Looking for a Tutor Near You?

Post Learning Requirement »
x

Choose Country Code

x

Direction

x

Ask a Question

x

x
x
For better view click here
x
Hire a Tutor
DISCOVER

Fopid Controller Dynamic System

Loading...

Published in: Instrumentation
780 Views

Brief Notes On "Design Of Fopid Controller For a Dynamic System"

Sneha / Chennai

23 years of teaching experience

Qualification: BDS (VELS University, Chennai - 2018)

Teaches: All Subjects, Biology, Botany, Zoology, Science

Contact this Tutor
  1. DESIGN OF FOPID CONTROLLER FOR A DYNAMIC SYSTEM Sudhakar.G, Nethaji .G.S, Praveen .N Mr. K. Anbumanni, Assistant Professor, Department of Electronics and Instrumentation Engineering, Sri Sairam Engineering College, Chennai. Abstract: Dynamic SISO system is modelled using system identification method and the model is controlled by using PID, FOPID controller and it is tuned by using optimization technique like PSO and the results were compared based on its performance indices. In many industrial processes, heat exchanger is one of the simplest and important unit for the transfer of thermal energy. The shell and tube heat exchanger is the most commonly used system. The main purpose of exchanger is to maintain specific temperature conditions, which is achieved by controlling the exit temperature of one of the fluids (mainly hot fluid) in response to variations of the operating conditions. The temperature control mechanism otheat exchangeris nonlinear, and thereby causes a time variation and time delay in the system. Under these situations, nonlinear control strategies can provide significant improvements over PID control strategy. Control of temperature using PID controllers, compared to is more effective other methods, and economical. The heat exchangers need torespond to highly nonlinear features and work well under different operating points. In order to achieve a wide range of high accurate temperature, FOPID control methods were implemented. The main design is to get mathematical model of heat exchanger according to different error 'e' and error change 'ec' and the model is simulated by using FOPID controller based on optimization technique. Keywords: Heat exchanger, PID controller, FOPID controller, Optimization technique. 1. Introduction: Heat exchanger is one of the most widely used industrial equipment which is used to transfer heat between two mediums, among them one should be a fluid medium. It thus finds applications in various chemical, petrochemical, and manufacturing applications. Heat exchanger is generally available in various types. But the commonly used is shell and tube type. Shell and tube type is very cheap and economical, and their low efficiency coupled with large space occupied in large scales, makes this type very widely used. The next generation controllers and MPC are relatively slow mechanism for rapidly changing processes, because even for a SISO system they are unable to adapt to the changes [l]. Fuzzy controller mechanism requiresa expertise knowledge of the system all the time and it has no standard tuning criteria. It is quite difficult to stabilise the fuzzy system and for a new input a knowledge of the past is essential [2]. Artificial neural networks provide accuracy only for MSE and SSE performances but not for ISE performance criteria [3]. Cross flow heat exchanger are prone to wear and tear, not economically feasible and seeks professional advice every time under abnormal conditions [4]. Thus, this paper majorly focusses on modelling a dynamic system that is, a mechanical system using a single input to control the single output of the system using PID and FOPID control mechanism. But FOPID control mechanism proves to be more advantageous as it does not matter whether the mathematical model is in transfer function or state space, whereas for PID control mechanism it requires the mathematical model to be in transfer function only. In FOPID mechanism, we can model a higher order system with a lower order model and also this control mechanism is more precise and accurate when compared to PID control mechanism. This system is modelled in MATLAB using black box system identification software testing as it examines the functionality of an application without any peering into its internal structure or working. 2. System Description: 2.1 Shell and tube heat exchanger A shell and tube heat exchanger is a class of heat exchanger designs. It is the most common type of heat exchanger in oil refineries and other large
  2. chemical processes, and is suited for higher- pressure applications. As its name implies, this type of heat exchanger consists of a shell (a large pressure vessel) with a bundle of tubes inside it. One fluid runs through the tubes, and another fluid flows over the tubes (through the shell) to transfer heat between the two fluids. The set of tubes is called a tube bundle, and may be composed of several types of tubes: plain, longitudinally finned, etc. Geothermal Fluid In Tube Plate Process Tube Nest Fluid Out End Head Geotherma Fluid Out Baffles Shell Process Fluid In a power-law tail. The effect is that the effects of all time are computed for each iteration of the control algorithm. This creates a 'distribution of time constants,' the upshot of which is there is no particular time constant, or resonance frequency, for the system. Fractional-order control shows promise in many controlled environments that suffer from the classical problems of overshoot and resonance, as well as time diffuse applications such as thermal dissipation and chemical mixing. 2.3 RTD Resistancethermometers, also called resistance temperature detectors (RTDs), are sensors used to measure temperature. Many R TD elements consist of a length of fine wire wrapped around a ceramic or glass core but other constructions are also used. The RTD wire is a pure material, typically platinum, nickel, or copper. The material has an accurate resistance/temperature relationship which is used to provide an indication of temperature. The advantages of platinum resistance thermometers include high accuracy, low drift, wide operating range and suitability for precision applications. 2.4 Control Valve A control valve is a valve used to control fluid flow by varying the size of the flow passage as directed by a signal from a controller. This enables the direct control of flow rate and the consequential Fig. 1 Shell and tube heat exchanger There can be many variations on the shell and tube design. Typically, the ends of each tube are connected to plenums (sometimes called water boxes) through holes in tube sheets. The tubes may be straight or bent in the shape of a U, called U- tubes. Most shell-and-tube heat exchangers are 1, 2, or 4 pass designs on the tube side. This refers to the number of times the fluid in the tubes passes through the fluid in the shell. In a single pass heat exchanger, the fluid goes in one end of each tube and out the other. Surface condensers in power plants are often I-pass straight-tube heat exchangers (see surface condenser for diagram). Two and four pass designs are common because the fluid can enter and exit on the same side. This makes construction much simpler. There are often baffles directing flow through the shell side so the fluid does not take a short cut through the shell side leaving ineffective low flow volumes. These are generally attached to the tube bundle rather than the shell in order that the bundle is still removable for maintenance. 2.2 FOPID Controller The fundamental advantage of fractional order control is that the fractional-order integrator weights history using a function that decays with of control process as pressure, temperature, quantities such and liquid level. In automatic control terminology a control valve is termed a "final control element". 2.5 DAQ Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing. The components of data acquisition systems include sensors, to convert physical parameters to electrical signals; signal conditioning circuitry, to convert sensor signals into a form that can be converted to digital values and analog-to- digital converters, to convert conditioned sensor signals to digital values. 2.6 Working Principle
  3. Steam is the input to the shell and tube heat exchanger, which heats the flowing fluid inside the exchanger and hot water, is obtained at the outlet. The temperature of this hot water is measured using a resistance thermometer detector. If sufficient temperature is not attained or more temperature than the required temperature is being attained, accordingly the cold water inlet control valve is turned on or off by the controller. Controller Cold water inlet RTD Steam water inlet Hot water outlet Condensate Fig. 2 Block Diagram Any temperature inside the heat exchanger can be obtained by appropriately measuring the temperature of the outlet and then controlling the cold water inlet valve using a control mechanism. 3. Software Description 3.1 MATLAB The system identification toolkit used is MATLAB (Matrix Laboratory). MATLAB is a multi- paradigm numerical computing environment. A proprietary programming language developed by Math Works, allows matrix manipulations, of functions and data, MATLAB plotting implementation of algorithms and creation of user interfaces. MATLAB users come from various backgrounds such as engineering, science and economics. 3.2 System Identification System identification is a methodology for building mathematical models of dynamic systems using measurements of the system's input and output signals.The process of system identification helps in the following: Measure the input and output signals from your system in time or frequency domain. Select a model structure. Apply an estimation method to estimate value for the adjustable parameters in the candidate model structure. Evaluate the estimated model to see if the model is adequate for your application needs. 3.2 Black Box Modeliing Black-box modelling is useful when your primary interest is in fitting the data regardless of a particular mathematical structure of the model. The toolbox provides several linear and nonlinear which have black-box model structures, traditionally been useful for representing dynamic systems. These model structures vary in complexity depending on the flexibility you need to account for the dynamics and noise in your system. You can choose one of these structures and compute its parameters to fit the measured response data. Black-box modelling is usually a trial-and-error process, where you estimate the parameters of various structures and compare the results. Typically, you start with the simple linear model structure and progress to more complex structures. You might also choose a model structure because you are more familiar with this structure or because you have specific application needs. The simplest linear black-box structures require the fewest options to configure: Transfer function, with a given number of poles and zeros. Linear ARX model, which is the simplest input-output polynomial model. State-space model, which you can estimate by specifying the number of model states. Mathematical model is obtained by system identification technique. The transfer function attained for this design is given by, G(s) = 0.1+1 s3+4. I s2+3.7 I
  4. 105+1 Fig.3 FOPID controllerTuning Diagram To obtain the tuning response for FOPID controller tuning diagram is drawn in the Simulink with input as step unit by giving appropriate disturbance and delay parameters. 4. Results and Conclusion Thus, the design is obtained both by tuning PID and FOPID controllers for different kp, kl and kd values. Fig.4 Tuning response of PID controller PID controller is tuned by taking kp=0.5, ki=0.008 and Iq=10.5as the parametric constant values. Fig. 5 Tuningresponse of FOPID controller FOPID controller is tuned by taking kp=0.4, 0.01and kd=4. the parametric constant values. Therefore, from the obtained graphs of tuning for PID and FOPID controller we can conclude that the response obtained when using FOPID controller has less rise time and settling time. Thereby, FOPID is said to more sensitive to changes than PID and also very precise in measurements. 5. Future Work For more accurate results we can extend the FOPID controller to OFOPID that is, Optimised FOP ID controller can be used to more optimised results and thereby reducing the error very much effectively. 6. References [l] V. Bagyaveereswara, Tushar D. Mathur a, Sukrit Gupta a,P.Arulmozhivarman b, Performance comparison of next generation controller and MPC in real time for a SISO process with low cost DAQ unit", Alexandria Engineering Journal ,V01.no. 55, pp.2515-2524, 2016. [2] Amit kumar, K.K Garg," Comparison of Ziegler-Nichols, Cohen-Coon and Fuzzy Logic Controllers for Heat Exchanger Model' International Journal of Science, Engineering and Technology Research (IJSETR), Volume 4, Issue 6, June 2015. JamaliSoufi Amlashil, Amin [3] Nader Shahsavari2, Alireza Vahidifar3, Mehrzad Nasirian4, "Nonlinear System Identification of Laboratory Heat Exchanger Using Artificial Neural Network Model", International Journal of Electrical and Computer Engineering (IJECE) Vol.3, No. l, pp. 118-128 , February 2013. [4] Guillaume Mercere, Member, IEEE, HalldorPalsson, and Thierry Poinot, "Continuous- Time Linear Parameter-Varying Identification of a Cross Flow Heat Exchanger: A Local Approach' IEEE Transactions On Control Systems Technology, Vol. 19, No. 1,pp.64-76 January 2011.
  5. N/A

Need a Tutor or Coaching Class?

Post an enquiry and get instant responses from qualified and experienced tutors.

Post Requirement

Related Study Notes

Upload Study Notes

If you have your own Study Notes which you think can benefit others, please upload on LearnPick. For each approved Study Note you will get 100 Credit Points and 100 Activity Score which will increase your profile visibility.

Upload Now
Home > Study Notes > Fopid Controller Dynamic System