混合绳索驱动机器人中基于力矩优化的冗余度求解与扰动抑制
Redundancy Resolution and Disturbance Rejection via Torque Optimization in Hybrid Cable-Driven Robots
AI总结：
针对混合绳索驱动机器人的冗余度求解与扰动抑制问题，提出两种基于关节空间力矩优化的新方法，其中TOAUJ方法可同时解决两类问题，经案例验证有效且可推广至同类机器人。
摘要：
本文针对混合绳索驱动机器人（Hybrid Cable-Driven Robots, HCDRs），提出了基于力矩优化的冗余度求解与扰动抑制方法。首先，本文构建了一款冗余HCDR，用于非线性全身系统建模与模型降阶。基于降阶后的动力学模型，提出两种求解冗余度问题的新方法：驱动关节关节空间力矩优化（TOAJ）以及驱动与非驱动关节联合的关节空间力矩优化（TOAUJ），二者均可推广至其他HCDR。与现有方法相比，本文提出的基于TOAUJ的方法是首个可同时解决HCDR冗余度求解与扰动抑制问题的方案。此外，本文还针对TOAJ与TOAUJ的实现开发了详细算法，并设计了一个简单有效的控制器用于生成数据的分析与验证。通过案例研究评估了TOAJ与TOAUJ的性能，结果表明上述方法均具备有效性。
英文摘要：
This paper presents redundancy resolution and disturbance rejection via torque optimization in Hybrid Cable-Driven Robots (HCDRs). To begin with, we initiate a redundant HCDR for nonlinear whole-body system modeling and model reduction. Based on the reduced dynamic model, two new methods are proposed to solve the redundancy resolution problem: joint-space torque optimization for actuated joints (TOAJ) and joint-space torque optimization for actuated and unactuated joints (TOAUJ), and they can be extended to other HCDRs. Compared to the existing approaches, this paper provides the first solution (TOAUJ-based method) for HCDRs that can solve the redundancy resolution problem as well as disturbance rejection. Additionally, this paper develops detailed algorithms targeting TOAJ and TOAUJ implementation. A simple yet effective controller is designed for generated data analysis and validation. Case studies are conducted to evaluate the performance of TOAJ and TOAUJ, and the results suggest the effectiveness of the aforementioned approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
