Load-independent Metrics for Benchmarking Force Controllers
Torque-controlled actuators are critical components in mechatronic systems that closely interact with their environment, such as legged robots, collaborative manipulators, and exoskeletons. The performance and stability of these actuators depend not only on controller design and system dynamics but also significantly on load characteristics, which may include interactions with humans or unstructured environments. This load dependence highlights the need for frameworks that properly assess and compare torque controllers independent of specific loading conditions. In this short paper, we concisely present a modeling approach that captures the impact of load on the closed-loop dynamics of torque-controlled systems. Based on this model, we propose new methods and quantitative metrics, including the Passivity Index Interval, which blends passivity and small-gain theory to offer a less conservative measure of coupled stability than passivity alone. These metrics can be used alongside traditional control performance indicators, such as settling time and bandwidth, to provide a more comprehensive characterization of torque-controlled systems. We demonstrate the application of the proposed metrics through experimental comparisons of linear actuator force controllers.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingSimilar Papers 제목 키워드 기반
Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fields
AI for science (AI4S) is an emerging research field that aims to enhance the accuracy and speed of scientific computing tasks using machine learning methods. Traditional AI benchmarking methods struggle to adapt to the u…
BenchmarkingConstrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN
In software-defined networking (SDN), the implementation of distributed SDN controllers, with each controller responsible for managing a specific sub-network or domain, plays a critical role in achieving a balance betwee…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Leveraging Port-Hamiltonian Theory for Impedance Control Benchmarking
This work proposes PH-based metrics for benchmarking impedance control. A causality-consistent PH model is introduced for mass-spring-damper impedance in Cartesian space. Based on this model, a differentiable, force-torq…
A ROS2 Benchmarking Framework for Hierarchical Control Strategies in Mobile Robots for Mediterranean Greenhouses
Mobile robots operating in agroindustrial environments, such as Mediterranean greenhouses, are subject to challenging conditions, including uneven terrain, variable friction, payload changes, and terrain slopes, all of w…
BenchCouncil's View on Benchmarking AI and Other Emerging Workloads
This paper outlines BenchCouncil's view on the challenges, rules, and vision of benchmarking modern workloads like Big Data, AI or machine learning, and Internet Services. We conclude the challenges of benchmarking moder…
Benchmarking