International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
PSO based NNIMC for a Conical Tank Level Process Geethanjali Karuppaiyan1 and .S.Srinivasan2
Research Scholar, Department of Electronics and Instrumentation Engineering, Annamalai University, Annamalai nagar, Tamil nadu, India.-608002. 2 Associate Professor, Department of Electronics and Instrumentation Engineering, Annamalai University, Annamalai nagar, Tamil nadu, India.-608002. ---------------------------------------------------------------------***--------------------------------------------------------------------1
Abstract - The control of conical tank level process is
complex because of its dynamics are nonlinear, time varying with change in gain of several orders. Hence in this work, modeling and control of conical tank level process is considered. First, the mathematical modeling of conical tank level process is developed and simulated. The entire operating region is divided into three linear zones to design a conventional PI controller. A small signal transfer functions are obtained for various operating regions by giving positive and negative step change in inflow rate. A conventional PI controller is designed using average of transfer function based on Z-N tuning method for each region. Simulation studies are carried out for setpoint tracking. However, conventional controller will not give satisfactory results for varying operating points due to non linearity and time varying nature of conical tank level process. In this work, PSO based Neural Network Internal Model Controller (NNIMC) is designed and its outputs are compared with those of conventional PI controller and NNIMC through simulation studies for setpoint tracking.
Key Words: Non-linear, NNIMC and PSO 1.INTRODUCTION
Liquid level control systems mainly control the manipulated parameter of liquid level, which in industry have a wide range of applications in various fields. In the industrial production process, there are many places need to control the liquid level, and make the liquid level maintain accurately for a given value. The traditional method is to use classical PID method. However, the practical application of the output is uncertain, in order to input well to follow the changes of output, then we need a continuously detect the number in time, to realize the liquid precise control. To implement a PID controller, three parameters (the proportional gain, Kp; the integral gain, Ki; the derivative gain, Kd) must be determined carefully. Many approaches have been developed to determine PID controller parameters for single input single output (SISO) systems. Among the well-known approaches is the Ziegler-Nichols (Z-N) method and the Cohen- Coon method. Conical tanks are mostly used in various process industries, such as metallurgical industries, food processing industries, concrete mixing industries and wastewater treatment industries. A conical tank is basically a nonlinear process due to the change in the area of cross section and the level system with change in shape. Conventional controllers are commonly used in process industries as they are simple, robust and familiar to the field operator. Real time systems Š 2017, IRJET
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are not precisely linear but may be represented as linearized models around a nominal operating point. The controller parameters tuned at that operating point may not reflect the real-time system characteristics due to variations in the process parameters. The variations in the process parameters can be overcome by continuous adjustment of the controller parameter’s using intelligent techniques like Artificial Neural Network (ANN) Bhuvaneswari et.al., (2008). Conical tank find wide applications in process industries. Conical tank with gravity discharge flows are used widely as an in expensive to feed slurries and liquids with solid particles to unit operations. Control of such process is very handled by P.Aravind et.al., [53] and real time system designs are analyzed. An implementation of the PI controller is done by direct synthesis method and skogestad method. The PI parameters obtained by process reaction curve method gives better result than the other techniques. D.Mercy and S.M.Girirajkumar (2013) analyzed the tuning of controllers for conical tank level process. Authors proposed tuning of PID control strategy using Z-N method and Genetic Algorithm technique. Comparison is done with other conventional techniques, the GA provide better results in terms of high stability, robust and reliable. Giriraj Kumar et.al., (2008) discusses the Particle swarm optimization Technique based design of PI controller for a real-time conical tank level process In this work, PSO based NNIMC, NNIMC and PI controllers are designed and implemented for a non-linear conical tank level process. Section 2 describes the mathematical modeling and process description of conical tank level process. Section 3 deals with the design and implementation of PI controller.
2. MATHEMATICAL MODELING AND PROCESS DESCRIPTION A mathematical model is a description of a process using mathematical concepts. The process of developing a mathematical model is termed as mathematical modeling. Mathematical modeling is used to explain the identified system and to study the effects of different components, and to make predictions about the process behavior. Mathematical models ca n take many forms, including but not limited to dynamical systems, statistical models, differential equations, etc. In this paper the proposed system includes the conical tank process whose area is variable throughout the height. The mathematical model of the conical tank is determined by the following assumptions. ďƒ˜ Level as the control variable ISO 9001:2008 Certified Journal
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