the parameters,

Manuscript received January 29, 2007.Shuliang Lei is with Texas A&M University, now at NASA Glenn

Research Center, Cleveland, OH 44135 USA (phone: 216-433-2696, email:sleiAneo.tamu.edu).

Alan Palazzolo is with Texas A&M University, Department ofMechanical Engineering, College Station, TX 77843, USA.

Albert Kascak is with NASA Glenn Research Center, Cleveland, OH44135, USA..

and to diagnose malfunctions, but also implements thecontrol on a real time basis to protect the magnetic bearingsfrom rub anomaly in terms of fuzzy logic control algorithm.

Therefore, the main function of this expert system is tomonitor, analyze, and control the machinery vibrations orfaults, and to apply corrective forces to the machinery systemusing magnetic bearing actuators.

The fuzzy logic intelligent control system consists of thefollowing software and hardware to implement the controlrules:

Software programs: (a) Executable digital signalprocessing (DSP) code downloadable to Texas Instrumentsmicroprocessor chip. This program was developed forimplementation of real time control which includes digitalproportional-derivative control stages, lead compensatorsand notch filters, etc. The source code was written in C++language and was compiled into executable code usingTexas Instruments Development Kit. (b) LabVIEWprograms developed to build a graphical user interface whichis used as the main entry to adjust the digital controllerparameters. The LabVIEW programs also work as auxiliaryDSP routines to calculate the filter parameters needed by thedigital controller. Fuzzy logic expert system for rubdetection-correction and gain scheduling were built up withthe LabVIEW fuzzy logic toolkit. The source code waswritten in LabVIEW G-language. Communications betweenthe LabVIEW program and the DSP digital controller wasimplemented via file read and write through the hostcomputer.

Hardware in the system consists of (a) DSP board with thea 32-bit processor core (Texas Instruments TMS320C67)and the on-site A/D-D/A converters for real time controlimplementations, (b) National Instruments (NI) PCI card fordata acquisition.

Fuzzy logic acts as an expert in the intelligent controlsystem. It accepts data from the LabVIEW data acquisitioncard, and maps from input space into output space.LabVIEW fuzzy logic toolkit is employed in the system. Theinput variables are given as exact numbers in the inputuniverse of discourse. A universe of discourse is made up offuzzy sets. For example, the universe of discourse ofmean values of the input current is made up of "positive","' negative" and 'zero' etc. Fuzzification takes the inputs anddetermines the degree to which they belong to each of theappropriate fuzzy sets in terms of membership functions.Then fuzzy operators are applied to the antecedent part ofthe fuzzy rule and implications/aggregations are conducted.Finally, defuzzification performs calculations withaggregated output fuzzy set to output the result. Mamdani

1-4244-1210-2/07/$25.00 C 2007 IEEE.

architecture is employed in the formulations of the fuzzysystem.

II. DATA ACQUISITION AND PROCESSING OF MAGNETICBEARINGS

A magnetic bearing is an electromechanical suspensionsystem employed to support the rotating shaft at very highspeed. It eliminates the needs of contact support to therolling components. An active magnetic bearing consists ofan electromagnet system with closed loop feed back tolevitate and stabilize the rotor during fast rotating operations.Because of frictionless operations, the rotating spewed canbe as high as 60,000 RPM. Magnetic levitatedelectromechanical system can be configured in vertical orhorizontal orientations. In vertical orientation, radialmagnetic bearings are installed on the upper and lower partsof the shaft, referred to as top bearing and bottom bearingrespectively. Both the top and bottom bearings control themotions of the rotor in two perpendicular directions: X andY directions, thus formulating four radial control channels.The thrust magnetic bearing is installed axially to support theshaft from falling down due to gravity. Fig. 1 is theschematic diagram of a magnetically supported flywheel testrig system. Closed loop is formulated for each controlledaxis of motions. The control loop starts from sensor input,via controller stages through to the power amplifier thatgenerates control current fed into the bearing coils to activatethe actuator. The electromechanical forces generated by theactuator levitate and stabilize the rotating shaft.

disturt

TopBearing

Flywheel

Botto rrBearing

sensors

imbalance

AxiaBearing

PoweiAmplfier

currents

Fig. 1 Diagram of magnetically supported flywheel system

detection of its components. Operating parameters of theelectromechanical system may be influenced by faults. Sincemagnetic bearing is a non-contact motion control device, it isa primary issue that the rub anomaly due to imbalance andvibrations be detected and corrected.To monitor, diagnose and maintain the operating status of

the system in order to generate corrective force, themeasurable parameters of the magnetic bearings are capturedby the LabVIEW data acquisition (DAQ) board. Beforefuzzy logic control processes the input signal to makedecisions, appropriate parameters must be acquired andprocessed. The measured parameters include displacement (5channels) and current signals (10 channels) from themagnetically supported rotor. These data are captured andprocessed by DAQ board and LabVIEW program. Anadditional channel in the DAQ is used for trigger signal.A vertical test rig was utilized to implement intelligent

control. Data acquisition from the test rig sensors includes:

(a) signals from displacement sensors (5 channels):* top bearing XA, YA axes* bottom bearing XB, YB axes* thrust Bearing Z axis

(b) current sensor signals (10 channels):* top bearing +I(XA) -I(XA) +I(YA) -I(YA)* bottom bearing +I(XB) -I(XB) +I(YB) -I(YB)* thrust bearing +I(Z) -I(Z)

(c) Rotor speed TTL signal from speed sensorThe above-acquired data are buffered in LabVIEW DAQprogram.

For each channel, the data are processed via intrinsicfunction routine, resulting in a 20 X 14 matrix that includes:(a) 14 channel captured data: 5-axis displacement data, 8 coilcurrent data of radial bearings, and a TTL speed signal;(b) 20 processed data type by LabVIEW program:* time domain: mean value, RMS (root mean square, or

quadratic mean) value, max. value, peak-to-peak value;* frequency domain: lx FFT amplitude, 2x FFT amplitude,

x/2 FFT amplitude;* amplitudes at interested frequencies, phase angles ofFFT

etcThe processed data are tabulated in the form of intrinsic

functions. LabVIEW front panel displays the onlineoperating parameters as shown in Fig. 2. Intrinsic functionsare updated during the real time data acquisition process.The expert system can display the processed data in the

front panel with graphs like an virtual instrument. Fig. 3 ispart of the front panel graphic display. The front panelvirtual instrument displays (a) time responses of rotordisplacements, (b) orbits of top and bottom radial bearings,(c) fast Fourier transform (FFT) plots of the current anddisplacement signals, etc.

To maintain stable operation of the magnetic bearingsystem, an important feature is the safe suspension and fault

Fig. 2 Intrinsic function display on front panel

Fig. 3 Front panel display of LabVIEW expert system

III FUZZY LOGIC BASED INTELLIGENT CONTROL

Intelligent control is implemented by fuzzy logic (FL)with the LabVIEW fuzzy logic toolkit. Rub detect controland automatic gain scheduling are programmed withLabVIEW G-language. The fuzzy logic toolkit can befurther utilized by the user to design customized controllerbased on his experience and specific properties of themagnetic bearings. The resulting fuzzy logic control (FLC)

program can be stored in a database for use in differentcases.

The expert system is implemented with LabVIEWbuilt-in fuzzy logic toolkit and SBC67 board (TexasInstruments TMS320C7601) for digital signal processing.It can perform real time control via a user-friendlyinterface. Its unique feature lies in that the systemsuccessfully build the bridge between the LabVIEWDAQ, data processing and the Texas Instrumentsmicroprocessor to formulate a through way from thesensor signal to the control voltage output. The desiredcontrol output signals are generated by the DSPmicrocontroller board and are routed to the poweramplifier to control the magnetic bearing.

Fuzzy logic control is a nonlinear control strategy basedon input-output membership functions and rule base. Itdescribes a control in a qualitative and intuitive way thatemulates the heuristic rule-of-thumb strategies of experts.

The main program of the LabVIEW expert systemincludes a user interface for intelligent control: It allows theuser to load the appropriate fuzzy logic control stored inthe database for automatic gain control and rub detectcontrol. The user can also manually input the controlparameters to bypass the automated FLC. The systemimplements real time control in terms of DSP executablecode and LabVIEW user interface handshaking program.The LabVIEW interface and handshaking program buildslinks between the LabVIEW expert system and the DSPreal time controller. It accepts data form FLC output resultsand output the parameters to DSP.

The task for the intelligent control is assigned by theuse of LabVIEW and DSP software packages. The

LabVIEW fuzzy logic expert systems is applied to theflywheel for rub detection and generate control signals fromFLC output space; the DSP program generates the executablecode and loads the code to microprocessor for real timecontrol. A user interface is built on LabVIEW panel to allowuser to adjust the parameters online. The real time parametersare transferred from LabVIEW to DSP program through handshaking.The core in the expert system is fuzzy logic control. The

following is the algorithm of fuzzy logic systems for rubanomaly detectMagnetic Bearing A (top bearing) in X axis:

Input IX mean value of current in positive X coilInput IY mean value of current in positive Y coil

Fuzzy logic generates target voltage signal in XA axis.The file is stored in expert database as rubXA.fcMagnetic Bearing A (top bearing) in Y axis:

Input IX mean value of current in positive X coilInput IY mean value of current in positive Y coil

Fuzzy logic generates target voltage signal in YA axis.The file is stored in expert database as rubYA.fcMagnetic Bearing B (bottom bearing) in X axis:

Input IX mean value of current in positive X coilInput IY mean value of current in positive Y coil

Fuzzy logic generates target voltage signal in XB axis.The file is stored in expert database as rubXB.fcMagnetic Bearing B (top bearing) in Y axis:

Input IX mean value of current in positive X coilInput IY mean value of current in positive Y coil

Fuzzy logic generates target voltage signal in YB axis.The file is stored in expert database as rubYB.fcMembership functions are defined and displayed via

graphical user interface on LabVIEW fuzzy logic controltoolkit as shown Fig. 4. The rule base of the FLC is shown inFig. 5.

(c)Fig. 4 Membership functions of fuzzy logic control system (a) Inputmembership functions: mean value of current at Channel 6. (b) Inputmembership functions: mean value of current at Channel 8 (c) Outputmembership functions: target voltage of magnetic bearing A in: Xdirection

LcFig. 5 Rule base of fuzzy logic controller for Bearing A: X direction(a)

Fig. 6 expert system panel for rub detect control

Fig. 7 Main entry of control parameters: proportional, derivative and integral gains of the five control loops

The fuzzy logic controller files with extensionfc are storedin appropriate directory of the computer and are available tousers. Furthermore, the user can define his ownfc-files basedon specific flywheel system parameters. Once thefc-file builtis completed, the user can access the intelligent controlLabVIEW panel, which is shown in Fig. 6. The main entry ofthe control parameters is shown in Fig. 7.The DSP part in the expert system performs the following

work: (a) implements digital filters, (b) generates the

executable code (c) downloads the code to microprocessorfor real time control.

IV EXPERIMENTS

The fuzzy logic expert system was tested with a verticaltest rig in Vibration Control and Electromechanics Lab atTexas A&M University as shown in Fig. 8. In thebeginning of the experiment, data acquisition was first

conducted. The processed data are displayed at the frontpanel shown in Figs. 2 and 3.To test the rub detection and intelligent control, the fuzzy

logic codes were first loaded into the systems. They are thefour the fuzzy logic controllers:

(a) top bearing controller in X direction (rubXA.fc): inputcurrent signal Ix and ly (DC values) of bearing and outputcontrol voltage at target XA

(b) top bearing controller in Y direction (rubYA.fc): inputcurrent signal Ix and ly (DC values) of bearing A and outputcontrol voltage at target YA

(c) bottom bearing controller in X direction (rubXB.fc):input current Ix and ly (DC values) of bearing B andoutput control voltage at target XB

(d) bottom bearing controller in Y direction (rubYB.fc):input current Ix and ly (DC values) of bearing B andoutput control voltage at target YB

Before activate the rub detection test, function generatorswere used as input sources for X and Y signals to emulate therub anomaly. Change the DC offset values from the functiongenerator to see the output target voltage. The resultsexhibited that the output voltage values are in consistent withthe fuzzy logic controller as expected.

Solenoids were employed to generate disturbance forces topush the cylinder bar stretched out to deliberately rub theflywheel (Fig. 8). The solenoids are placed at X and Ydirections respectively. When one of the solenoid wasactivated, rub occurred in the system and currents in themagnetic bearing coils changed. The intrinsic functiongenerator of the expert system fed the current signals to thefuzzy logic controller as inputs. During the process, theflywheel was seen moving away from the stretched cylinderbar in the same direction, thus avoiding the rub contact.Fuzzy logic controller worked behind the front the panel tomake the magnetically levitated system operating normally inthe condition of the occurrence of rubbing

Automatic gain control is also implemented and tested withfuzzy logic controller. PD controller is utilized to stabilize thesystem. The user can use the fuzzy logic controller to changethe proportional gains or derivative gains with predefinedfuzzy logic controller programs loaded in the system. The FLcontroller membership functions and the rule bases aresimilar to those of the rub protection controller.

The test with the complete system of intelligent controlemployed LabVIEW and DSP Innovative Integration boardto generate real time control outputs. The system test wassuccessfully completed.

V CONCLUSION

The fuzzy logic expert system with real time intelligentcontrol, implemented with LabVIEW fuzzy logic toolkit andDSP SBC67 board (Texas Instruments TMS320C7601), canperform real time control via a user-friendly interface. Thesystem built a bridge between the LabVIEW DAQ, data

formulate a through way from the sensor signal to thecontroller voltage output. The core in the expert system isfuzzy logic controllers with Mamdani architecture. Thefuzzy logic controllers for rub protection and automaticgain scheduling were archived in the form offc-files storedin the expert system database. More of the FLC files can bebuilt with respect to specific magnetic suspension systemand different operating conditions. The desired controloutput voltages are generated by the DSP microcontrollerboard and are sent to the to the power amplifier of themagnetic bearing. As different from some expert systemswhich are merely for fault detecting and monitoring, thissystem adds functionality to generate corrective force toresolve the anomaly on real time basis.

LU|g~~~~~~~~~~~~~~~~~~..'.........

bearing

x

Fig. 8 Vertical test rig of magnetic suspension system

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