Xiang-yang ZHOU, Jun ZHU, Yan-jun SHI. Multi-objective optimization on mechatronic system of a light and small pan-tilt system for unmanned aerial vehicle application[J]. Optics and precision engineering, 2018, 26(11): 2754-2763.
DOI:
Xiang-yang ZHOU, Jun ZHU, Yan-jun SHI. Multi-objective optimization on mechatronic system of a light and small pan-tilt system for unmanned aerial vehicle application[J]. Optics and precision engineering, 2018, 26(11): 2754-2763. DOI: 10.3788/OPE.20182611.2754.
Multi-objective optimization on mechatronic system of a light and small pan-tilt system for unmanned aerial vehicle application
The light and small pan-tilt system of a multi-rotor unmanned aerial vehicle is a complicated electromechanical servo system that requires a number of desirable properties such as light weight and rapid response to be realized. We focus on the inherent limitations of the traditional methods in the electromechanical servo-system design to present a multi-objective optimization method for a mechatronic system based on the bandwidth. For the structure system
seven-dimensional parameters were selected using sensitivity analysis in which the mass and first-order natural frequency are considered as the optimization goals. For the control system
the controller parameters in the rate and position loops are selected as design variables in which the rise time
regulating time
and integral absolute error (IAE) are considered as the optimization goals. In the optimization process
an optimization method that combines the approximation model and multi-objective genetic algorithm is proposed to reduce the complexity
improve the efficiency
and improve the overall optimization ability during the optimization process. Simulations are then conducted to verify the proposed method. The results show that
compared with the original model of the control and structure systems
the mass
IAE
regulating time
and rise time are reduced by 8.8%
54.8%
81.9%
and 53.4%
respectively. Finally
a modal pan-tilt experiment is carried out by hammering
and the error in the experimental and optimization results is found to be 13.4%. Therefore
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