Fuzzy Control of a Multivariable Nonlinear Process
|
|
- Merryl Hunt
- 6 years ago
- Views:
Transcription
1 Fuzzy Control of a Multivariable Nonlinear Process A. Iriarte Lanas 1, G. L.A. Mota 1, R. Tanscheit 1, M.M. Vellasco 1, J.M.Barreto 2 1 DEE-PUC-Rio, CP , Rio de Janeiro - RJ, Brazil 1 (ricardo, marley)@ele.puc-rio.br; 2 barreto@inf.ufsc.br Abstract This paper presents a qualitative control of a fluid mixer, which is a multivariable and intrinsically non-linear plant. The mixer has as inputs two fluids of different colours and the output is the colour of the resulting mix. The control system consists of two independent fuzzy controllers which are responsible for maintaining the water level at a given height and for adjusting the colour of the fluid in the mixing tank. The main points studied are the response when the desired colour is changed and when the output flow changes. Simulation results show that the approach of using two independent controllers, with simple rule-bases, can give good results. 1: Introduction Ordinary fuzzy controllers have been succesfully applied to a variety of plants since the pioneering works of Mamdani and colaborators [1]. In the case of multivariable processes, the natural approach would be to consider as rules antecedents all the controller inputs, which grow in number as the number of desired outputs, or reference inputs, grows. This would certainly make the process of designing the control strategy, or rule-base, a more complex one. On the other hand, if independent controllers are used for each reference input, rule-base design becomes simpler and the control strategy becomes potentially more reliable. In the case of robot control through a learning fuzzy controller [2], it has been shown that the use of independent controllers for each link can give good results. That is, the controllers, by adjusting their set of rules, cope very well with the coupling between variables. In this work the approach of using separate fuzzy controllers is employed, with the purpose of verifying whether this strategy is adequate. The plant, described in the next section, is a fluid mixer, which, by presenting non-linear characteristics, provides an additional complexity and constitutes a good test for the designed fuzzy control system. 2: Description of the Plant The plant, shown in Fig.1, consists of a mixing and two auxiliary tanks. The first auxiliary tank contains coulored water c 1, while the second one contains clear water c 2. The input flow q to the mixing tank is controlled by two valves, which regulate the output flows q 1 and q 2 from the auxiliary tanks. The output flow q 0, taken as a disturbance, has the coloration c of the resulting mix and is a function of the output pipe cross-section ab, of the liquid level h in the mixing tank and of a constant C d related to the shape and material of the output pipe. In oder to simplify the simulation, it has been assumed that the auxiliary tanks always contain sufficient liquid for the process to keep on running. The mixing tank dimension is 20x10x180 cm. and the output cross-section ab can be set to values between 0 and 0.4 cm 2. The output flow q 0 lies between 0 and 60 cm 3 /sec. Colorations c 1 and c 2 are set to 1 and 0, respectively. Fig. 1: The Fluid Mixer To derive a mathematical model, so that a simulated experiments can be performed, the time needed to obtain a uniform mixture, time delays related to flows in the pipes and the dynamics of input and ouput valves have been neglected [3].
2 The following equations model the plant dynamics: dv dh q - q0 = = S. dt dt (1) q = q 1 + q 2 (2) q 0 = Cd.ab. 2gh (3) where g is the gravity acceleration, V is the volume of liquid and S is the area of the liquid surface in the mixing tank. By using (3) in (1): dh Cd.ab. 2gh q = + (4) dt S S The mixing process is modelled by: dh 1 = (q1 + q2 q0) dt S (5) d(c0.s.h) dh dc0 c1.q1 c0.q0 = = S.(c0. + h. ) dt dt dt (6) By combining (5) and (6): dc0 1 =.(c1.q1 c0(q1 + q 2)) (7) dt Sh Equations (4) and (7) describe the system's dynamics; h and c 0 are the variables to be manipulated by the controlling system. 3: Fuzzy Control System The control strategy is described by a set of linguistic statements, or rules. Consider, for example, the case where each control rule relates two input variables e and ce to the controller ouput u, and a control algorithm consisting of a set of rules R 1, R 2,..., R n, of the IF (E is E j ) AND (CE is CE j ) THEN (U is U j ) form, connected by a ELSE connective [4]. The combination of those rules can be expressed mathematically (by its membership function) as: µ R N (e,ce,u) = f 1 [µ R 1 (e,ce,u),..., µ R n (e,ce,u)] (8) where f 1 expresses the ELSE connective. In (8) above, each control rule j can be expressed as: µ R j (e,ce,u) = f 2 [f 3 (µ E j (e), µ CE j (ce)), µ U j (u)] (9) In (9), E = {e}, CE = {ce}, U = {u} are finite universes and E j, CE j and U j are fuzzy subsets of those universes. The operator f 2 stands for implication [5], and f 3 is the interpretation of the connective AND, which is usually taken as min ( ). The controller decides which action to take through a compositional rule of inference. As is generally the case in control, the controller inputs are real measured values given by singletons, and called here e s and ce s. The controller output fuzzy set Us will thus be given by: µ Us (u) = f 1 [f 2 (µ E 1(e s ) µ CE 1(ce s ), µ U 1(u)),......, f 2 (µ E n(e s ) µ CE n(ce s ), µ U n(u))] (10) Since the process requires at its input non-fuzzy values, the controller output fuzzy set must be defuzzified, the result being a value u s. In the fluid mixer under consideration, the control system has to be designed to keep both the output coloration c 0 and the liquid height h in the mixing tank at desired setpoints. The quantitative information needed by the control system in order to attain those goals is given by the coloration error e c and change in error Δe c, the height error e h and the ouput flow q 0. Since the chosen strategy makes use of two independents fuzzy controllers, one for height control and another for coloration control, it is more convenient to choose as output variables the total flow q (equation 2) and the proportion q r of coloured water in the total flow, defined as: q1 q r = (11) q + q 1 2 The height controller has two quantitative inputs, e h and q 0, and one ouput, q. The coloration controller has as quantitative inputs e c and Δe c, and as its ouput Δq r. There is a slight difference between the height and coloration controllers. While in the former precision is not a fundamental factor, in the latter the goal is to have a fine control, with null steady-state error if possible; thus the use of a structure with PI characteristics. 3.1: Height Control In the design of the height controller, fuzzy sets NB, NM, Z, M, B, PB are assigned to E h ={e h }, and fuzzy sets Z, S,M, B, VB to Q = {q} and Q 0 = {q 0 }, as specified by their membership functions shown in Fig. 2 and Fig. 3 below. The universes of discourse for each variable are clearly shown in those figures. The non-fuzzy actual values of the measured input variables are mapped to the chosen universes of discourse through scaling factors GE h and GQ 0, which are part of blocks S 0 and S 1 in the simulation diagram of Fig. 6. The resulting values are called e hs and q 0s. The defuzzified controller output q s is mapped to the process input actual values through a scaling factor GQ, so that q = q s x GQ. In this controller, f 1 is implemented by max and f 2 by min; Mean of Maxima (MOM) [6] is used for defuzzification.
3 Fig. 2: Membership functions for E h Fig. 5: Membership functions for ΔQ r Fig. 3: Membership functions for Q and Q 0 The rule-base for height control is summarized in Table I, where the entries correspond to the values of Q. E h Q Z S M B VB PB M B VB VB VB PM S M B VB VB Z Z S M B VB NM Z Z S M B NB Z Z Z M B Q 0 Table I: Rule-base for height control 3.2: Coloration Control In the coloration controller, fuzzy sets NB, NB, Z, PM and PB are used for E c ={e c } and ΔE c ={Δe c }, while fuzzy sets NVB, NB, NM, NS, Z, PS, PM, PB and PVB are used for the controller output ΔQ r ={q r }, as specified by their membership functions shown in Fig. 4 and Fig. 5. The universes of discourse are as shown in those figures. The actual measured values of the controller inputs are mapped to the universes of discourse through scaling factors GE c and GΔE c, (contained in S 2 and S 3 in the diagram of Fig. 6), the result being variables e cs and Δe cs. The defuzzified controller output Δq rs is mapped to the process input actual values through a scaling factor GΔQ r. The actual input flows to the fluid mixer are given by q 1 =q r q and q 2 =q r q 1. In this controller, f 1 is implemented by max, and f 2 by product [6]; defuzzification is performed through Centre of Gravity (COG), which in general provides smoother responses than MOM. Fig. 4: Membership functions for E c and ΔE c The larger number of fuzzy sets used for the controller output in coloration control, as compared to height control, is due to the requirement of higher accuracy in the coloration of the resulting mix than that for the level of liquid in the mixing tank. In fact, the reason for keeping h at a desired setpoint is mainly to prevent an undesirable overflow in that tank. The rule-base for coloration control is summarized in Table II, where the entries are the values of ΔQ r. E c ΔQ r NB NM Z PM PB PB NVB NB NM NS Z PM NB NM NS Z PS Z NM NS Z PS PM NM NS Z PS PM PB NB Z PS PM PB PVB E c Table II: Rule-base for coloration control 4: Simulated experiments The process response will depend on the scaling factors, which can be empirically set beforehand and then tuned in order to improve the controller performance. For example, in the case of the fluid mixer, the range established for q and q 0 was from 0 to 60 cm 3. Since the corresponding universes of discourse are [0,4], GQ and GQ 0 were initially set at 15. This value gave origin to satisfactory responses and did not need to be tuned. The maximum possible height error e h was assumed to be ± 7 cm, which, considering the corresponding universe as [-2,2], gives GE h =0.3. In order to have good accuracy in coloration, and by noting that the universe of discourse is [-2,2] for that variable, the scaling factor GE c was
4 eventually set at 200; GΔE c was set at Finally, GΔQ r was made equal to 1/15. In the simulation MATLAB, its associated tool SIMULINK and the FuzzyToolbox have been used. The simulation diagram is shown in Fig. 6. Experiment 1: The resulting mix coloration behaviour for output valve settings of 0.1, 0.2 and 0.4 cm 2 is shown in Fig. 7, when a step from 0.1 to 0.3 is applied to the reference input. The level of liquid in the mixing tank should be kept constant 30 cm. It can be seen that the larger the output valve opening, the faster the change in coloration. Since the level of liquid must be kept unchanged, the larger the output flow, the larger the input one, which causes a faster change in coloration. It can be concluded that the coloration controller tries to reach the reference input as fast as possible, but this will also depend on the action of the height controller on the input flow. Experiment 2: The level (height) of liquid in the mixing tank is shown in Fig. 8 when a step from 10 to 50 cm is applied to the height reference input. Output valve settings of 0.1 and 0.2 cm 2 have been used.; the final coloration should be kept unchanged at 0.5. It can be observed that in both cases the reference is quickly achieved, but the control is not as precise as in the coloration case. As said before, this is due to less rigid specifications for height control. The objective of keeping coloration unchanged at 0.5 was also achieved. Fig. 6: Simulation Diagram Fig. 7: Experiment 1 Fig. 8: Experiment 2 Experiment 3: Coloration and height variations are shown in Fig. 9 for different output valve openings when steps of
5 0.1 to 0.3 and of 0 to 30 cm. are apllied to the coloration and height reference inputs, respectively. It can be observed that the reponse-time is smaller than in experiment 1. This can be attributed to a larger input flow, since the step applied to the height reference input allows a rather fast change in coloration. The response shows a small overshoot and the output valve opening has a small influence on the overall behaviour. The tuning of the controllers showed, as expected, that the system's performance is sensible to changes in the scaling factors. Although this is beyond the scope of this work, an automatic tuning procedure could be of help in this respect. This same plant is being used for further investigations, such as automatic rule creation and modification and the design of a MIMO fuzzy controller, with four inputs and two outputs. Results will then be compared to those presented in this work. 6: References 5: Conclusions Fig. 9: Experiment 3 The simulated experiments have shown that the approach of using two independent fuzzy controllers in the control of a MIMO system can give good results. In the plant considered in this work, the introduction of a new variable allowed the reasoning process to occur in a decoupled fashion. The main objective of reaching a specified coloration for the liquid in the mixing tank was achieved with very good precision - error of less than The height controller was capable of maintaining the liquid level in the mixing tank near the reference value, even without the use of the change in error as a controller input. [1] Mamdani, E.H., "Application of fuzzy algorithms for control of simple dynamic plant". Proc. IEE (Control and Science), V. 121, pp , [2] Tanscheit, R. and Lembessis, E., "On the behaviour and tuning of a fuzzy rule-based self-organising controller". In: Mathematics of the Analysis and Design of Process Control. P. Borne, S.G. Tzafestas, N.E. Radhy (Eds). Elsevier Science Publishers, [3] Barreto, J.M., Hierarchical Control of Fluid Mixer". Ciência e Técnica (Brazil), V. 13, pp 35-39, [4] Lembessis, E. and Tanscheit, R., "The influence of implication operators and defuzzification methods on the deterministic output of a fuzzy rule-based controller". Proc. 4 th IFSA Congress, Brussels, [5] Mendel, J.M. Fuzzy logic systems for engineering: a tutorial, Proc. IEEE, V. 3, pp , [6] Driankov, D.; Hellendorn, H.; Rheinfrank, M., "An Introduction to Fuzzy Control". Springer-Verlag, Acknowledgement This work was partially supported by the Brazilian Research Agencies CAPES and CNPq.
CHAPTER 7 MODELING AND CONTROL OF SPHERICAL TANK LEVEL PROCESS 7.1 INTRODUCTION
141 CHAPTER 7 MODELING AND CONTROL OF SPHERICAL TANK LEVEL PROCESS 7.1 INTRODUCTION In most of the industrial processes like a water treatment plant, paper making industries, petrochemical industries,
More informationRamchandraBhosale, Bindu R (Electrical Department, Fr.CRIT,Navi Mumbai,India)
Indirect Vector Control of Induction motor using Fuzzy Logic Controller RamchandraBhosale, Bindu R (Electrical Department, Fr.CRIT,Navi Mumbai,India) ABSTRACT: AC motors are widely used in industries for
More informationCHAPTER 5 FREQUENCY STABILIZATION USING SUPERVISORY EXPERT FUZZY CONTROLLER
85 CAPTER 5 FREQUENCY STABILIZATION USING SUPERVISORY EXPERT FUZZY CONTROLLER 5. INTRODUCTION The simulation studies presented in the earlier chapter are obviously proved that the design of a classical
More informationA Study on Performance of Fuzzy And Fuzyy Model Reference Learning Pss In Presence of Interaction Between Lfc and avr Loops
Australian Journal of Basic and Applied Sciences, 5(2): 258-263, 20 ISSN 99-878 A Study on Performance of Fuzzy And Fuzyy Model Reference Learning Pss In Presence of Interaction Between Lfc and avr Loops
More informationFUZZY CONTROL CONVENTIONAL CONTROL CONVENTIONAL CONTROL CONVENTIONAL CONTROL CONVENTIONAL CONTROL CONVENTIONAL CONTROL
Eample: design a cruise control system After gaining an intuitive understanding of the plant s dynamics and establishing the design objectives, the control engineer typically solves the cruise control
More informationType-2 Fuzzy Logic Control of Continuous Stirred Tank Reactor
dvance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 3, Number 2 (2013), pp. 169-178 Research India Publications http://www.ripublication.com/aeee.htm Type-2 Fuzzy Logic Control of Continuous
More informationDesign of Decentralized Fuzzy Controllers for Quadruple tank Process
IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.11, November 2008 163 Design of Fuzzy Controllers for Quadruple tank Process R.Suja Mani Malar1 and T.Thyagarajan2, 1 Assistant
More informationEnhanced Fuzzy Model Reference Learning Control for Conical tank process
Enhanced Fuzzy Model Reference Learning Control for Conical tank process S.Ramesh 1 Assistant Professor, Dept. of Electronics and Instrumentation Engineering, Annamalai University, Annamalainagar, Tamilnadu.
More informationFuzzy Control Systems Process of Fuzzy Control
Fuzzy Control Systems The most widespread use of fuzzy logic today is in fuzzy control applications. Across section of applications that have successfully used fuzzy control includes: Environmental Control
More informationDesign On-Line Tunable Gain Artificial Nonlinear Controller
Journal of Computer Engineering 1 (2009) 3-11 Design On-Line Tunable Gain Artificial Nonlinear Controller Farzin Piltan, Nasri Sulaiman, M. H. Marhaban and R. Ramli Department of Electrical and Electronic
More informationLOW COST FUZZY CONTROLLERS FOR CLASSES OF SECOND-ORDER SYSTEMS. Stefan Preitl, Zsuzsa Preitl and Radu-Emil Precup
Copyright 2002 IFAC 15th Triennial World Congress, Barcelona, Spain LOW COST FUZZY CONTROLLERS FOR CLASSES OF SECOND-ORDER SYSTEMS Stefan Preitl, Zsuzsa Preitl and Radu-Emil Precup Politehnica University
More informationProcess Control Hardware Fundamentals
Unit-1: Process Control Process Control Hardware Fundamentals In order to analyse a control system, the individual components that make up the system must be understood. Only with this understanding can
More informationEFFECT OF VARYING CONTROLLER PARAMETERS ON THE PERFORMANCE OF A FUZZY LOGIC CONTROL SYSTEM
Nigerian Journal of Technology, Vol. 19, No. 1, 2000, EKEMEZIE & OSUAGWU 40 EFFECT OF VARYING CONTROLLER PARAMETERS ON THE PERFORMANCE OF A FUZZY LOGIC CONTROL SYSTEM Paul N. Ekemezie and Charles C. Osuagwu
More informationUncertain System Control: An Engineering Approach
Uncertain System Control: An Engineering Approach Stanisław H. Żak School of Electrical and Computer Engineering ECE 680 Fall 207 Fuzzy Logic Control---Another Tool in Our Control Toolbox to Cope with
More informationControl of Conical Tank Level using in Industrial Process by Fuzzy Logic Controller
Control of Conical Tank Level using in Industrial Process by Fuzzy Logic Controller Alia Mohamed 1 and D. Dalia Mahmoud 2 1 Department of Electronic Engineering, Alneelain University, Khartoum, Sudan aliaabuzaid90@gmail.com
More information3- DOF Scara type Robot Manipulator using Mamdani Based Fuzzy Controller
659 3- DOF Scara type Robot Manipulator using Mamdani Based Fuzzy Controller Nitesh Kumar Jaiswal *, Vijay Kumar ** *(Department of Electronics and Communication Engineering, Indian Institute of Technology,
More informationControl of MIMO processes. 1. Introduction. Control of MIMO processes. Control of Multiple-Input, Multiple Output (MIMO) Processes
Control of MIMO processes Control of Multiple-Input, Multiple Output (MIMO) Processes Statistical Process Control Feedforward and ratio control Cascade control Split range and selective control Control
More informationDesign and Stability Analysis of Single-Input Fuzzy Logic Controller
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B: CYBERNETICS, VOL. 30, NO. 2, APRIL 2000 303 Design and Stability Analysis of Single-Input Fuzzy Logic Controller Byung-Jae Choi, Seong-Woo Kwak,
More informationProcess Control, 3P4 Assignment 6
Process Control, 3P4 Assignment 6 Kevin Dunn, kevin.dunn@mcmaster.ca Due date: 28 March 204 This assignment gives you practice with cascade control and feedforward control. Question [0 = 6 + 4] The outlet
More informationIntelligent Systems and Control Prof. Laxmidhar Behera Indian Institute of Technology, Kanpur
Intelligent Systems and Control Prof. Laxmidhar Behera Indian Institute of Technology, Kanpur Module - 2 Lecture - 4 Introduction to Fuzzy Logic Control In this lecture today, we will be discussing fuzzy
More informationIndex Terms Magnetic Levitation System, Interval type-2 fuzzy logic controller, Self tuning type-2 fuzzy controller.
Comparison Of Interval Type- Fuzzy Controller And Self Tuning Interval Type- Fuzzy Controller For A Magnetic Levitation System Shabeer Ali K P 1, Sanjay Sharma, Dr.Vijay Kumar 3 1 Student, E & CE Department,
More informationFUZZY CONTROL. Main bibliography
FUZZY CONTROL Main bibliography J.M.C. Sousa and U. Kaymak. Fuzzy Decision Making in Modeling and Control. World Scientific Series in Robotics and Intelligent Systems, vol. 27, Dec. 2002. FakhreddineO.
More informationCONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XVII - Analysis and Stability of Fuzzy Systems - Ralf Mikut and Georg Bretthauer
ANALYSIS AND STABILITY OF FUZZY SYSTEMS Ralf Mikut and Forschungszentrum Karlsruhe GmbH, Germany Keywords: Systems, Linear Systems, Nonlinear Systems, Closed-loop Systems, SISO Systems, MISO systems, MIMO
More informationEEE 8005 Student Directed Learning (SDL) Industrial Automation Fuzzy Logic
EEE 8005 Student Directed Learning (SDL) Industrial utomation Fuzzy Logic Desire location z 0 Rot ( y, φ ) Nail cos( φ) 0 = sin( φ) 0 0 0 0 sin( φ) 0 cos( φ) 0 0 0 0 z 0 y n (0,a,0) y 0 y 0 z n End effector
More informationInstitute for Advanced Management Systems Research Department of Information Technologies Åbo Akademi University. Fuzzy Logic Controllers - Tutorial
Institute for Advanced Management Systems Research Department of Information Technologies Åbo Akademi University Directory Table of Contents Begin Article Fuzzy Logic Controllers - Tutorial Robert Fullér
More informationSoft Computing Technique and Conventional Controller for Conical Tank Level Control
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol. 4, No. 1, March 016, pp. 65~73 ISSN: 089-37, DOI: 10.11591/ijeei.v4i1.196 65 Soft Computing Technique and Conventional Controller
More informationFuzzy Logic Control for Half Car Suspension System Using Matlab
Fuzzy Logic Control for Half Car Suspension System Using Matlab Mirji Sairaj Gururaj 1, Arockia Selvakumar A 2 1,2 School of Mechanical and Building Sciences, VIT Chennai, Tamilnadu, India Abstract- To
More informationControl of an Ambiguous Real Time System Using Interval Type 2 Fuzzy Logic Control
International Journal of Applied Engineering Research ISSN 973-462 Volume 12, Number 21 (17) pp.11383-11391 Control of an Ambiguous Real Time System Using Interval Type 2 Fuzzy Logic Control Deepa Thangavelusamy
More informationNEW CONTROL STRATEGY FOR LOAD FREQUENCY PROBLEM OF A SINGLE AREA POWER SYSTEM USING FUZZY LOGIC CONTROL
NEW CONTROL STRATEGY FOR LOAD FREQUENCY PROBLEM OF A SINGLE AREA POWER SYSTEM USING FUZZY LOGIC CONTROL 1 B. Venkata Prasanth, 2 Dr. S. V. Jayaram Kumar 1 Associate Professor, Department of Electrical
More informationDESIGN OF FUZZY ESTIMATOR TO ASSIST FAULT RECOVERY IN A NON LINEAR SYSTEM K.
DESIGN OF FUZZY ESTIMATOR TO ASSIST FAULT RECOVERY IN A NON LINEAR SYSTEM K. Suresh and K. Balu* Lecturer, Dept. of E&I, St. Peters Engg. College, affiliated to Anna University, T.N, India *Professor,
More informationRule-Based Fuzzy Model
In rule-based fuzzy systems, the relationships between variables are represented by means of fuzzy if then rules of the following general form: Ifantecedent proposition then consequent proposition The
More informationMULTILOOP PI CONTROLLER FOR ACHIEVING SIMULTANEOUS TIME AND FREQUENCY DOMAIN SPECIFICATIONS
Journal of Engineering Science and Technology Vol. 1, No. 8 (215) 113-1115 School of Engineering, Taylor s University MULTILOOP PI CONTROLLER FOR ACHIEVING SIMULTANEOUS TIME AND FREQUENCY DOMAIN SPECIFICATIONS
More informationA linguistic fuzzy model with a monotone rule base is not always monotone
EUSFLAT - LFA 25 A linguistic fuzzy model with a monotone rule base is not always monotone Ester Van Broekhoven and Bernard De Baets Department of Applied Mathematics, Biometrics and Process Control Ghent
More informationAERO-ENGINE ADAPTIVE FUZZY DECOUPLING CONTROL
AERO-ENGINE ADAPTIVE FUZZY DECOUPLING CONTROL Xinyu Ren and Siqi Fan School of Aero-engine Engineering, Northwestern Polytechnical University Xi'an 710072, China Abstract: Key words: A new kind of multivariable
More informationGAIN SCHEDULING CONTROL WITH MULTI-LOOP PID FOR 2- DOF ARM ROBOT TRAJECTORY CONTROL
GAIN SCHEDULING CONTROL WITH MULTI-LOOP PID FOR 2- DOF ARM ROBOT TRAJECTORY CONTROL 1 KHALED M. HELAL, 2 MOSTAFA R.A. ATIA, 3 MOHAMED I. ABU EL-SEBAH 1, 2 Mechanical Engineering Department ARAB ACADEMY
More informationReduced Size Rule Set Based Fuzzy Logic Dual Input Power System Stabilizer
772 NATIONAL POWER SYSTEMS CONFERENCE, NPSC 2002 Reduced Size Rule Set Based Fuzzy Logic Dual Input Power System Stabilizer Avdhesh Sharma and MLKothari Abstract-- The paper deals with design of fuzzy
More informationGain Scheduling Control with Multi-loop PID for 2-DOF Arm Robot Trajectory Control
Gain Scheduling Control with Multi-loop PID for 2-DOF Arm Robot Trajectory Control Khaled M. Helal, 2 Mostafa R.A. Atia, 3 Mohamed I. Abu El-Sebah, 2 Mechanical Engineering Department ARAB ACADEMY FOR
More informationA fuzzy logic controller with fuzzy scaling factor calculator applied to a nonlinear chemical process
Rev. Téc. Ing. Univ. Zulia. Vol. 26, Nº 3, 53-67, 2003 A fuzzy logic controller with fuzzy scaling factor calculator applied to a nonlinear chemical process Alessandro Anzalone, Edinzo Iglesias, Yohn García
More informationDesign of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process
Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process D.Angeline Vijula #, Dr.N.Devarajan * # Electronics and Instrumentation Engineering Sri Ramakrishna
More informationTHE ANNALS OF "DUNAREA DE JOS" UNIVERSITY OF GALATI FASCICLE III, 2000 ISSN X ELECTROTECHNICS, ELECTRONICS, AUTOMATIC CONTROL, INFORMATICS
ELECTROTECHNICS, ELECTRONICS, AUTOMATIC CONTROL, INFORMATICS ON A TAKAGI-SUGENO FUZZY CONTROLLER WITH NON-HOMOGENOUS DYNAMICS Radu-Emil PRECUP and Stefan PREITL Politehnica University of Timisoara, Department
More informationPerformance Of Power System Stabilizerusing Fuzzy Logic Controller
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331, Volume 9, Issue 3 Ver. I (May Jun. 2014), PP 42-49 Performance Of Power System Stabilizerusing Fuzzy
More informationFAST DEFUZZIFICATION METHOD BASED ON CENTROID ESTIMATION
FAS DEFUZZIFICAION MEHOD BASED ON CENROID ESIMAION GINAR, Antonio Universidad Simón Bolívar, Departamento de ecnología Industrial, Sartenejas, Edo. Miranda, Apartado Postal 89000, enezuela. E-mail: aginart@usb.ve
More informationDesign and Implementation of PI and PIFL Controllers for Continuous Stirred Tank Reactor System
International Journal of omputer Science and Electronics Engineering (IJSEE olume, Issue (4 ISSN 3 48 (Online Design and Implementation of PI and PIFL ontrollers for ontinuous Stirred Tank Reactor System
More informationCOMPARISON OF FUZZY LOGIC CONTROLLERS FOR A MULTIVARIABLE PROCESS
COMPARISON OF FUZZY LOGIC CONTROLLERS FOR A MULTIVARIABLE PROCESS KARTHICK S, LAKSHMI P, DEEPA T 3 PG Student, DEEE, College of Engineering, Guindy, Anna University, Cennai Associate Professor, DEEE, College
More informationDECENTRALIZED PI CONTROLLER DESIGN FOR NON LINEAR MULTIVARIABLE SYSTEMS BASED ON IDEAL DECOUPLER
th June 4. Vol. 64 No. 5-4 JATIT & LLS. All rights reserved. ISSN: 99-8645 www.jatit.org E-ISSN: 87-395 DECENTRALIZED PI CONTROLLER DESIGN FOR NON LINEAR MULTIVARIABLE SYSTEMS BASED ON IDEAL DECOUPLER
More informationCHAPTER 6 CLOSED LOOP STUDIES
180 CHAPTER 6 CLOSED LOOP STUDIES Improvement of closed-loop performance needs proper tuning of controller parameters that requires process model structure and the estimation of respective parameters which
More informationGeneral-Purpose Fuzzy Controller for DC/DC Converters
General-Purpose Fuzzy Controller for DC/DC Converters P. Mattavelli*, L. Rossetto*, G. Spiazzi**, P.Tenti ** *Department of Electrical Engineering **Department of Electronics and Informatics University
More informationDesign of an Intelligent Controller for Armature Controlled DC Motor using Fuzzy Logic Technique
esign of an ntelligent Controller for Armature Controlled C Motor using Fuzzy Logic Technique J. Velmurugan jvelmurugan76@gmail.com R. M. Sekar ssvedha08@gmail.com B. Pushpavanam pushpavanamb4u@gmail.com
More informationPERFORMANCE ANALYSIS OF TWO-DEGREE-OF-FREEDOM CONTROLLER AND MODEL PREDICTIVE CONTROLLER FOR THREE TANK INTERACTING SYSTEM
PERFORMANCE ANALYSIS OF TWO-DEGREE-OF-FREEDOM CONTROLLER AND MODEL PREDICTIVE CONTROLLER FOR THREE TANK INTERACTING SYSTEM K.Senthilkumar 1, Dr. D.Angeline Vijula 2, P.Venkadesan 3 1 PG Scholar, Department
More informationFUZZY LOGIC CONTROL of SRM 1 KIRAN SRIVASTAVA, 2 B.K.SINGH 1 RajKumar Goel Institute of Technology, Ghaziabad 2 B.T.K.I.T.
FUZZY LOGIC CONTROL of SRM 1 KIRAN SRIVASTAVA, 2 B.K.SINGH 1 RajKumar Goel Institute of Technology, Ghaziabad 2 B.T.K.I.T., Dwarhat E-mail: 1 2001.kiran@gmail.com,, 2 bksapkec@yahoo.com ABSTRACT The fuzzy
More informationSimulation based Modeling and Implementation of Adaptive Control Technique for Non Linear Process Tank
Simulation based Modeling and Implementation of Adaptive Control Technique for Non Linear Process Tank P.Aravind PG Scholar, Department of Control and Instrumentation Engineering, JJ College of Engineering
More informationFuzzy Control. PI vs. Fuzzy PI-Control. Olaf Wolkenhauer. Control Systems Centre UMIST.
Fuzzy Control PI vs. Fuzzy PI-Control Olaf Wolkenhauer Control Systems Centre UMIST o.wolkenhauer@umist.ac.uk www.csc.umist.ac.uk/people/wolkenhauer.htm 2 Contents Learning Objectives 4 2 Feedback Control
More informationSimulation of Quadruple Tank Process for Liquid Level Control
Simulation of Quadruple Tank Process for Liquid Level Control Ritika Thusoo 1, Sakshi Bangia 2 1 M.Tech Student, Electronics Engg, Department, YMCA University of Science and Technology, Faridabad 2 Assistant
More informationSecondary Frequency Control of Microgrids In Islanded Operation Mode and Its Optimum Regulation Based on the Particle Swarm Optimization Algorithm
International Academic Institute for Science and Technology International Academic Journal of Science and Engineering Vol. 3, No. 1, 2016, pp. 159-169. ISSN 2454-3896 International Academic Journal of
More informationControl Of Heat Exchanger Using Internal Model Controller
IOSR Journal of Engineering (IOSRJEN) e-issn: 2250-3021, p-issn: 2278-8719 Vol. 3, Issue 7 (July. 2013), V1 PP 09-15 Control Of Heat Exchanger Using Internal Model Controller K.Rajalakshmi $1, Ms.V.Mangaiyarkarasi
More informationDESIGN OF POWER SYSTEM STABILIZER USING FUZZY BASED SLIDING MODE CONTROL TECHNIQUE
DESIGN OF POWER SYSTEM STABILIZER USING FUZZY BASED SLIDING MODE CONTROL TECHNIQUE LATHA.R Department of Instrumentation and Control Systems Engineering, PSG College of Technology, Coimbatore, 641004,
More informationA NEW STRUCTURE FOR THE FUZZY LOGIC CONTROL IN DC TO DC CONVERTERS
A NEW STRUCTURE FOR THE FUZZY LOGIC CONTROL IN DC TO DC CONVERTERS JENICA ILEANA CORCAU Division Avionics University of Craiova, Faculty of Electrotechnics Blv. Decebal, nr. 07, Craiova, Dolj ROMANIA ELEONOR
More informationFuzzy Based Robust Controller Design for Robotic Two-Link Manipulator
Abstract Fuzzy Based Robust Controller Design for Robotic Two-Link Manipulator N. Selvaganesan 1 Prabhu Jude Rajendran 2 S.Renganathan 3 1 Department of Instrumentation Engineering, Madras Institute of
More informationLQR CONTROL OF LIQUID LEVEL AND TEMPERATURE CONTROL FOR COUPLED-TANK SYSTEM
ISSN 454-83 Proceedings of 27 International Conference on Hydraulics and Pneumatics - HERVEX November 8-, Băile Govora, Romania LQR CONTROL OF LIQUID LEVEL AND TEMPERATURE CONTROL FOR COUPLED-TANK SYSTEM
More informationApplication of GA and PSO Tuned Fuzzy Controller for AGC of Three Area Thermal- Thermal-Hydro Power System
International Journal of Computer Theory and Engineering, Vol. 2, No. 2 April, 2 793-82 Application of GA and PSO Tuned Fuzzy Controller for AGC of Three Area Thermal- Thermal-Hydro Power System S. K.
More informationFUZZY LOGIC CONTROL Vs. CONVENTIONAL PID CONTROL OF AN INVERTED PENDULUM ROBOT
http:// FUZZY LOGIC CONTROL Vs. CONVENTIONAL PID CONTROL OF AN INVERTED PENDULUM ROBOT 1 Ms.Mukesh Beniwal, 2 Mr. Davender Kumar 1 M.Tech Student, 2 Asst.Prof, Department of Electronics and Communication
More informationThe Performance Analysis Of Four Tank System For Conventional Controller And Hybrid Controller
International Research Journal of Engineering and Technology (IRJET) e-issn: 2395-56 Volume: 3 Issue: 6 June-216 www.irjet.net p-issn: 2395-72 The Performance Analysis Of Four Tank System For Conventional
More informationDesign and Comparative Analysis of Controller for Non Linear Tank System
Design and Comparative Analysis of for Non Linear Tank System Janaki.M 1, Soniya.V 2, Arunkumar.E 3 12 Assistant professor, Department of EIE, Karpagam College of Engineering, Coimbatore, India 3 Associate
More informationFault Detection and Diagnosis for a Three-tank system using Structured Residual Approach
Fault Detection and Diagnosis for a Three-tank system using Structured Residual Approach A.Asokan and D.Sivakumar Department of Instrumentation Engineering, Faculty of Engineering & Technology Annamalai
More informationINTELLIGENT CONTROL OF DYNAMIC SYSTEMS USING TYPE-2 FUZZY LOGIC AND STABILITY ISSUES
International Mathematical Forum, 1, 2006, no. 28, 1371-1382 INTELLIGENT CONTROL OF DYNAMIC SYSTEMS USING TYPE-2 FUZZY LOGIC AND STABILITY ISSUES Oscar Castillo, Nohé Cázarez, and Dario Rico Instituto
More informationLecture 06. (Fuzzy Inference System)
Lecture 06 Fuzzy Rule-based System (Fuzzy Inference System) Fuzzy Inference System vfuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. Fuzzy Inference
More informationModel Based Fault Detection and Diagnosis Using Structured Residual Approach in a Multi-Input Multi-Output System
SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol. 4, No. 2, November 2007, 133-145 Model Based Fault Detection and Diagnosis Using Structured Residual Approach in a Multi-Input Multi-Output System A. Asokan
More informationInter-Ing 2005 INTERDISCIPLINARITY IN ENGINEERING SCIENTIFIC CONFERENCE WITH INTERNATIONAL PARTICIPATION, TG. MUREŞ ROMÂNIA, NOVEMBER 2005.
Inter-Ing 5 INTERDISCIPLINARITY IN ENGINEERING SCIENTIFIC CONFERENCE WITH INTERNATIONAL PARTICIPATION, TG. MUREŞ ROMÂNIA, 1-11 NOVEMBER 5. FUZZY CONTROL FOR A MAGNETIC LEVITATION SYSTEM. MODELING AND SIMULATION
More informationCHAPTER V TYPE 2 FUZZY LOGIC CONTROLLERS
CHAPTER V TYPE 2 FUZZY LOGIC CONTROLLERS In the last chapter fuzzy logic controller and ABC based fuzzy controller are implemented for nonlinear model of Inverted Pendulum. Fuzzy logic deals with imprecision,
More informationFUZZY CONTROL OF CHAOS
FUZZY CONTROL OF CHAOS OSCAR CALVO, CICpBA, L.E.I.C.I., Departamento de Electrotecnia, Facultad de Ingeniería, Universidad Nacional de La Plata, 1900 La Plata, Argentina JULYAN H. E. CARTWRIGHT, Departament
More informationDesign of Fuzzy PD-Controlled Overhead Crane System with Anti-Swing Compensation
Engineering, 2011, 3, 755-762 doi:10.4236/eng.2011.37091 Published Online July 2011 (http://www.scirp.org/journal/eng) Design of Fuzzy PD-Controlled Overhead Crane System with Anti-Swing Compensation Abstract
More informationControl Solutions in Mechatronics Systems
FACTA UNIVERSITATIS (NIŠ) SER.: ELEC. ENERG. vol. 18, no. 3, December 2005, 379-394 Control Solutions in Mechatronics Systems Dedicated to Professor Milić Stojić on the occasion of his 65th birthday Radu-Emil
More informationHandling Uncertainty using FUZZY LOGIC
Handling Uncertainty using FUZZY LOGIC Fuzzy Set Theory Conventional (Boolean) Set Theory: 38 C 40.1 C 41.4 C 38.7 C 39.3 C 37.2 C 42 C Strong Fever 38 C Fuzzy Set Theory: 38.7 C 40.1 C 41.4 C More-or-Less
More informationProcess Control Exercise 2
Process Control Exercise 2 1 Distillation Case Study Distillation is a method of separating liquid mixtures by means of partial evaporation. The volatility α of a compound determines how enriched the liquid
More informationA Boiler-Turbine System Control Using A Fuzzy Auto-Regressive Moving Average (FARMA) Model
142 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 1, MARCH 2003 A Boiler-Turbine System Control Using A Fuzzy Auto-Regressive Moving Average (FARMA) Model Un-Chul Moon and Kwang Y. Lee, Fellow,
More informationInternational Journal of Emerging Technology and Advanced Engineering Website: (ISSN , Volume 2, Issue 5, May 2012)
FUZZY SPEED CONTROLLER DESIGN OF THREE PHASE INDUCTION MOTOR Divya Rai 1,Swati Sharma 2, Vijay Bhuria 3 1,2 P.G.Student, 3 Assistant Professor Department of Electrical Engineering, Madhav institute of
More informationFUZZY-ARITHMETIC-BASED LYAPUNOV FUNCTION FOR DESIGN OF FUZZY CONTROLLERS Changjiu Zhou
Copyright IFAC 5th Triennial World Congress, Barcelona, Spain FUZZY-ARITHMTIC-BASD LYAPUNOV FUNCTION FOR DSIGN OF FUZZY CONTROLLRS Changjiu Zhou School of lectrical and lectronic ngineering Singapore Polytechnic,
More informationMulti-Input Multi-output (MIMO) Processes CBE495 LECTURE III CONTROL OF MULTI INPUT MULTI OUTPUT PROCESSES. Professor Dae Ryook Yang
Multi-Input Multi-output (MIMO) Processes CBE495 LECTURE III CONTROL OF MULTI INPUT MULTI OUTPUT PROCESSES Professor Dae Ryook Yang Fall 2013 Dept. of Chemical and Biological Engineering Korea University
More informationModels for Inexact Reasoning. Fuzzy Logic Lesson 8 Fuzzy Controllers. Master in Computational Logic Department of Artificial Intelligence
Models for Inexact Reasoning Fuzzy Logic Lesson 8 Fuzzy Controllers Master in Computational Logic Department of Artificial Intelligence Fuzzy Controllers Fuzzy Controllers are special expert systems KB
More informationCHAPTER 2 PROCESS CHARACTERISTICS AND CONTROL STRATEGIES
14 CHAPTER 2 PROCESS CHARACTERISTICS AND CONTROL STRATEGIES In this chapter, the process characteristics and the methods to obtain them using simple process information is presented. Different types of
More informationDesign Artificial Nonlinear Controller Based on Computed Torque like Controller with Tunable Gain
World Applied Sciences Journal 14 (9): 1306-1312, 2011 ISSN 1818-4952 IDOSI Publications, 2011 Design Artificial Nonlinear Controller Based on Computed Torque like Controller with Tunable Gain Samira Soltani
More informationMODELING AND SIMULATION OF ROTOR FLUX OBSERVER BASED INDIRECT VECTOR CONTROL OF INDUCTION MOTOR DRIVE USING FUZZY LOGIC CONTROL
MODELING AND SIMULATION OF ROTOR FLUX OBSERVER BASED INDIRECT VECTOR CONTROL OF INDUCTION MOTOR DRIVE USING FUZZY LOGIC CONTROL B. MOULI CHANDRA 1 & S.TARA KALYANI 2 1 Electrical and Electronics Department,
More informationFuzzy controller for adjustment of liquid level in the tank
Annals of the University of Craiova, Mathematics and Computer Science Series Volume 38(4), 2011, Pages 33 43 ISSN: 1223-6934, Online 2246-9958 Fuzzy controller for adjustment of liquid level in the tank
More informationAuto-tuning of PID controller based on fuzzy logic
Computer Applications in Electrical Engineering Auto-tuning of PID controller based on fuzzy logic Łukasz Niewiara, Krzysztof Zawirski Poznań University of Technology 60-965 Poznań, ul. Piotrowo 3a, e-mail:
More information3.1 Overview 3.2 Process and control-loop interactions
3. Multivariable 3.1 Overview 3.2 and control-loop interactions 3.2.1 Interaction analysis 3.2.2 Closed-loop stability 3.3 Decoupling control 3.3.1 Basic design principle 3.3.2 Complete decoupling 3.3.3
More informationAN INTELLIGENT HYBRID FUZZY PID CONTROLLER
AN INTELLIGENT CONTROLLER Isin Erenoglu Ibrahim Eksin Engin Yesil Mujde Guzelkaya Istanbul Technical University, Faculty of Electrical and Electronics Engineering, Control Engineering Department, Maslak,
More informationProcess Unit Control System Design
Process Unit Control System Design 1. Introduction 2. Influence of process design 3. Control degrees of freedom 4. Selection of control system variables 5. Process safety Introduction Control system requirements»
More informationME 534. Mechanical Engineering University of Gaziantep. Dr. A. Tolga Bozdana Assistant Professor
ME 534 Intelligent Manufacturing Systems Chp 4 Fuzzy Logic Mechanical Engineering University of Gaziantep Dr. A. Tolga Bozdana Assistant Professor Motivation and Definition Fuzzy Logic was initiated by
More informationAnalysis of Modelling Methods of Quadruple Tank System
ISSN (Print) : 232 3765 (An ISO 3297: 27 Certified Organization) Vol. 3, Issue 8, August 214 Analysis of Modelling Methods of Quadruple Tank System Jayaprakash J 1, SenthilRajan T 2, Harish Babu T 3 1,
More informationQ-V droop control using fuzzy logic and reciprocal characteristic
International Journal of Smart Grid and Clean Energy Q-V droop control using fuzzy logic and reciprocal characteristic Lu Wang a*, Yanting Hu a, Zhe Chen b a School of Engineering and Applied Physics,
More informationFUZZY CONTROL OF CHAOS
International Journal of Bifurcation and Chaos, Vol. 8, No. 8 (1998) 1743 1747 c World Scientific Publishing Company FUZZY CONTROL OF CHAOS OSCAR CALVO CICpBA, L.E.I.C.I., Departamento de Electrotecnia,
More informationProceedings of the Pakistan Academy of Sciences 48 (2): 65 77, 2011
Proceedings of the Pakistan Academy of Sciences 48 (2): 65 77, 2011 Copyright Pakistan Academy of Sciences ISSN: 0377-2969 Pakistan Academy of Sciences Original Article Design of Multi-Input Multi-Output
More informationFuzzy PID Control System In Industrial Environment
Information Technology and Mechatronics Engineering Conference (ITOEC 2015) Fuzzy I Control System In Industrial Environment Hong HE1,a*, Yu LI1, Zhi-Hong ZHANG1,2, Xiaojun XU1 1 Tianjin Key Laboratory
More informationFuzzy Systems. Fuzzy Control
Fuzzy Systems Fuzzy Control Prof. Dr. Rudolf Kruse Christoph Doell {kruse,doell}@ovgu.de Otto-von-Guericke University of Magdeburg Faculty of Computer Science Institute for Intelligent Cooperating Systems
More informationSPEED CONTROL OF DC MOTOR ON LOAD USING FUZZY LOGIC CONTROLLER (A CASE STUDY OF EMERGENCY LUBE OIL PUMP MOTOR OF H25 HITACHI TURBINE GENERATOR)
Nigerian Journal of Technology (NIJOTECH) Vol. 36, No. 3, July 2017, pp. 867 875 Copyright Faculty of Engineering, University of Nigeria, Nsukka, Print ISSN: 0331-8443, Electronic ISSN: 2467-8821 www.nijotech.com
More informationOUTLINE. Introduction History and basic concepts. Fuzzy sets and fuzzy logic. Fuzzy clustering. Fuzzy inference. Fuzzy systems. Application examples
OUTLINE Introduction History and basic concepts Fuzzy sets and fuzzy logic Fuzzy clustering Fuzzy inference Fuzzy systems Application examples "So far as the laws of mathematics refer to reality, they
More informationHYDRAULIC LINEAR ACTUATOR VELOCITY CONTROL USING A FEEDFORWARD-PLUS-PID CONTROL
HYDRAULIC LINEAR ACTUATOR VELOCITY CONTROL UING A FEEDFORWARD-PLU-PID CONTROL Qin Zhang Department of Agricultural Engineering University of Illinois at Urbana-Champaign, Urbana, IL 68 ABTRACT: A practical
More informationDESIGN OF A HIERARCHICAL FUZZY LOGIC PSS FOR A MULTI-MACHINE POWER SYSTEM
Proceedings of the 5th Mediterranean Conference on Control & Automation, July 27-29, 27, Athens - Greece T26-6 DESIGN OF A HIERARCHICAL FUY LOGIC PSS FOR A MULTI-MACHINE POWER SYSTEM T. Hussein, A. L.
More informationFuzzy control systems. Miklós Gerzson
Fuzzy control systems Miklós Gerzson 2016.11.24. 1 Introduction The notion of fuzziness: type of car the determination is unambiguous speed of car can be measured, but the judgment is not unambiguous:
More informationFuzzy Controller of Switched Reluctance Motor
124 ACTA ELECTROTEHNICA Fuzzy Controller of Switched Reluctance Motor Ahmed TAHOUR, Hamza ABID, Abdel Ghani AISSAOUI and Mohamed ABID Abstract- Fuzzy logic or fuzzy set theory is recently getting increasing
More information