Automated synthesis of multivariable QFT controller using interval constraint satisfaction technique

2012 ◽  
Vol 22 (4) ◽  
pp. 751-765 ◽  
Author(s):  
Mukesh D. Patil ◽  
P.S.V. Nataraj
Keyword(s):  
Constraint Satisfaction ◽  
Automated Synthesis ◽  
Interval Constraint Satisfaction Technique ◽  
Interval Constraint

scholarly journals System Sizing with a Model-Based Approach: Application to the Optimization of a Power Transmission System

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Pierre-Alain Yvars ◽  
Laurent Zimmer
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Programming ◽  
Power Transmission ◽  
Constraint Satisfaction Problem ◽  
Transmission System ◽  
System Description ◽  
Model Based ◽  
Power Transmission System ◽  
Design Requirements ◽  
Interval Constraint

We test the relevance of a model-based approach for sizing and optimizing complex systems. Classically a model-based approach is characterized by a clear partition between the problem description and the solving process. In the case of a design problem, we show that the sizing task could be systematically characterized and therefore could lead to a declarative model combining both system description and design requirements. Once translated into a constraint satisfaction problem, the resulting model can be solved with interval constraint programming methods and algorithms. Our first contribution to this approach is to precisely characterize the sizing task in design. The resulting terminology enables us to easily and systematically express the problem as a constraint satisfaction problem (CSP) which combines in the same model the system description and the design requirements. We have tested the approach on the optimal sizing problem of a power transmission system. Previous authors have described this scalable case study. They provide a mathematical formulation of the problem and solve it with an evolutionary algorithm. Starting from their description, we apply our methodology to model the problem as a CSP and then solve it with interval constraint programming algorithms. Our solutions are more adequate both in computational time and in optimization results than those published in the literature on the same problem. Moreover the declarative nature of constraint programming makes modifications or extensions easier than with evolutionary programming. The explanation of these results is our second contribution to the approach. However some important modelling issues remain to address in order to capture more and more complex system specifications. Further research is presented at the end of this paper.

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