Open AccessSoftware Environment for Computer-Aided Heuristic Optimization of Hydraulic Systems for Synchronous Movement of Actuators of Various Functional Purposes
Author(s) -
A. Yu. Bushuev,
Mikhail Ivanov,
D. V. Korotaev,
G. F. Resh
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1391/1/012141
Subject(s) - computer science , software , heuristic , synchronization (alternating current) , actuator , process (computing) , control engineering , convergence (economics) , minification , genetic algorithm , mathematical optimization , basis (linear algebra) , control theory (sociology) , engineering , artificial intelligence , channel (broadcasting) , mathematics , control (management) , computer network , geometry , machine learning , economics , programming language , economic growth , operating system
A software environment was developed for computer-aided design of optimal throttle hydraulic synchronization systems of actuators of various functional purposes operating under conditions of external alternating-sign force effects. The criterion of an optimization procedure was the minimization of a mismatch time of relative movement of actuators during operation. A compute core of an object-oriented code was constructed on the basis of a dynamic mathematical model of a synchronization system consisting of four power cylinders. A model problem was solved with the help of the created software environment. This model problem demonstrated the efficiency of the proposed multidimensional optimization process. The methodology was based on the use of the well-known heuristic method (binary coded genetic algorithm) and the subsequent improvement (in the sense of a given objective functional) of the obtained solution by a method on the basis of the Hooke-Jeeves algorithm. Recommendations on the practical application of the software and mathematical support for achieving the best convergence to the extreme value of a vector of controlled parameters were formulated.