Optimal multi-loop control system design subject to explicit constraints

• Verfasst von: Wellenreuther, Andrea [VerfasserIn]
• Verfasst von: Gambier, Adrián [VerfasserIn]
• Verfasst von: Badreddin, Essameddin [VerfasserIn]
• Titel: Optimal multi-loop control system design subject to explicit constraints
• Verf.angabe: A. Wellenreuther, A. Gambier, E. Badreddin
• Jahr: 2007
• Umfang: 6 S.
• Fussnoten: Gesehen am 21.01.2020 ; Vom Autor zitiert als: Optimal multi-loop control design of a reverse osmosis desalination plant with robust performance
• Titel Quelle: Enthalten in: European Control Conference (2007 : Kos)Control Conference (ECC), 2007 European
• Ort Quelle: [Piscataway, New Jersey] : IEEE, 2007
• Jahr Quelle: 2007
• Band/Heft Quelle: (2007), Seite 3957-3962
• ISBN Quelle: 3-9524173-8-6
• ISBN Quelle: 978-3-9524173-8-6
• DOI: doi:10.23919/ECC.2007.7069013
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Conductivity
• Sach-SW: control system synthesis
• Sach-SW: controller parameter tuning
• Sach-SW: decoupling method
• Sach-SW: Desalination
• Sach-SW: explicit constraints
• Sach-SW: game
• Sach-SW: game theory
• Sach-SW: genetic algorithm
• Sach-SW: genetic algorithms
• Sach-SW: Genetic algorithms
• Sach-SW: Linear programming
• Sach-SW: multiobjective optimization problem
• Sach-SW: objective function
• Sach-SW: optimal control
• Sach-SW: Optimal control
• Sach-SW: optimal multiloop control system design
• Sach-SW: Optimization
• Sach-SW: reverse osmosis desalination plant
• Sach-SW: squared error formulation
• K10plus-PPN: 1687962391

Abstract

A present method of tuning controller parameters in a multi-loop system is modified to maintain explicit constraints. This control system design is called optimal in the sense of performance and it is treated as a game including more than one interacting player representing the controller. Each player (controller) has to satisfy an objective function implying a squared error formulation. In practice, since every control signal cannot be followed by the physical system, the control signals of the plant have to be limited. Thus, explicit constraints are added to the game. On the one hand, the constraints are considered during the optimization of the objective functions, on the other hand, they are additionally reformulated as objective functions and optimized, too. It follows, that each player has to satisfy one, respectively two objective functions for this game. The solution of the game is the optimization of all multiple objective functions, leading to a multi-objective optimization problem, which is solved by a genetic algorithm. The modified method is applied to a model of a reverse osmosis desalination plant and a comparison of this method with the present method and a conventional decoupling method is shown.