A Data-Driven Multi-Scale Online Joint Estimation of States and Parameters for Electro-Hydraulic Actuator in Legged Robot
A Data-Driven Multi-Scale Online Joint Estimation of States and Parameters for Electro-Hydraulic Actuator in Legged Robot
复制标题
腿式机器人电液执行器的数据驱动多尺度在线联合估计状态和参数
DOI：
10.1109/access.2020.2974984
复制
发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Ma Hongxu
中科院分区：
文献类型：
--
作者：
Huang Jie;An Honglei;Lang Lin;Wei Qing;Ma Hongxu
为满足基于模型的控制器对模型参数和全状态反馈的实时需求，本文针对时变参数估计（包括系统参数和外部负载力）、不可测状态估计以及可测状态滤波这三个问题，对腿式机器人电液作动器的状态与参数估计展开了深入研究。基于动力学模型的一阶轨迹灵敏度法被用于确定待估计参数集，并详细分析参数的快慢特性，以获取广义状态和慢变参数。随后，创新性地提出了一种结合快变时间尺度（由融合卡尔曼滤波器和快变时间尺度扩展卡尔曼滤波器组成）和慢变时间尺度（由慢变时间尺度扩展卡尔曼滤波器组成）的联合算法，以实现作动器系统状态与参数的数据驱动多尺度在线联合估计。最后，三项对比实验结果表明，所提算法相较于双扩展卡尔曼滤波算法，具有更好的稳定性、更快的收敛速度以及更精确的估计，且所提算法估计出的状态和参数能准确反映作动器的实际特性。此外，该算法在不同的作动器硬件环境中具有很强的适应性和鲁棒性，对状态和参数的不同初始值也具有很强的收敛能力。
In order to satisfy the real-time need of model-based controllers for model parameters and full states feedback, this paper has conducted in-depth research on the states and parameters estimation of electro-hydraulic actuator in legged robot with three problems for time-varying parameters estimation (including system parameters and external load force), non-measurable states estimation and measurable states filtering. The first-order trajectory sensitivity method based on the dynamic model is used to determine the parameter set to be estimated, and the parameter fast and slow characteristics are analyzed in detail to obtain the generalized states and slow-varying parameters. Then, the combined algorithm with a fast-varying time scale (composed of a fusion kalman filter and a fast-varying time scale extended kalman filter) and a slow-varying time scale (composed of a slow-varying time scale extended kalman filter) is innovatively proposed to realize the data-driven multi-scale online joint estimation of states and parameters for the actuator system. Finally, the results of three comparative experiments show that the proposed algorithm has better stability, faster convergence speed and more accurate estimation than the dual extended kalman filter algorithm, and the states and parameters estimated by the proposed algorithm accurately reflect the actual characteristics of actuator. Moreover, the algorithm has strong adaptability and robustness in different actuator hardware environment and strong convergence ability for different initial values of states and parameters.
登录
查看更多内容
影响因子：
3.4
作者：
Barasuol V;Villarreal-Magaña OA;Sangiah D;Frigerio M;Baker M;Morgan R;Medrano-Cerda GA;Caldwell DG;Semini C
通讯作者：
Semini C
DOI：
10.1016/j.amc.2019.124860
发表时间：
2020-03
期刊：
Appl. Math. Comput.
影响因子：
--
作者：
Roger Miranda‐Colorado
通讯作者：
Roger Miranda‐Colorado
DOI：
10.1109/tmech.2013.2265312
发表时间：
2014-06
期刊：
IEEE/ASME Transactions on Mechatronics
影响因子：
--
作者：
K. Ahn;D. N. C. Nam;Maolin Jin
通讯作者：
K. Ahn;D. N. C. Nam;Maolin Jin
影响因子：
4.9
作者：
Hai-Bo Yuan;Hong-Cheol Na;Youngbae Kim
通讯作者：
Hai-Bo Yuan;Hong-Cheol Na;Youngbae Kim
影响因子：
7.3
作者：
Qing Guo;Jingmin Yin;Tian Yu;D. Jiang
通讯作者：
Qing Guo;Jingmin Yin;Tian Yu;D. Jiang