Measurement and Control of Process using Soft Computing

Nithya, S.; Gour, Abhay Singh; Sivakumaran, N.; Anantharaman, N.

doi:10.1080/10739140701850944

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…However, plants that have time delays cannot be controlled effectively by a simple PID controller. Fuzzy logic controllers outperform PID controllers because they have no integral accumulator to increase the error of the naturally delayed system [14].…”

Section: Testing Variabilitymentioning

confidence: 99%

“…The familiarity to PID controller, its known performance, and its relative implementation feasibility have obscured some of the clear benefits of FLC. Nevertheless, Nithya et al [14] shows that FLC exhibits an improved performance over PID controllers when dealing with first-order plus dead time systems; Gupta et al [11] remark that nonlinear characteristics make PID unsuitable while intelligent control can cope with control specifications; and Linder and Shafai [12] and Kalavathi and Reddy [6] say that FLC greatly outperforms PI/PID controllers. In some cases, FLC is becoming an immediate choice when dealing with nonlinear, time-delayed, and high-order systems [15]; parameter variance has also been properly tackled by FLC [7].…”

Section: Introductionmentioning

confidence: 99%

“…Gupta et al [11] show how FLC has been merged with artificial neural networks and traditional PID controllers to control BLDC motors. Lastly, Nithya et al [14] show tuning schemes, auto-tuning, and modelbased tuning FLC for first-order plus dead time models (FOPDT) in their cited literature; they propose a simple Mamdani FLC to deal with these kinds of systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advantages of Fuzzy Control While Dealing with Complex/ Unknown Model Dynamics: A Quadcopter Example

Ibarra¹,

Webb²

2016

New Applications of Artificial Intelligence

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Commonly, complex and uncertain plants cannot be faced through well-known linear approaches. Most of the time, complex controllers are needed to attain expected stability and robustness; however, they usually lack a simple design methodology and their actual implementation is difficult (if not impossible). Fuzzy logic control is an intelligent technique which, on its basis, allows the translation from logic statements to a nonlinear mapping. Although it has been proven to effectively deal with complex plants, many recent studies have moved away from the basic premise of linguistic interpretability. In this work, a simple fuzzy controller is designed in a clear way, privileging design easiness and logical consistency of linguistic operators. It is simulated together to a nonlinear model of a quadcopter with added actuators variability, so the robust operation of the controller is also proven. Uneven gain, bandwidth, and time-delay variations are applied among quadcopter's motors, so the simulations results enclose those characteristics which could be found in reality. As those variations can be related to actuators' performance, an analysis can be driven in terms of the features which are not commonly included in mathematical models like power electronics drives or electric machinery. These considerations may shorten the gap between simulation and actual implementation of the fuzzy controller. Briefly, this chapter presents a simple fuzzy controller which deals with a quadcopter plant as a first approach to intelligent control.

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Section: Testing Variabilitymentioning

confidence: 99%