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Comparison of Tuning Methods of PID Controllers of Two Conical Tank System of Interacting Type

Aasin Rukshna R, Bhuvaneswarri E., Madhubala V., Savithri G., Girirajkumar S.M.


The rapid increasing complexity of modern control systems has accentuated the idea of applying new modern approaches in order to solve design problems for various control engineering applications. This paper deals with the tuning of PID controllers for complex nonlinear process of two interacting Conical Tank Systems. Conical tanks play vital role in leaching extractions in pharmaceutical and chemical industries, as well as in food processing industry. It is a kind of multiple-use equipment, designed for boiling and extraction working procedure. Level control of a conical tank is a tedious process because of its non-linear characteristics hence proportional-Integral-Derivative (PID) control schemes have been widely used to overcome the issue by applying different controlling techniques. A PID controller is otherwise called as three term-control which has three constant parameters and it takes the present error, accumulation of past errors and prediction of future errors into account based upon the current rate of change of error, respectively. The proposed control strategy includes the tuning of PID controller using Ziegler-Nicholos (Z-N) method and intelligent techniques like Genetic Algorithm (GA). The scope of this paper is to compare the different conventional tuning methods for single input single output (SISO) systems such as Z-N, Tyreus-Luben (T-L), Internal Model Control (IMC) and GA to predict the most efficient controller. The factors for comparison include time domain specifications and performance of the controller. The performance of the controller for different tuning rules has been investigated in a MATLAB simulation Platform in which Genetic Algorithm outperformed well when compared to all other controllers in terms of time domain specification and performance index.

Keywords: Conical tank, interacting system, non-linear, PID, controller, performance and genetic algorithm

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