控制理论(社会学)
工作区
控制器(灌溉)
水力机械
理论(学习稳定性)
鉴定(生物学)
系统标识
液压缸
控制工程
最小二乘函数近似
计算机科学
工程类
机器人
数学
控制(管理)
人工智能
数据建模
机械工程
统计
生物
机器学习
数据库
估计员
植物
农学
作者
Junhui Zhang,Zhang Fu,Min Cheng,Ruqi Ding,Bing Xu,Huaizhi Zong
标识
DOI:10.1109/tie.2023.3250753
摘要
In the challenging applications of hydraulic robots, control performance in the full workspace is required for successful task completion. The model-based controller of the hydraulic manipulator is an effective means to improve control performance. However, most of the existing identification methods rely on least squares or weighted least squares, which may lead to the physically infeasible parameters. Although the physical feasibility of inertial parameters can be solved by the linear matrix inequality, physical feasibility and prior knowledge constraints of hydraulic parameters, such as bulk modulus and valve port coefficients, have not been considered in the identification process. Parameters that do not satisfy constraints will affect system stability in the model-based controller. In this article, an identification framework is proposed, which integrates inertial, friction, and hydraulic parameters physical feasibility. Compared with the nonidentified parameters, the ratio of tracking error to maximum velocity with the proposed method is reduced by 50%–56%. Compared with the least squares identification, the control stability in the full workspace is guaranteed. The proposed method is applicable to serial hydraulic manipulators with arbitrary degrees of freedom and is supported by experimental analysis of a hydraulic manipulator.
科研通智能强力驱动
Strongly Powered by AbleSci AI