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中文摘要：
提出一种针对复杂对象参数辨识问题的复合评价策略，通过分层设计优化指标评价函数，降低了迭代搜索计算对分辨局部极小值与全局最优值的需求，在少量增加计算消耗的条件下，显著提高辨识精度。以搭载多关节机械臂的空间机器人为复杂对象代表，配合一种改进型粒子群算法，在无须线动量测量信息和仅使用历史数据的条件下，对机器人抓取的目标的惯性参数进行参数辨识。115组辨识仿真算例的统计结果表明，使用该复合评价策略，基本不增加计算消耗，而辨识精度得到大幅提升。定性地分析认为群体智能算法与该策略配合更易发挥效果，有望在更宽泛的对象和领域中得到应用。
英文摘要：
A composite evaluation strategy for complex object parameter identification problem is proposed. By hierarchically design optimized index evaluation function, the requirement of anti-local minimal ability for iterative search algorithm is reduced. Identification accuracy is significantly improved while small increase of calculate consumption. A multi-joint manipulator space robot is used as a representative of complex objects. An improved particle swarm optimization is used to identify the parameters of its crawl target inertial parameters without linear momentum measurement information. 115 group identification simulation example statistical results show that using the composite evaluation strategy, the recognition accuracy is increased dramatically by little increased of the calculate consumption. Qualitative analysis considers that swarm intelligence algorithms have better performance with the strategy, which is expected to be applied in a broader objects and fields.
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