Design of fuzzy I-PD controller for level control of spherical tank system

Vinodhini, S.; Prasad, S. J. Suji; Meenakshipriya, B.; Venkatesan, B.

doi:10.1109/iciiecs.2017.8276046

Cited by 3 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of the responses have a better performance than other controllers with fewer errors and overshoot. S.J.Suji Prasad et al have proposed the design of a Fuzzy PI controller for level control of a single tank spherical system [9]. The simulation of the responses is compared with the PID controller, and other controllers like I-PD, Fuzzy PID, Fuzzy I-PD.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Intelligent Controller for Non-Linear Spherical Tank System

Singh¹

2021

ITII

View full text Add to dashboard Cite

Spherical tanks are used to store fluids in many industries such as petrochemical, effluent treatment, and aerospace. Spherical tanks are used as they are highly resistant to internal pressure making them suitable for storing high-pressure materials due to their large volume, small weight, and strong load-bearing capacity. The spherical tanks have the lowest possible surface area to volume ratio. These tanks are preferred due to their capability of balancing pressure in and out of the tank and their ability to minimize the amount of heat that gets inside the tank wall. It is cost-effective when compared to other tanks. But, controlling the water level in the spherical tank is difficult and a highly challenging one. In this article, the aim is to stabilize the level of the spherical tank by using the PID and FUZZY logic controllers. By controlling nonlinear dynamic behavior, uncertainty, time-varying parameters, frequency disturbances and dead time, the stability of the tank is achieved. The mathematical modelling of the spherical system is obtained using first principles design and the stability of the model is analyzed using various techniques. Then, the simulation is done using MATLAB and the responses are obtained and compared for PID and FUZZY logic. Based on these comparisons made on the performance of the PID and FUZZY logic controllers, the results are concluded

show abstract

Section: Introductionmentioning

confidence: 99%