Rodriguez-Molina Alejandro, Villarreal-Cervantes Miguel G, Mezura-Montes Efren, Aldape-Perez Mario
IEEE Trans Cybern. 2021 Mar;51(3):1272-1285. doi: 10.1109/TCYB.2019.2903491. Epub 2021 Feb 17.
The efficient speed regulation of four-bar mechanisms is required for many industrial processes. These mechanisms are hard to control due to the highly nonlinear behavior and the presence of uncertainties or disturbances. In this paper, different Pareto-front approximation search approaches in the adaptive controller tuning based on online multiobjective metaheuristic optimization are studied through their application in the four-bar mechanism speed regulation problem. Dominance-based, decomposition-based, metric-driven, and hybrid search approaches included in the algorithms, such as nondominated sorting genetic algorithm II, multiobjective evolutionary algorithm based on decomposition and differential evolution, S-metric selection evolutionary multiobjective algorithm, and nondominated sorting genetic algorithm III, respectively, are considered in this paper. Also, a proposed metric-driven algorithm based on the differential evolution and the hypervolume indicator (HV-MODE) is incorporated into the analysis. The comparative descriptive and nonparametric statistical evidence presented in this paper shows the effectiveness of the adaptive controller tuning based on online multiobjective metaheuristic optimization and reveals the advantages of the metric-driven search approach.
许多工业过程都需要对四杆机构进行高效调速。由于其高度非线性行为以及存在不确定性或干扰，这些机构很难控制。本文通过将不同的帕累托前沿近似搜索方法应用于四杆机构调速问题，研究了基于在线多目标元启发式优化的自适应控制器调整。本文考虑了算法中包含的基于支配、基于分解、基于度量和混合搜索方法，分别为非支配排序遗传算法II、基于分解的多目标进化算法和差分进化、S-度量选择进化多目标算法以及非支配排序遗传算法III。此外，一种基于差分进化和超体积指标的拟议度量驱动算法（HV-MODE）也被纳入分析。本文给出的比较性描述性和非参数统计证据表明了基于在线多目标元启发式优化的自适应控制器调整的有效性，并揭示了度量驱动搜索方法的优势。
