Redundancy Resolution and Disturbance Rejection via Torque Optimization in Hybrid Cable-Driven Robots

11/25/2020 ∙ by Ronghuai Qi, et al. ∙ 0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.