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Sensors & TransducersVolume 109, Issue 10October 2009www.sensorsportal.com ISSN 1726-5479Editors-in-Chief: professor Sergey Y. 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Sensors & Transducers JournalContentsVolume 109Issue 10October 2009www.sensorsportal.com ISSN 1726-5479Research ArticlesAspects of Gas Sensor´s Modeling and Implementation in a Dynamic EnvironmentHakim Baha, Zohir Dibi....................................................................................................................... 1A Fuzzy-Based Tactile Sensor for Implementation in Specialized Motion of Land RoverBadal Chakraborty, K. Rajanna.......................................................................................................... 13Neuro and Fuzzy Computing Approach for the Flow Sensorless MeasurementR. Kumar, and P. Sivashanmugam..................................................................................................... 21Fuzzy Logic Based Set-Point Weighting Controller Tuning for an Internal Model ControlBased Pid ControllerMaruthai Suresh, Ranganathan Rani Hemamalini, Gunna Jeersamy Srinivasan ............................. 29Adaptive PI Controller for a Nonlinear SystemD. Rathikarani, D. Sivakumar. ............................................................................................................ 43Integration of Fault Detection and Isolation with Control Using Neuro-fuzzy SchemeA. Asokan, D. Sivakumar .................................................................................................................... 59Control Scheme of a Designed Step Climbing Wheeled RobotSrijan Bhattacharya, Sagarika Pal, Subrata Chattopadhyay.............................................................. 70Accurate Fluid Level Measurement in Dynamic Environment Using Ultrasonic Sensorand ν-SVMJenny Terzic, Romesh Nagarajah, Muhammad Alamgir.................................................................... 76Design of a MEMS Capacitive Comb-drive Micro-accelerometer with Sag OptimizationB. D. Pant, Lokesh Dhakar, P. J. George and S. Ahmad................................................................... 92Remarkable Electromechanical Coupling in the 2–2 Composite Based on Single-domainPMN–0.33PT CrystalVitaly Yu. Topolov, Sergei V. Glushanin, Christopher R. Bowen and Anatoly E. Panich .................. 108Effect on H 2 S Gas Sensing Performance of Nb 2 O 5 Addition to TiO 2 Thick FilmsC. G. Dighavkar, A. V. Patil, S. J. Patil and R. Y. Borse. ................................................................... 117Al 2 O 3 - BSST Based Chemical Sensors for Ammonia Gas SensingL. A. Patil, H. M. Baviskar................................................................................................................... 126Wireless Telemetry 2.4 GHz for Measurement Occupational VibrationDiogo Koenig and Alexandre Balbinot................................................................................................ 137pH Homeostasis Linked with Capacitance Relaxation Phenomena and ElectrostrictiveEnergy in Cancer CellsT. K. Basak, T. Ramanujam, J. C. Kavitha, Poonam Goyal, Deepali Garg, Arpita Gupta,Suman Halder..................................................................................................................................... 147Authors are encouraged to submit article in MS Word (doc) and Acrobat (pdf) formats by e-mail: editor@sensorsportal.comPlease visit journal’s webpage with preparation instructions: http://www.sensorsportal.com/HTML/DIGEST/Submition.htmInternational Frequency Sensor Association (IFSA).

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58Sensors & TransducersISSN 1726-5479© 2009 by IFSAhttp://www.sensorsportal.comAdaptive PI Controller for a Nonlinear SystemD. Rathikarani, * D. SivakumarDept. of Instrumentation Engineering, Annamalai University,Annamalainagar – 608002, IndiaTel.: 914144223640, * 9443238642E mail: dradhikarani_2k6@yahoo.co.in, dsk2k5@gmail.comReceived: 28 August 2009 /Accepted: 23 October 2009 /Published: 30 October 2009Abstract: Most of the industrial processes are inherently nonlinear in their behaviour. Designs ofcontrollers for these nonlinear processes are difficult, as they do not follow superposition theorem.Adaptive controller can change its behaviour in response to changes in the dynamics of the process anddisturbances. Hence adaptive controller can be used to control nonlinear processes. Direct ModelReference Adaptive Control is a technique, in which a reference model involving the desiredperformances is specified. In the present work, a DMRAC is designed and implemented to achievesatisfactory control of a nonlinear system in all its local linear operating regions. The closed loopsystem is made BIBO stable by proper control techniques. The controller is designed throughsimulation in Matlab platform and is validated in real time by conducting experiments on thelaboratory Air Flow Control System using the dSPACE interface. Copyright © 2009 IFSA.Keywords: Nonlinear system, Direct model reference adaptive control (DMRAC), Adaptive MITcontrol, Adaptive PI control, Fixed gain controller1. IntroductionControl of nonlinear dynamic system is a challenging task because nonlinear processes do not sharemany properties with linear system [1]. The knowledge about a system and its environment is requiredapriori to design a control strategy [2]. The most commonly used control techniques for nonlinearsystems are the PID control [3], optimal control, adaptive control [4] and robust control. Adaptivecontrol schemes [4, 5] are useful for systems with unknown or slowly time-varying or nonlinearprocesses and represent a class of advanced control algorithms. On the other hand, PID [6–8] controlalgorithms still continue to be widely used for solving industrial control systems, particularly in the43

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58chemical process industries. This is mainly because PID controllers have simple control structures andare easy to maintain and tune. Hence it is still attractive to design an advanced control systems withPID control structures. One may not be able to get good control performance in the case of nonlinearprocesses. In order to improve the closed loop response of the nonlinear process, Model ReferenceAdaptive Control [9-10], auto tuning [11–13] and self-tuning PID control [14–21] can be designed.In this paper a procedure to design and implement DMRACs after modelling a nonlinear system isproposed. The basic idea of modelling is to represent the nonlinear system into a family of local linearmodels. Each linear model represents the dynamics of the complex system in that particular localregion. These models are used to design local controllers specific to that region.Adaptive MIT and Adaptive PI controllers are designed and implemented for a nonlinear air flowcontrol system. The designed controller has a conventional inner loop followed by a separate adaptiveouter loop to adjust the controller’s gains (theta1, θ 1 , theta2, θ 2 , Proportional gain, K P , Integral gain,K I ) based on the modelling error to equalize the coefficients of closed loop plant to the coefficients ofthe plant model [2, 18, 22]. The advantage is that the proposed technique can deal with the nonlinearnature of the process and also retain the designer’s intuition and insight through the relatively simpledesign scheme that is proposed. Hence, the plant output converges to model’s output. The control lawis implemented using DS1104 board of dSPACE interface with the process to perform experimentationand the results are analyzed.There is no guarantee that the Adaptive MIT and Adaptive PI controllers can provide a stable closedloop performance. Hence the closed loop system is made bounded input, bounded output stable(BIBO) by maintaining the gain of the closed loop system within limits always.The rest of the paper is organized as follows. Section 2 describes the nonlinear air flow control systemavailable in our lab, section 3 deals with the system modelling, section 4 and 5 discusses adaptivecontroller design and analysis with the stabilizing set of controller. A sample case from simulation andalso from real time implementation along with the results are presented in section 6 which is followedby concluding remarks in section 7.2. Laboratory Air Flow Control SystemThe process considered in this paper is a simple model of air flow process. The important aspect of theprocess is its simplicity. This is mildly nonlinear and can show acceptable range of linearization. Thisprocess can be modeled as First Order Plus Time Delay Process and the model dynamics depend onoperating conditions. The piping and instrumentation diagram (Fig. 1) depicts the entire process and itsassociated control system. The controlled variable (air flow rate) is sensed by an appropriate sensor(differential pressure flow transmitter). This flow transmitter produces current output in the range of4 to 20 mA. A current to voltage (I/V) converter is used to convert 4 to 20 mA into 1 to 5 Volts. Thismeasured voltage (controlled variable) is compared with the reference signal. The difference betweenthe two is given as input to the DMRAC. The controller produces manipulating variable based on theidentified Process Parameters and the difference between the set point and controlled variable. Themanipulating variable in voltage form is converted into current by a Voltage/Current (V/I) converter.A Current/Pneumatic (I/P) converter is used to convert this current to pressure (3 to 15 psi) acceptedby the control valve. The air failure to open (AFO) pneumatic control valve restricts the path of the airflow in the process pipe line, thus controlling the air flow rate. The process gain for various flow ratesin the process pipe line are shown in Fig. 2. The process shows the hysteresis nonlinearity in itscharacter. The dynamic characteristic of the Final Control Element with Differential Pressure FlowTransmitter is shown in Fig. 3.The modeling and identification of the process is continued in the nextsection.44

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58V1 to V4 - Manifold valves; FT - Flow Transmitter; MV-1 to MV-3 - Manual control valves; PS - PowerSupply; M1 and M2 - Manometer connections; mA - Milliammeter; DH - De humidifier; I/P - Current toPressure converter; AFR - Air Filter Regulator; PCV - Pneumatic Control Valve; PRG - Pressure Gauge ;G-2 - Galvanized pipe for cold air flow.Fig. 1. Piping and Instrumentation Diagram of Air Flow Control System4Process gain32100 500 1000Flow rate(lpm)--------- Increasing Flow Rate- - - - - - Decreasing Flow RateFig. 2. Characteristics of Air Flow Process.Fig. 3.Characteristic of Air Flow Control System.45

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-583. Identification of ProcessThe model of the process is not needed for the design of DMRACs. Before implementing the controlalgorithm in real time, the designer has to observe the performance by various simulation studies. Inorder to design controllers through simulation, the actual process need to be modeled. Hence theprocedure for identification of the nonlinear air flow process is outlined in this section. Identificationrefers to the process of selecting a model structure first, then estimating its parameters [23]. Thegeneral First Order plus Time Delay Process (FOPTD) is given byG(s)=−LsKe, (1)Ts + 1where K represents the steady-state gain of the process, L represents the time delay and T representsthe time constant of the process. The process parameters for different operating condition are estimatedby Two point Method [1]. Neglecting the time delay, the process model is approximated as First OrderProcess (FOP)The process parameters are determined as follows:whereK( s)= Ts + 1∆ Y is the change in output and ∆ U is the change in input.G (2)∆YK = , (3)∆ UT = .5 * ( T 2− T ) , (4)11where T 1 is the time taken by the process to reach 28.3 % of the final steady state value.T 2 is the timetaken by the process to reach 63.2 % of the final steady state value. 50% of the collected real time dataare used for modeling and the remaining 50 % are used for model validation [23].The identified FOP models are given in Table I. The time domain validation of the model with theactual process is presented in Fig. 4. The obtained responses reveal the degree of closeness of theestimated FOP model with that of the actual process.Flow rate(Lpm)350300250200150100 110 120 130 140Time (sec)-------Actual Process response------- Model responseFig. 4. Response of the identified Model and actual process.46

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58Table I. Identified Models.Flow rate(lpm)Identified ModelFlow rate(lpm)Identified ModelFlow rate(lpm)Identified Model5 – 1340.01592M 1( s)=872 – 10250.475s+ 11.3548M 7 ( s)=872 – 7250.475s+ 1M13 (0.9168s)=0.475s+ 1134 – 2800.0252M 2( s)=1025 – 11700.475s+ 12.2488M 8 ( s)=725 – 5750.475s+ 1M14 (0.5281s)=0.475s+ 1280 – 4280.0683M 3 ( s)=1170 – 13190.475s+ 14.0796M 9 ( s)=575 – 4280.475s+ 1M15 (0.2654s)=0.475s+ 1428 – 5750.2194M 4 ( s)=1319 – 11700.475s+ 14.0768M 10 ( s)=428 – 2800.475s+ 1M16 (0.0692s)=0.475s+ 1575 – 7250.4188M 5 ( s)=1170 – 10250.475s+ 12.5045M 11( s)=280 – 1340.475s+ 1M170.0505( s)=0.475s+ 1725 – 8720.8195M 6( s)=1025 – 8720.475s+ 11.4522M 12 ( s)=134 – 150.475s+ 1M18 (0.01536s)=0.475s+ 14. Adaptive Control AlgorithmThe Block Diagram Representation of DMRACs implemented in this work is presented in Fig. 5. Thecontrol system consists of two loops. The inner conventional feedback loop is composed of the processand the controller. The outer loop is for Parameter Adjustment Algorithm (PAA) with decision block.The decision, comparator in the figure decides input to the controller based on the input range (U C ) andstability condition (equation (31) and (32)). The PAA updates the controller parameters after satisfyingthe stability criterion for the specific range. The fixed gain controller block is placed in theconventional loop if PAA does not satisfy the stability criterion.ModelFixed Gain controllerY MDecision andComparisonAdaptive Mechanism (PAA) eeYU CControllerProcessFig. 5. Block Diagram Representation of Direct Model Reference Adaptive Control.47

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58The outer loop adjusts the controller parameter in such a way that the model error (e), the differencebetween process output Y and model output Y M is smalle = Y −(5)Y MThe controller parameters may be adjusted with the following loss function1J ( ) = e22θ (6)To minimize J, the parameters can be changed in the direction of negative gradient of JdθdJ ∂e= −γ= −γedt dθ∂θ(7)The above parameter adjustment mechanism, called MIT algorithm is used to control the laboratory air∂eflow control system. The quantity is the sensitivity derivative of the error with respect to∂θcontroller parameterθ . The parameter γ determines the adaptation rate.4.1. Adaptive MIT Controller [4]Consider a system described by a model (equation 8)YU=bp + a,(8)where p=d/dt is the differential operator, U is the control variable and Y is the measured output. Thecontrol law is given byU t)= θ UC( t)−θY()(9)(1 2tThe closed loop transfer function is given by equation (10)YU=bθbθ1CP + a +2,(10)where U C is the command signal (input). The controller parameters are to be adapted such that processoutput follows the model output (equation 11)YUMCαp+ β=2(11)p +22δωnp + ωnEquation (10) and (11) are substituted in equation (5) to obtain modeling error.⎡ bθ1αp+ β ⎤e = ⎢ −* U22 ⎥⎣ P + a + bθ2 p + 2δωnp + ωn⎦C(12)48

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58For perfect model following the controller parameters are to be chosen (when e=0) as in Equation (13)and (14).pα+ βθ1 =(13)bθ =2p22+ 2δωnp + ωn−bp − a(14)As the process parameter a and b are not known, the following approximation based on the observationis applied for perfect model following. The sensitivity derivatives are obtained by taking partialderivatives of modelling error with respect to the controller parameters θ 1 and θ 2 .p + a + bθ+(15)= p 2+222 δω p ω n n∂eb=U2∂θ 1 p + 2δωp + ωn2n∂eb= −Y×∂θ +222 p + 2δωn p ωnC(16)(17)The controller parameters θ 1 and θ 2 are obtained by substituting equations (16) and (17) in MITalgorithmθγ ' ⎛− ⎜p⎝ p1=22+ 2δωnp + ωn1× UC⎞⎟e⎠(18)= γ ' ⎛ 1 ⎞θ Y ep⎜×pnp⎟22(19)⎝ + 2δω+ ω2n ⎠γ ' = γb(20)4.2. Adaptive PI ControlAn auto tuning method using model reference adaptive control (MRAC) based on MIT algorithm isproposed. The adaptation loop is simply switched for a period of time when tuning is required. Thenthe adaptation loop is disconnected and the system is left running with fixed controller parameters. ThePI control law is given by equation (21)KIU ( t)= KP( UC( t)− Y ( t))+ ( UC( t)− Y ( t))(21)pwhere K P and K I are the proportional and integral gains. The process transfer function considered forcontrol is represented by equation (8). The closed loop transfer function is given by equation49

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58YUC=bpKP+ KIp2 + p(a + bK ) + bK(22)PIConsider the second order reference model given by equation (11). There is a real zero at − ωn2 α2[ Q β = ω n ] in the reference model. This zero is introduced to match the structure of the equation (22).For perfect model matchingp2+ p(23)22( a + bKP) + bKI= p + 2δω np + ωnThe adapted PI Controller parameters based on MIT algorithm are as followsKP'⎡− γ ⎤= ⎢ ⎥e⎣ p ⎦p2p+ 2δwnp + w2n( UC− Y )(24)KI'⎡− γ ⎤= ⎢ ⎥e⎣ p ⎦p21+ 2δwnp + w2n( UC− Y )(25)5. Stabilizing Range for ControllersThe control loop of the laboratory air flow control system is closed by adaptive MIT and adaptive PIcontroller. The conventional PI controller eliminates offsets and retains acceptable speed of response[16]. The complete range of stabilizing values for controllers for specified flow ranges are calculatedand are given in Table II. The closed-loop characteristic equation of the system with adaptive MIT andAdaptive PI controller are given by equation (26) and (27).δ ( s ) = s + a + bθ(26)22δ ( s ) = s + ( bK + a)s +(27)PbK IThe condition for closed loop stability of the process with Adaptive MIT control is given byaθ2> −(28)bThe following equations present the stability condition of the process with Adaptive PI controllerK pa> − and KI> 0(29)bWhen the values of the controller parameters does not obey equations (30), Fixed Gain controller(FGC) is placed in the loop with gains K FGC1 from Table II and K FGC2 (0.475) by disconnecting thePAA loop.50

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58Table II. Stability condition for various operating regions with two different controllers.ModelKpa> −bθ2Fixed gain controllerKp(K FGC1 )Modela> −bθ2Fixed gaincontroller(K FGC1 )M 1 (s) > - 62.8141 62.8141 M 11 (s) > - 0.2453 0.2453M 2 (s) > - 39.6825 39.6825 M 12 (s) > - 0.3993 0.3993M 3 (s) > - 14.6413 14.6413 M 13 (s) > - 0.6886 0.6886M 4 (s) > - 4.5579 4.5579 M 14 (s) > - 1.0908 1.0908M 5 (s) > - 2.3878 2.3878 M 15 (s) > - 1.8936 1.8936M 6 (s) > - 1.2203 1.2203 M 16 (s) > - 3.7679 3.7679M 7 (s) > - 0.7381 0.7381 M 17 (s) > - 14.4509 14.4509M 8 (s) > - 0.4447 0.4447 M 18 (s) > - 19.8020 19.8020M 10 (s) > - 0.2451 0.2451 M 19 (s) > - 65.1042 65.10425.1. Adaptive MIT1 ControllerAs long as input to the process and its output, { U(t) } and { Y(t) } respectively are bounded, closed loopstability is assured, when implementing Adaptive MIT or Adaptive PI controllers. In order to achieveBIBO stability a constant gain controller has been introduced in Adaptive MIT controller. Thecontroller parameters from PAA are compared (Fig. 5). Whenever the parameter θ 2 of the AdaptiveMIT becomes less than –a/b (refer table II), a fixed gain controller provides the control action byeliminating the output from PAA. Once the condition reverses, adaptive mechanism is allowed toupdate the parameters of the inner loop controller again. This new strategy which accounts for stabilityis named as Adaptive MIT-1.6. Results and DiscussionThe process model is estimated from the data acquired from real time experimentation on thelaboratory air flow control system. The DMRACs with Adaptive MIT, Adaptive MIT-1 and AdaptivePI controllers are designed with the same adaptation rate and implemented in simulation and in realtime experimentation. Fig. 6 shows the tracking responses of the identified model M 3 (s) for the desiredperformance specification in simulation study. The adaptive PI controller with the identified model haslesser settling time when compared to model with adaptive MIT algorithm. The vanishing adaptivebehavior of the controller can be inferred from Fig. 6(d) to 6(g). The response of the Adaptive MITcontroller is smooth and takes more time to settle. The Adaptive PI controller responds very quickly toset point variations than the Adaptive MIT controller. The spans of controller parameters variations arelarge for Adaptive MIT than Adaptive PI for the same input variation .The closed loop is disturbed at 41 and 42 sampling instants. The servo and regulatory responses of theprocess with Adaptive MIT, Adaptive MIT-1 and Adaptive PI are observed and plotted in Figs. 7(a) to7(g). The loop with Adaptive MIT becomes unstable after 53 sampling instants. For the samedisturbance the closed loop is made stable by Adaptive MIT-1 and Adaptive PI controllers. TheAdaptive PI has acted to regulate the disturbance only at 41 and 42 sampling instants. After this thecontroller works for servo variation. The response shows the robustness of the Adaptive PI controller.The θ 2 variation of Adaptive MIT-1 is between -0.81 to -0.5 after the disturbance, thus maintainingthe loop gain within bounds.51

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58Process output3.63.43.232.820 30 40 50 60Sampling instants------- Model output;------- Process response (MIT);------- Process output (PI);Fig. 6 (a). Response of Model and Process inSimulation.Manipulatedvariable605550454020 40 60Sampling instant------ Adaptive MIT controller;------- Adaptive PI controller;Fig. 6 (b). Response of the Controllers.Error0.20.10-0 .1-0 .220 30 40 50 60S a m p lin g in s ta n tProportional gain0.80.3-0.2-0.720 30 40 50 60Sam pling instants------- Adaptive MIT controller;------- Adaptive PI controllerFig. 6 (c). Response of Model Error.Fig. 6 (d). Adaptation of Proportional Gain.Integral gain2.852.82.752.72.6520 30 40 50 60Sampling instantsTheta 14035302520 30 40 50 60Sampling instantsFig. 6 (e). Adaptation of Integral Gain. Fig. 6 (f). Adaptation of Theta 1.Theta 220-2 20 40 60-4-6-8 Sampling instantsFig. 6 (g). Adaptation of Theta 2.52

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58Process output54321020 30 40 50 60 70 80-1Sampling instants------- Model output;--------Unstable Process response with Adaptive MIT;------- Stable Process response with Adaptive MIT-1;-------- Stable Process output with Adaptive PI;-------- Disturbance;Manipulated variable1614121086420-2 20 30 40 50 60 70 80Sam pling instants------- Adaptive MIT controller;-------- Adaptive stable MIT-1 controller;------- Adaptive PI controller;Fig. 7 (a). Response of Model and Process inSimulation (with disturbance).Fig. 7 (b). Response of the Controller.Error1086420-2 20 40 60 80-4-6Sam pling ins tants------- Adaptive MIT controller;-------- Adaptive stable MIT-1 controller------- Adaptive PI controllerProportional gain1.210.80.60.40.2020-0.230 40 50 60 70 80-0.4Sampling instantsFig. 7 (c). Response of Model Error.Fig 7 (d). Adaptation of Proportional GainIntegral gain0.540.520.50.480.460.440.420.420 30 40 50 60 70 80S a m pling insta ntstheta132.521.510.5020 30 40 50 60 70 80-0.5Samp ling instants------- Adaptive MIT controller;-------- Stable Adaptive MIT-1 controller;Fig. 7 (e). Adaptation of Integral Gain. Fig. 7 (f). Adaptation of Theta 1.0.5Theta 2020 30 40 50 60 70 80-0.5-1-1.5-2Sam pling instants------- Adaptive MIT controller;-------- Stable Adaptive MIT-1 controller;Fig. 7 (g). Adaptation of Theta 2.53

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58The control of laboratory air flow control system in real time using dSPACE is presented in thefollowing figures. Fig. 8(a) shows the laboratory air flow process. Fig. 8(b) displays the connection ofthe dSPACE, with the control system.Fig. 8 (a). Laboratory Air Flow Control System.Fig. 8 (b). Experimental Set up for Real TimeImplementation of Adaptive Controllers.The response of the nonlinear process in the range 872 to 1025 lpm is shown in Fig. 9 (a). The trackingability of the process with adaptive PI controller is better when compared to process with adaptiveMIT controller. Fig. 9 (b) displays the output of the controllers. The MIT controller has oscillatoryoutput. But the output of the adaptive PI controller is very smooth. The spans of modelling error(Fig. 9 c) variations are large for the process with adaptive MIT controller. The adaptive PI controlleradapts faster than the adaptive MIT controller. Vanishing adaptation can be visualized from thetransient and steady state nature of the controller parameters (Figs. 9 (d) to 9 (g)).Table III presents the range of adaptive gain variations of two different controllers. Duringexperimentation theta1 ( θ1) parameter of the adaptive MIT controller varies from –0.05 to 0.91 andfrom - 0.3 to 1.61 for theta2 ( θ2) [Table III] for from 1170 to 134 lpm variation. The parameters of theadaptive PI controller various from – 0.017 to 0.017 for K P and from 1.432 to 1.63 for K I . The span ofcontroller parameter variations is more for the adaptive MIT controller. Hence the amount of energyspent by the controller to bring the process output nearer to model output is more for the MITcontroller. The Time Integral Performance Criteria [2] (Integral Square Error (ISE), Integral AbsoluteError (IAE) and Integral Time Absolute Error (ITAE)) are tabulated (Table IV). The ISE, IAE andITAE values are small when adaptive PI controller is placed in the control loop. From table IV, it isinferred that he parameter adaptation loop of the adaptive MIT controller has an increase of 9 % to20 % in ISE, -35 % to 81 % in IAE and -54 % to 65 % in ITAE when compared to adaptive PIcontroller in simulation. Table V displays the various criteria for a stabilized system with two differentcontrollers. Adaptive PI controller’s performance is more satisfactory compared to Adaptive MIT-1controller. The experimental time integral performance criteria for the laboratory nonlinear air flowcontrol system are presented in Table VI.54

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58Flow rate (lpm)1200110010009008000 100 200 300Time (sec)Manipulated variable(*10 volts)0.150.10.0500 100 200 300Tim e (sec)------- Adaptive MIT controller;------- Adaptive PI controller------- Model output;------- process output (MIT);------- Process output (PI);Fig. 9 (a). Response of Model and Process.Fig. 9 (b). Response of the Controllers.Error0.060.040.020-0.02-0.04-0.060 50 100 150 200 250 300Tim e (se c)------- Adaptive MIT controller;------- Adaptive PI controllerProportional gain0.020.0150.010.0050-0.005-0.01-0.015-0.020 100 200 300Tim e (sec)Fig. 9 (c). Response of Model Error.Fig. 9 (d). Adaptation of Proportional gain.1.4380.9Integral gain1.4371.4361.4351.4341.4331.4320 100 200 300Time (sec)Theta 10.850.80.750.70.650.60 100 200 300Time (sec)Fig. 9 (e). Adaptation of Integral gain. Fig. 9 (f). Adaptation of Theta 1.Theta 21.21.151.11.0510 100 200 300Time (sec)Fig. 9 (g). Adaptation of Theta 2.55

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58Table III. Variation in Adaptation Gains for the Different Controllers.Model K P K I θ1θ2Max Min Max Min Min Max MinM 8 (s) & M 11 (s) 0.017 -0.017 1.4825 1.481 0.83 0.7 1.16 1.03M 7 (s) & M 12 (s) 0.015 -0.014 1.4765 1.4705 0.25 - 0.05 1.61 .25M 6 (s) & M 13 (s) 0.016 -0.016 1.467 1.461 0.52 0.29 1.39 .15M 5 (s) & M 14 (s) 0.007 -0.016 1.4583 1.4563 0.74 0.55 1.27 .07M4(s) & M15(s) 0.015 -0.015 1.437 1.432 0.83 0.7 1.16 .03M3(s) & M16(s) 0.002 -0.002 1.4437 1.44 0.91 0.82 1.07 .98M2(s) & M17(s) 0.016 -0.014 1.63 1.54 2.4 0.09 1.1 -0.3Table IV. Time Integral Performance Criteria (Simulation).ModelAdaptive MITAdaptive PIISE IAE ITAE ISE IAE ITAEM 1 (s) 54.15 19.22 184.6 43.56 17.86 284.3M 2 (s) 38.6 15.91 173.1 32.69 15.55 258.1M 3 (s) 22.49 11.85 158.9 20.23 11.64 199.4M 4 (s) 13.02 0.942 180.5 11.81 8.457 156.1M 7 (s) 9.392 13.27 410.9 7.742 7.006 143.8M 15 (s) 12.95 10.49 207.6 11.2 8.082 149.2M 16 (s) 23.15 12 .0 157.5 20.16 11.6 198.6M 17 (s) 28.82 13.18 163.2 26.45 12.94 204.6M 18 (s) 55.15 19.55 186.4 44.04 18.03 288.3Table V. Time Integral Performance Criteria for a Stabilized System (Simulation- Model M7).Controller ISE IAE ITAEAdaptive MIT1 18.42 12.43 -246.1Adaptive PI 14.06 10.88 -72.53Flow Rate (lpm)Table VI. Time Integral Performance Criteria (Real Time).Adaptive MITAdaptive PIISE IAE ITAE ISE IAE ITAE575 – 725 & 725 – 575 0.0064 0.5541 85.16 0.00105 0.2358 23.91025 – 1170 & 1170 – 1025 0.6406 10.05 121.0 0.1056 1.916 109.9872 – 1025 & 1025 – 872 0.09036 3.226 401.1 0.0044 0.6084 68.98725 – 872 & 872 – 725 0.02113 1.017 97.52 0.0037 0.5133 48.9428 – 575 & 575 – 428 0.0083 0.572 61.81 0.00819 0.5767 58.61280 – 428 & 428 – 280 0.0048 0.4898 46.26 0.0054 0.6611 91.07134 – 280 & 280 – 134 0.05178 2.208 229.5 0.0402 1.706 23.956

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-587. ConclusionsThis paper has presented design of adaptive PI controller based on MIT algorithm and identification ofthe nonlinear Air Flow Control System using the data acquired in real time. The methodology ofdesign and implementation of Adaptive controllers in simulation are discussed. The performance ofAdaptive PI and MIT controllers are validated in real time by interfacing the laboratory Air FlowControl System through dSPACE interface Card and MATLAB. The overall system performancewhen employing Adaptive PI is observed to be better than that of the system with Adaptive MITcontroller. The oscillations in the PI controller output are negligible when compared to MIT controller.It is inferred that there is an increase of -12 % to 95 % in ISE values, -34 % to 81 %, increase in IAEvalues for the Adaptive MIT controller from the comparison table VI. Further it is inferred that ITAEvalues of adaptive MIT controllers has an increase of -96 % to 90 % when compared to adaptive PI. Soit can be concluded that except for flow ranges from 280 – 428 & 428 – 280 lpm the adaptive PIcontroller outperforms the adaptive MIT controller. Adaptive PI controller responds with 76.33 %lesser, 87.53 % lesser IAE and 29.47 % lesser ITAE (Table V) when compared to Adaptive MIT-1controller. From the simulation and experimental responses it is found that the adaptive PI controller isa better controller for this airflow process.AcknowledgementsD. Rathikarani, author thanks the authorities of Annamalai University for granting permission toaccess the facilities in the Instrumentation and Process Control lab to carry out the experimentationwork.References[1]. B. Wayne Bequette, Process Control – Modelling, Design and Simulation, Prentice-Hall of India, 2003,pp. 132.[2]. George Stephanopoulos, Chemical Process Control, Prentice Hall of India, 1984, pp. 247.[3]. Karl Johan Astrom, PID Controllers-Theory, Design, and Tuning, ISA, North California, NC, 1994.[4]. Astrom, K. J, Theory and applications of adaptive control - A survey, Automatica, 19, 5, 1983,pp. 471–486.[5]. Clarke, D. W. and Gawthrop, P. J., Self-tuning control, Proc. Inst. Electr. Eng., 126 D, 6, 1979,pp. 633–640.[6]. Ziegler, J. G. and Nichols, N. B., Optimum settings for automatic controllers, Trans. ASME, 1942, 64, 8,pp. 759–768.[7]. Nims, P. T, Some Design Criteria for Automatic Controls, Trans. AIEE, 70, 1950, pp. 606–611.[8]. Chien, K. L., Hrones, J. A., and Reswick, J. B, On the automatic control of generalized passive systems,Trans. ASME, 74, 1972, pp. 175–185.[9]. K. Sukvichai, The Application of Nonlinear Model Reference PID Controller for a Planar Robot Arm, inProceedings of ECTI-CON, Vol.2, 2008, pp. 637-640.[10]. Xu Guo-kail, Research on Discrete Model Reference Adaptive Control System of Electric Vehicle,SICE- ICASE International Joint Conference, 2006.[11]. Astrom, K. J. and Hagglund T, Automatic tuning of PID controllers, Instrument Society of America,Research Triangle Park, 1988.[12]. Nishikawa, Y., Sannomiya, N., Ohta, T. and Tanaka, H., A method for autotuning of PID controlparameters, Automatica, 1984, 20, 3, pp. 321–332.[13]. Menani, S., and Koivo, H. N., Relay tuning of multivariable PI controllers, Preprints of 13 th World IFACCongress, San Francisco, Vol. K, 1996, pp. 139–144.[14]. N. Selvaganesan, Online Tuning of PID Controller for Time Variant Systems using Genetic Algorithm,Proceedings of the Second International Conference on ‘Artificial Intelligence in Engineering &Technology’, August 3-5, 2004, pp. 74-77.57

Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58[15]. Engin YESIL, Online Tuning of Set-point Regulator with a Blending Mechanism Using PI Controller, TurkJ Elec Engin, Vol. 16, No. 2, 2008.[16]. Ali S. Zayed, A multivariable generalised minimum-variance stochastic self-tuning controller with polezeroplacement, International Journal of Control and Intelligent Systems, Vol. 32, No. 1, 2004, pp. 35-44.[17]. Tjokro, S. and Shah, S. L, Adaptive PID control, Proc. American Control Conf., Boston, MA,pp. 1528–1534.[18]. Gawthrop, P. J., Self-tuning PID controller: Algorithms and Implementation, IEEE Trans. Autom. Control,31, 3, 1986, pp. 201–209.[19]. Radke, F. and Isermann, R., A parameter-adaptive PID-controller with stepwise parameter optimization,Automatica, 23, 4, 1987, pp. 449–457.[20]. Miller, R. M., Kwok, K. E., Shah, S. L. and Wood, R. K., Development of a stochastic predictive PIDcontroller, in Proc. American Control Conf., Seattle, WA, 1995, pp. 4204–4208.[21]. Fujii, K., Yamamoto, T. and Kaneda, M., Self-tuning PID control of a polybutene process, Preprints ofIFAC Int. Symp. on ‘Advance Control of Chemical Processes', Banff, 1997, pp. 107–112.[22]. R. Marino, S. Peresada, and P. Tomei, Adaptive output feedback control of current-fed induction motorswith uncertain rotor resistance and load torque, Automatica, Vol. 34, No. 5, May 1998, pp. 617-624.[23]. Isermann K., Practical Aspects of Process Identification, Automatica, Vol. 16, 1980, pp. 575 – 587.[24]. Nagrath I. J, Gopal M., Control System Engineering, New Age International Publishers, Chennai, 2004.___________________2009 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.(http://www.sensorsportal.com)58

Sensors & Transducers JournalGuide for ContributorsAims and ScopeSensors & Transducers Journal (ISSN 1726-5479) provides an advanced forum for the science and technologyof physical, chemical sensors and biosensors. It publishes state-of-the-art reviews, regular research andapplication specific papers, short notes, letters to Editor and sensors related books reviews as well asacademic, practical and commercial information of interest to its readership. Because it is an open access, peerreview international journal, papers rapidly published in Sensors & Transducers Journal will receive a very highpublicity. The journal is published monthly as twelve issues per annual by International Frequency Association(IFSA). In additional, some special sponsored and conference issues published annually. Sensors &Transducers Journal is indexed and abstracted very quickly by Chemical Abstracts, IndexCopernicus JournalsMaster List, Open J-Gate, Google Scholar, etc.Topics CoveredContributions are invited on all aspects of research, development and application of the science and technologyof sensors, transducers and sensor instrumentations. Topics include, but are not restricted to:• Physical, chemical and biosensors;• Digital, frequency, period, duty-cycle, time interval, PWM, pulse number output sensors and transducers;• Theory, principles, effects, design, standardization and modeling;• Smart sensors and systems;• Sensor instrumentation;• Virtual instruments;• Sensors interfaces, buses and networks;• Signal processing;• Frequency (period, duty-cycle)-to-digital converters, ADC;• Technologies and materials;• Nanosensors;• Microsystems;• Applications.Submission of papersArticles should be written in English. Authors are invited to submit by e-mail editor@sensorsportal.com 8-14pages article (including abstract, illustrations (color or grayscale), photos and references) in both: MS Word(doc) and Acrobat (pdf) formats. Detailed preparation instructions, paper example and template of manuscriptare available from the journal’s webpage: http://www.sensorsportal.com/HTML/DIGEST/Submition.htm Authorsmust follow the instructions strictly when submitting their manuscripts.Advertising InformationAdvertising orders and enquires may be sent to sales@sensorsportal.com Please download also our media kit:http://www.sensorsportal.com/DOWNLOADS/Media_Kit_2009.pdf