空间机器人惯性参数辨识的粒子群优化新算法

提出了针对一类多自由度空间机器人卫星惯性参数在轨辨识的一种粒子群（PSO）优化新算法。通过粒子邻域限定的多样性保持、低效粒子随机重置和粒子误差的序列性评价，得到了比常规方法更好的结果，且具有无附加燃料消耗、线动量测量和特定的机器人路径规划等便利性优点。仿真算例表明，该改进方法具有较高的准确性与效率。

A New Particle Swarm Optimization Approach to the Inertia Parameters Identification of Onorbit Space Robot

A new kind of particle swarm optimization (PSO) algorithm is proposed to identify the inertia parameters of an onorbit satellite equipped with a class of Multi-DOF robot. By diversity maintenance by limiting the definition of particle neighborhood, random reset of inefficient particles and sequential evaluation of particle errors, a better result is achieved in contrast with the classical PSO algorithm. Moreover, it doesn’t require additional fuel consumption, linear momentum measurement nor specific robot path planning. The simulation experiments show that the improved algorithm performs more accurately and efficiently.