基于遗传与粒子群算法的隔振平台减振性能优化

doi:10.13229/j.cnki.jdxbgxb20190469

摘要/Abstract

摘要：

重载隔振平台主要应用于搭载高精密仪器设备，隔离复杂路面不平所产生振动与冲击，为其提供稳定的工作环境。本文以6自由度并式联隔振平台为研究对象，使用精英策略下快速非支配排序遗传算法（NSGA-Ⅱ）和多目标粒子群算法（MOPSO）对平台的减振系统进行参数优化，显著提高了其隔振性能。在多种不同路面干扰信号激励下，使用NSGA-Ⅱ和MOPSO两种方法对减振系统的阻尼和刚度参数进行优化，优化结果表明：不同激励下，平台上平面垂向加速度和垂向位移两优化目标的隔振率最高可达56.21%；此外，应用NSGA-Ⅱ对隔振平台阻尼器支撑角度参数进行优化，使两优化目标隔振率提高了7.00%；并对正弦激励情况下的仿真参数进行了实验验证，实验与仿真误差可达8.30%，验证了参数优化的有效性。

关键词: 隔振平台, 虚拟样机模型, 多目标优化, 隔振率, 多目标粒子群算法

Abstract:

Heavy-loaded Anti-Vibration Platform (AVP) is mainly used to carry high-precision instruments and equipments to isolate vibration and impact caused by uneven complex road surface, thus, providing a stable working environment. In this paper, taking the 6 DOF series AVP as the research object, the parameters of the AVP damping system are optimized by the usage of the fast elitist multi-objective genetic algorithm (NSGA_II) and multi-objective particle swarm optimization algorithm (MOPSO) to significantly improve the anti-vibration performance of AVP. The damping and stiffness parameters of the damping system are optimized by NSGA-II and MOPSO under various road surface analog signals. The optimization results show that the anti-vibration rate of the two optimization targets of vertical acceleration and vertical displacement of upper platform under different excitation can be up to 56.21%. In addition, NSGA-II is applied to optimize the damper support angle parameters of the AVP, which increases the anti-vibration rate of the two optimization targets by 7.00%. The sine excitation is verified by experiments, and the error between experiment and simulation is as small as 8.30%, thus verifying the effectiveness of optimization parameters.

Key words: anti-vibration platform, virtual prototyping, multi-objective optimization, anti-vibration rate, multi-objective particle swarm optimization algorithm

中图分类号:

U463.99

马芳武,韩丽,吴量,李金杭,杨龙帆. 基于遗传与粒子群算法的隔振平台减振性能优化[J]. 吉林大学学报(工学版), 2020, 50(5): 1608-1616.

Fang-wu MA,Li HAN,Liang WU,Jin-hang LI,Long-fan YANG. Damping optimization of heavy⁃loaded anti⁃vibration platform based on genetic algorithm and particle swarm algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1608-1616.

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参考文献 14

1	李耀. 六自由度隔振平台的设计与分析[D]. 南京：南京航空航天大学机电学院, 2015.
	Li Yao. Design and analysis of the six-degree-of-freedom vibration-isolation platform[D]. Nanjing: College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 2015.
2	Youn I, Wu L, Youn E, et al. Tomizuka: attitude motion control of the active suspension system with tracking controller[J] Internal Journal of Automotive Technology, 2015, 16(4): 593-601.
3	马芳武, 倪利伟, 吴量, 等. 轮腿式全地形移动机器人位姿闭环控制[J]. 吉林大学学报: 工学版, 2019, 49(6): 1745-1755.
	Ma Fang-wu, Ni Li-wei, Wu Liang, et al. Position and attitude closed loop control of wheel⁃leggedall terrain mobile robot[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 1745-1755.
4	Wu L， Khan M A， Youn E, et al. Attitude motion control of vehicle including the active passenger seat system[J]. International Journal of Vehicle Design, 2018, 78(1-4): 131-160.
5	张抗抗, 徐梁飞, 华剑锋, 等. 基于多目标优化的纯电动车动力系统参数匹配[J]. 汽车工程, 2015, 37(7): 757-765.
	Zhang Kang-kang, Xu Liang-fei, Hua Jian-feng, et al. A parameter matching method for the powertrain of battery electric vehicle based on multi-objective optimization[J]. Automotive Engineering, 2015, 37(7): 757-765.
6	王庆年, 段本明, 王鹏宇, 等. 插电式混合动力汽车动力传动系参数优化[J]. 吉林大学学报: 工学版, 2017, 47(1): 1-7.
	Wang Qing-nian, Duan Ben-ming, Wang Peng-yu, et al. Optimization of powertrain transmission parameters of plug in hybrid electric vehicle[J]. Journal of Jilin University(Engineering and Technology Edition), 2017, 47(1): 1-7.
7	王咏. 高精密设备多自由度平台系统微振动混合控制研究[D]. 哈尔滨：哈尔滨工业大学深圳研究生院, 2007.
	Wang Yong. Multi-degree-of-freedom microvibration hybrid control system for high technology facilities[D]. Harbin: Harbin Institute of Technology, Shenzhen Graduate School, 2007.
8	李林, 宣明, 贾宏光, 等. 光电稳定平台隔振系统的设计与优化[J]. 计算机仿真, 2017, 34(3): 77-81.
	Li Lin, Xuan Ming, Jia Hong-guang, et al. Design and optimization of vibration isolation system for photoelectric stabilized platform[J]. Computer Simulation, 2017, 34(3): 77-81.
9	谢振盛. 汽車主動式懸吊系統之最佳化控制器設計研究[D]. 台南：昆山科技大学工程学院, 2007.
	Xie Zhen-sheng. Study on optimal controllers design for automotive active suspension systems[D]. Tainan: Science and Technology, Kun Shan University of Technology, 2007.
10	Wu L, Li J H, Ma F W, et al. Optimal anti-vibration design of vehicle-mounted vibration isolation platform[C]∥ SAE Technical Paper, 2018-01-1400.
11	Deb K, Pratap A, Agarwal S, et al. A fast elitist multi-objective genetic algorithm[J]. Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
12	李杰, 车华军, 哈兰涛. 遗传算法及在汽车悬架参数优化设计中的应用[J]. 汽车技术, 2006(6): 18-21.
	Li Jie, Che Hua-jun, Lan-tao Ha. The application of genetic algorithm in the optimum design automotive suspension parameter[J]. Automotive Technology, 2006(6): 18-21.
13	张勇, 巩敦卫. 先进多目标粒子群优化理论及其应用[M]. 北京：科学出版社, 2016.
14	周红妮, 冯樱, 赵慧勇, 等. 基于ISIGHT 集成技术的某重卡双轴转向机构多目标优化[J]. 汽车研究与开发, 2015, 6(4): 26-30.
	Zhou Hong-ni, Feng Ying, Zhao Hui-yong, et al. Multi-objective optimization on dual-axle steering mechanism of a heavy vehicle based on integration technology of ISIGHT[J]. Automobile Research and Development, 2015, 6(4): 26-30.

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参数	数值
簧上质量/kg	650
簧下质量/kg	300
阻尼/(N·s·m^-1)	1 500
空气弹簧刚度/(N·m^-1)	16 800
钢索弹簧刚度/(N·m^-1)	200 000

参数	初值	上限	下限
DV9	0.488	0.439	0.537
DV10	0.938	0.844	1.032
DV11	0.486	0.437	0.535
DV12	1.045	0.941	1.150
DV13	0.488	0.439	0.537
DV14	0.938	0.844	1.032
DV15	1.036	0.932	1.140
DV16	0.486	0.437	0.535

参数	NSGA-II	优化前
DV9	0.500	0.480
DV10	1.016	0.938
DV11	0.392	0.486
DV12	1.100	1.045
DV13	0.389	0.488
DV14	1.033	0.938
DV15	1.128	1.036
DV16	0.394	0.486

变量	NSGA-II	MOPSO	优化前
S₁	1 130	1 000	1 500
S₂	1 044	1 026	1 500
S₃	1 060	2 000	1 500
S₄	1 247	1 000	1 500
S₅	1 364	1 000	1 500
S₆	1 382	1 000	1 500
S₇	1 086	1 000	1 500
S₈	1 078	1 000	1 500
S₉	10 050	10 000	16 800
S₁₀	10 017	10 477	16 800
S₁₁	10 028	10 000	16 800
S₁₂	10 035	10 000	16 800

变量	NSGA-II	MOPSO	优化前
S₁	1 352	1 360	1 500
S₂	1 132	1 085	1 500
S₃	1 574	1 000	1 500
S₄	1 110	1 000	1 500
S₅	1 254	1 000	1 500
S₆	1 482	1 000	1 500
S₇	1 243	1 086	1 500
S₈	1 165	2 030	1 500
S₉	12 205	10 161	16 800
S₁₀	10 002	11 156	16 800
S₁₁	13 430	13 482	16 800
S₁₂	14 202	10 597	16 800