Experimental Validation of a Multi Model PI Controller for a Non Linear Hybrid System in LabVIEW

Abstract

In this paper a real time Single Spherical Tank Liquid Level System (SSTLLS) has been chosen for investigation. This paper describes the design and development of a Multi IntroductionIn common terms, most of the industries have typical problems raised because of the dynamic non linear behavior of the storage tanks. It's only because of the inherent non linearity, most of the chemical process industries are in need of classical control techniques. Hydrometallurgical industries, food process industries, concrete mixing industries and waste water treatment industries have been actively using the spherical tanks as an integral process element. Due to its changing cross section and non linearity, a spherical tank provides a challenging problem for the level control.Liquid level control systems have always pulled the attention of industry for its very important manipulated parameter of level, which finds many applications in various fields. An accurate knowledge of an adequate model is often not easily available. An insufficiency in this aspect of model design can always lead to a failure in some non linear region with higher non linearity. The evidence that many researchers are working in the nonlinear models and their controlling strategies [1],[2], which in turn explain about the process dynamics around a larger operating region than the corresponding linear models have been gaining great popularity [3].The non linear models are obtained from first principle and further from the parameters which appear within such models that are obtained from the data of the process. However the conventional methods for developing such models are still in search. Once the model has been developed, the need for the controller design comes in to picture to maintain the process under steady state. Proportional Integral Derivative (PID) controller is the name that is widely heard as a part of the process control industry. Despite much advancement in control theory which has been recently seen, PID controllers are still extensively used in the process industry. Conventional PID controllers are simple, inexpensive in cost [4], easy to design and robust provided the system is linear. The PID controller operates with three parameters, which can be easily tuned by trial and error, or by using different tuning strategies and rules available in literature such as Zieglar Nichols [5], Zhuang and Atherton [6].These rules have their bases laid on open-loop stable first or second order plus dead time process models. There are many other methods and approaches which have periodically evolved to improvise the performance of PID tuning, The software and technology have been assisting the mankind to design and implement more sophisticated control algorithms. Despite all the effort, industries emphasize more on robust and transparent process control structure that uses simple controllers which makes PID controller the most widely implemented controller.

An Adaptive Liquid Level Controller Using Multi Sensor Data Fusion

“…In the last stage tuning of K P , K I , and K D is done for controlling of liquid level. The front panel of VI for proposed work is as shown in Figure 5 [36].…”

IMC Based Fractional Order Controller for Three Interacting Tank Process

“…petrochemical industries, pharmaceutical industries, food and beverages industries etc., level control problem of interacting tank arises. Thus the effective control of these variables shall prove very beneficent from economic point of view and also very much needed for the safety of equipments involved in the process [1,2].…”

Linearized PI controller for real-time delay dominant second order nonlinear systems

“…The performances ofthe controllers are shown to be better than the recently reported methods. An extensive experimentation and verification has been performed on different FOPTD models using different kinds of controllers [12,13,14,15,20] .A gain scheduled controller was also validated experimentally on a SOSPD system with experimental model approximation method [15]. Sliding Mode Control for Third Order systems has also been employed for the Cart-Inverted Pendulum System, which has many uncertainties [16].…”