基于多传感器递推总体最小二乘融合的水下机器人动力学模型参数辨识

对于水下机器人动力学模型辨识问题,如果其观测方程的系数矩阵包含随机扰动,则其最小二乘估计一般是有偏的。为此,该文提出一种基于多传感器递推总体最小二乘融合的水下机器人动力学模型辨识算法(RTLS_F)。首先,给出了集中式总体最小二乘融合的算法;然后,在总体最小二乘框架下,推导出多传感器递推融合估计算法。通过仿真实验对RTLS_F与其它水下机器人动力学参数辨识算法进行了比较。实验结果表明,在系数矩阵和观测向量都含有误差的情况下,最小二乘融合是有偏估计且难以提高估计精度,而RTLS_F算法可以有效改善参数辨识性能。

Dynamics model identification of underwater vehicles based on the multi-sensor fusion of recursive total least squares

For the dynamics model identification of the underwater vehicles, if the coefficient matrix of the observed equation contains random perturbation, its least squares estimation is generally biased. In this pa-per, a novel algorithm (RTLS_F) for the dynamic model identification of the underwater vehicle is proposed. The centralized fusion method of total least squares is given. Under the framework of the total least squares, the algorithm of multi-sensor recursive fusion is deduced. Performance comparisons between the proposed and the other algorithms are carried out through the simulation experiments. The experimental results show that the least squares fusion is the biased estimation and it is difficult to improve the estimation accuracy if both the coefficient matrix and the observed vector contain errors, whereas the RTLS_F algorithm can ef-fectively improve the performance of parameter identification in the same situation.