Genetic tuning of digital PID controllers

Porter, B.; Jones, A.H.

doi:10.1049/el:19920533

Cited by 135 publications

(42 citation statements)

References 5 publications

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“…It should be noted that genetic algorithms can also be readily used in connection with time-domain performance e.ce measures of control such as integral square error (lSE). Indeed, Porter and Jones (1992) have used this ISE performance measure in developing their methodology for the genetic tuning of digital PID controllers.…”

Section: Resultsmentioning

confidence: 99%

Genetic design of linear multivariable feedback control systems using eigenstructure assignment

Porter

Borairi

1992

International Journal of Systems Science

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In the design of linear multivariable feedback control systems, performance specifications can frequently be met by assigning appropriate closed-loop eigenstructure. However, although various powerful algorithms for the solution of this problem of eigenstructure assignment have been proposed over the years, no universally applicable and computationally stable methodology has yet emerged. These difficulties motivate the use of genetic algorithms in the solution of the fundamental problem of eigenstructure assignment.

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Section: Resultsmentioning

confidence: 99%

Genetic design of linear multivariable feedback control systems using eigenstructure assignment

Porter

Borairi

1992

International Journal of Systems Science

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“…In particular, Evolutionary Algorithms were seen as truly librating algorithms when compared to for example linear programming where only some types of problems could be solved and even then a considerable design effort was required to formulate the problem. For example, in control systems theory Porter and his coauthors were able to solve several difficult systems theory problems using genetic algorithms [68,69,70,71]. Although the Evolutionary Algorithms had truly opened new doors in all optimization fields and especially those related to engineering applications, this initial frenzy was soon overcame with the realization that Evolutionary Algorithms suffer from some major drawbacks making their use nonsensical in some cases.…”

Section: Recent Trends In Evolutionary Algorithmsmentioning

confidence: 99%

On Natural Based Optimization

Nobakhti

2010

Cogn Comput

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Nature has always been a source of great inspiration for engineers and mathematicians. Evolutionary Algorithms are the latest in a line of natural-based innovations which have had a profound effect on the application of optimization in science and engineering. Although based on nature, Evolutionary Algorithms are nonetheless distinctly different from natural evolution in several areas. This paper outlines early and recent developments of Evolutionary Algorithms while covering those areas of difference. Practical issues related to the use of Evolutionary Algorithms, key parameters that affect the quality of the search and impact of user choices in problem formulation are also covered in this paper.

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“…They stressed the benefit of flexibility with regard to cost function (there being no requirement for mathematical tractability) and, in particular, alluded to the concept of Pareto optimality (providing the potential to simultaneously address multiple objectives, such as transient performance and disturbance rejection). In an independent enquiry, Porter and Jones (1992) proposed a GA-based technique as a simple, generic, method of tuning digital PID controllers. Vlachos, Williams, and Gomm (1992) applied a GA to the tuning of decentralised Proportional-Integral (PI) controllers for multivariable processes.…”

Section: Introductionmentioning

confidence: 99%