A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators
| Autor: | Eusebio Hernandez, S. Ivvan Valdez, Alejandra Rios |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Optimal design
0209 industrial biotechnology Mathematical optimization Optimization problem Computer science parallel manipulator General Mathematics Evolutionary algorithm PID controller 02 engineering and technology mono and multi-objective optimization Multi-objective optimization 020901 industrial engineering & automation Genetic algorithm 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) optimal design Engineering (miscellaneous) two-stage method lcsh:Mathematics Sorting Particle swarm optimization Gough–Stewart lcsh:QA1-939 performance metrics multi-objective optimization 020201 artificial intelligence & image processing |
| Zdroj: | Mathematics Volume 9 Issue 5 Mathematics, Vol 9, Iss 543, p 543 (2021) |
| ISSN: | 2227-7390 |
| DOI: | 10.3390/math9050543 |
| Popis: | This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests. |
| Databáze: | OpenAIRE |
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