This paper proposes a new method for the selection of input-output pairing in decentralized control structures for multivariable systems. This method proposes the input-output pairing problem as a multi-objective optimization problem (MOP). For each control structure and loop pairing analyzed, a different design concept is proposed and a MOP is stated. All MOPs share the same design objectives, and Pareto fronts associated with each design concept can be compared globally under a multi-objective (MO) approach. The design objectives were chosen for the MOP, as well as the designer's preferences, have an important role in selecting a certain loop pairing. The main contribution of the proposed approach is that it enables a systematic analysis of the conflicts between the objectives and the performance of a control system. The method enables selecting a certain input-output pairing and a suitable tuning of the controller directly using information that a designer can interpret. To show the application of the methodology, two loop pairing examples are presented, one of them for a two-input-output system (with four scenarios of analysis), and the other for a three-input-output system (with one scenario of analysis). Through the examples presented in this paper, it is evident how the designer can affect the loop pairing to be used, either by choosing the objectives or preferences.INDEX TERMS Multivariable control system, input-output pairing, decentralized control structures, multiobjective evolutionary optimization, Pareto front.
In the multivariable control literature there are few techniques that face the problem of selecting suitable loop pairings in non-linear multivariable systems. Most techniques analyze the linearized system at a specific operating point. This paper proposes a new methodology to optimally and simultaneously select the loop pairings and the tuning of the parameters of the decentralized control by applying a multi-objective optimization approach directly on the non-linear system. The main contribution of this work is that the proposed methodology enables a detailed multi-dimensional analysis of the performances and trade-offs in the available loop pairings to control a multivariable non-linear system. The methodology is applied in this paper to three examples that analyze how the different types of loop pairings conflict. In one of the examples, the proposed methodology was applied first in the linearized system and later in the non-linear system. The results were contradictory and show how the application of loop pairing techniques for linear systems can be inaccurate when they are applied on a non-linear system previously linearized at an operating point. The following examples show that the operating point of a non-linear system, the design objectives of each multiobjective problem, as well as the designer's preferences have important roles in the selection of an optimal loop pairing. INDEX TERMS Multivariable control system, non-linear systems, loop pairing, decentralized control structures, multi-objective evolutionary optimization, Pareto front.
Este artículo muestra la aplicación de técnicas de optimización multiobjetivo, tanto para la identificación de parámetros de un modelo como para el ajuste de controladores. En particular, se propone una técnica para identificar los parámetros de un modelo en primeros principios para un péndulo invertido rotatorio aplicando una metodología de optimización multiobjetivo y datos experimentales. Así también la metodología se extiende a la sintonización de controladores PID y PI para el sistema en mención. En la aplicación de la metodología multiobjetivo se utilizan una serie de herramientas para cada una de las etapas. Como optimizador se ha utilizado una implementación basada en algoritmos evolutivos, ev-MOGA (Herrero et al., 2007). Para la fase de análisis de las soluciones del frente se utiliza la herramienta de visualización del frente de Pareto denominada level diagram (Blasco et al., 2017), que permite explorar satisfactoriamente el conjunto de soluciones óptimas de Pareto y seleccionar una de ellas de acuerdo con las preferencias del diseñador. Una ventaja que ofrece esta metodología es la fácil comprensión de las conflictos que aparecen entre los objetivos de diseño, permitiendo seleccionar una solución de compromiso satisfactoria de cuerdo a las preferencias del diseñador, sin perder de vista el conjunto de soluciones óptimas encontradas.
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