Linear matrix inequalities and evolutionary optimization in multiobjective control

Molina-Cristóbal, Arturo; Griffin, Ian; Fleming, Peter J.; Owens, D.H.

doi:10.1080/00207720600783942

Cited by 30 publications

(21 citation statements)

References 18 publications

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“…This is the main advantage of the proposed method when compared to previous works by Herreros (2000) and Molina-Cristobal et al (2006a).…”

Section: The Pole Placement Methodsmentioning

confidence: 49%

“…The first is a H 2 problem with a PI controller structure, for a first order stable plant. The second is am o r ec o m p l e xH 2 /H 4 problem, previously studied by Herreros (2000) and Molina-Cristobal et al (2006a).…”

Section: Introductionmentioning

confidence: 99%

“…The approach was generalized to design multivariable coupled and decentralized PID loops and was successfully validated for a large number of experimental cases. Takahashi et al (2004) and Molina-Cristobal et al (2006a) also compared genetic versus LMI -based approaches to estimate the Pareto set for H 2 /H 4 problems. The authors asserted that the genetic approach could find better results compared to the LMI approach.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Solving Multi-Objective Linear Control Design Problems Using Genetic Algorithms

Sánchez

Villasana

Strefezza

2008

IFAC Proceedings Volumes

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“…This is the main advantage of the proposed method when compared to previous works by Herreros (2000) and Molina-Cristobal et al (2006a).…”

Section: The Pole Placement Methodsmentioning

confidence: 49%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solving Multi-Objective Linear Control Design Problems Using Genetic Algorithms

Sánchez

Villasana

Strefezza

2008

IFAC Proceedings Volumes

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“…This last statement enabled the performance improvement of the controller to be analysed as the controller order size variate. Similarly, in [96], the MOOD approach is compared with the LMI concept for an LQR and H 2 /H ∞ controllers using as objectives noise sensitivities for output and actuator.…”

Section: Applications On State Space Feedback Controller Design Conceptmentioning

confidence: 99%

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

Reynoso-Meza

Blasco

Sanchis

et al. 2014

Control Engineering Practice

118

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Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach to calculate a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective searching for a set of potentially preferable solutions; the designer may then analyse the trade-off among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are commented on. Through this paper it is noticed that EMO research has been developing towards different optimization statements, but such statements are not commonly used on controller tuning. Therefore gaps between EMO research and EMO applications on controller tuning are detected and suggested as potential trends for research.

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“…In 2006, Molina-Cristobal et al [7] compared MOGA against a LMI approach to find the trade-off of a multi-objective H 2 /H ∞ control problem. The author asserted that MOGA could find an improved Pareto-optimal front compared to the LMI approach.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Pole Placement with Evolutionary Algorithms

Sánchez

Villasana

Strefezza

Lecture Notes in Computer Science

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Abstract. Multi-Objective Evolutionary Algorithms (MOEA) have been succesfully applied to solve control problems. However, many improvements are still to be accomplished. In this paper a new approach is proposed: the Multi-Objective Pole Placement with Evolutionary Algorithms (MOPPEA). The design method is based upon using complexvalued chromosomes that contain information about closed-loop poles, which are then placed through an output feedback controller. Specific cross-over and mutation operators were implemented in simple but efficient ways. The performance is tested on a mixed multi-objective H2/H∞ control problem.

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Linear matrix inequalities and evolutionary optimization in multiobjective control

Cited by 30 publications

References 18 publications

Solving Multi-Objective Linear Control Design Problems Using Genetic Algorithms

Solving Multi-Objective Linear Control Design Problems Using Genetic Algorithms

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

Multi-objective Pole Placement with Evolutionary Algorithms

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