Benchmarking Force Control Algorithms
Rudy Vicario, Andrea Calanca, Eldison Dimo, Noè Murr, Matteo Meneghetti, Rafael Medina Ferro, Enrico Sartori, Thiago Boaventura
- 发表年份
- 2021
- 引用次数
- 10
摘要
Force control is nowadays a mature technology and it is becoming more and more common in robotic applications. A specific and significant example are robotic devices for rehabilitation. Despite this, an effective standardized and comprehensive method to assess force control performance does not exist. Defining the performance of a force-controlled system is not a trivial task, and one of the reasons is that performance does not only depend on the dynamics of the robot, but also on the dynamics of the environment, which are usually uncertain and time-varying. Exerting a force on a soft environment is different from exerting a force on a rigid environment. Indeed, the same force-controlled robot can have different force responses in different environments. This paper proposes a methodology to standardize the benchmarking of force control algorithms applied to different actuation architectures and considering a wide set of possible environments and disturbances, defined by the application of interest.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991