Structural multi-objective optimization on a MUAV-based pan–tilt for aerial remote sensing applications. (May 2020)

• Record Type: Journal Article
• Authors: Zhou, Xiangyang; Shi, Yanjun; Zhu, Jun; Zhao, Libo; Zhu, Zhuangsheng
• Is Part Of: ISA transactions. Volume 100(2020)
• Page Start: 405
• Page End: 421
• Publication Date: 2020-05
• Subjects: Multirotor unmanned aerial vehicle -- Pan-tilt -- Multi-objective optimization -- Approximation model -- NSGA-II; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2019.11.036
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: To realize both mechatronic properties of light mass and high control precision for a pan–tilt based on the multirotor unmanned aerial vehicle (MUAV) for aerial remote sensing applications, a structural multi-objective optimization approach based on the approximate model and genetic algorithm is proposed. The optimized structural parameters for establishing the approximate model are determined through the sensitivity analysis method, by which the predominant variables affecting mass and modal are selected. The approximate models on mass and first-order modal frequency are built through response surface methodology (RSM) and radial basis function (RBF) to heighten the optimization efficiency and smooth the numerical optimization results. Particularly, the non-dominated sorting genetic algorithm-II (NSGA-II) and Pareto Optimality are combined to acquire the parameters' optimal solutions. The modal tests based on hammering method and the control experiments are both conducted to validate the performance of the proposed approach. The results indicate that the structural multi-objective optimization approach presented in this paper is effective to reduce the mass meanwhile enhance its first-order modal frequency, hence leading to good stabilizing and tracking performances. Highlights: A structural multi-objective optimization approach is proposed for the pan-tilt. A two-step optimization scheme is proposed to achieve optimization objectives. The approximate models are built to heighten the optimization efficiency. The NSGA-II and Pareto Optimality are combined to obtain the optimal solution. The experiments are conducted to verify the effectiveness of the proposed method.