Tailor-made small simulator for a drum boiler control based on linear techniques

Seisdedos, Luís Vázquez

doi:10.1109/etfa.2011.6059211

Cited by 6 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results of Fig. 7 and Table II show that the speed performance of our intelligent controllers are better than controllers in [17], [26]. The main sensitive parameters in drum boiler are level and pressure.…”

Section: Simulation Resultsmentioning

confidence: 82%

“…In this paper, a fuzzy simulator designed to control pressure and level of drum boiler in [26] is proposed. The aim is to introduce intelligent approaches rather than classical solution with feedback and feedforward strategies [26].…”

Section: Introductionmentioning

confidence: 99%

“…The goal of this work is to improve the precision and speed performances of drum boiler classical control model [26]. Because fuzzy logic is good for decisional models and Genetic Algorithms are efficient search algorithms, geneticfuzzy controllers are designed.…”

Section: Introductionmentioning

confidence: 99%

“…The aim is to introduce intelligent approaches rather than classical solution with feedback and feedforward strategies [26]. Such strategies was inspired on the 160 MW-Sweden P16-G16 power plant [7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy algorithm control effectiveness on drum boiler simulated dynamics

Pelusi

Vázquez

Diaz

et al. 2013

2013 36th International Conference on Telecommunications and Signal Processing (TSP)

View full text Add to dashboard Cite

The optimal control of parameters in a system assumes an important role in industrial processes. Models based on boiler-turbine plant are proposed in various applications. The target of this paper is to apply intelligent techniques on a boiler simulator to improve the speed and precision of control. In order to release such task, genetic-fuzzy controllers able of tuning the time duration of specific boiler drum signals, are designed. The results show good precision and relevant speed of control system improving the control performances of classical control structures.

show abstract

Section: Simulation Resultsmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy algorithm control effectiveness on drum boiler simulated dynamics

Pelusi

Vázquez

Diaz

et al. 2013

2013 36th International Conference on Telecommunications and Signal Processing (TSP)

View full text Add to dashboard Cite

show abstract

“…A future challenge will be the application of the proposed skills on simulators for drum boiler (Prego and Seisdedos, 2011).…”

Section: Resultsmentioning

confidence: 99%

Optimal Control Algorithms for Second Order Systems

Pelusi¹

2013

Journal of Computer Science

View full text Add to dashboard Cite

Proportional Integral Derivative (PID) controllers are widely used in industrial processes for their simplicity and robustness. The main application problems are the tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Networks. The combination of these theories can give good results in terms of settling time, rise time and overshoot. In this study, suitable controllers able of improving timing performance of second order plants are proposed. The results show that the PID controller has good overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of settling time and a very good overshoot value. The neural-fuzzy controller gives the best timing parameters improving the control performances of the others two approaches. Further improvements are achieved designing a real-time optimization algorithm which works on a genetic-neuro-fuzzy controller.

show abstract