自适应粒子群算法在AUV水动力参数辨识中的应用

Application of adaptive particle swarm optimization algorithm in AUV hydrodynamic parameter identification

以自主式水下航行器(AUV)的水动力参数辨识为背景,对其辨识方法进行研究。针对现阶段一些辨识方法中存在的辨识方程未采用AUV六自由度运动方程、需要数据消噪、对数据初值具有敏感性的问题,以及考虑AUV水动力参数多、参数之间耦合度高的特点,提出基于最小二乘准则和自适应粒子群优化算法的AUV水动力参数辨识方法,并利用仿真对该方法进行检验。仿真结果表明,该方法具有较好的可行性和鲁棒性,并同粒子群优化算法相比具有更好的稳定性和快速性。

Based on the hydrodynamic parameter identification of autonomous underwater vehicle(AUV),the identification method of the hydrodynamic parameter identification of autonomous underwater vehicle is studied.In view of the problems existing in some identification methods of autonomous underwater vehicle’s hydrodynamic parameter identification at present that the hydrodynamic parameter identification equation does not adopt AUV six degrees of freedom motion equation and the hydrodynamic parameter identification process of AUV requires noise elimination and some identification methods are sensitive to the initial value of data,and considering the characteristics that there are many AUV hydrodynamic parameters and the coupling between parameters is high,an AUV hydrodynamic parameter identification method based on least square criterion and adaptive particle swarm optimization algorithm is proposed,and the method is verified by simulation experiments.The simulation results show that the method is feasible and robust and has better stability and rapidity compared with the particle swarm optimization algorithm.