Varsha Bhambhani

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In many industrial processes, the first order plus time delay (FOPDT) is still being widely used. FOPDT systems are also called " KLT systems (gain, delay, and time constant). " Considering uncertainties in the time delay, this paper attempts to answer this research question: " Will a fractional order controller help and do better? " In this paper, we first(More)
— This paper presents a strategy to tune a fractional order integral and derivative controller satisfying gain and phase margins. The closed-loop system designed has a feature of robustness to gain variations with step responses exhibiting a nearly iso-damping property. This paper aims to apply the tuning procedure proposed to temperature control at(More)
— Based on our previously developed tuning procedure for fractional order proportional integral controller (FO-PI), we present in this paper an extensive comparative experimental study on coupled-tank liquid level controls. Our experimental study consists of four steps, they are mathematical modeling of the plant, identification of plant parameters,(More)
Random delays have serious effects in networked control systems, which deteriorate the performance and may even cause instability of the system. Hence a controller which can make the plant stable at large values of delay is always desirable in NCS systems. Our previous work on OFOPI controller showed that fractional order PI controllers have larger jitter(More)
This thesis developed a new practical tuning method for fractional order proportional and integral controllers (FO-PI / PI α) for varying time-delay systems like networked control systems (NCS), sensor networks, etc. Based on previously proposed FO-PI controller tuning rules using fractional Ms constrained integral gain optimization (F-MIGO), simultaneous(More)
Purpose – The objective of this work is to develop a methodology for the design of cellular neural networks with interconnection topologies optimized and suitable for spatially distributed implementation. Design/Methodology/Approach – We perform combinatorial optimization on the neural network's topology to obtain a sparser network, in which the links(More)
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