Optimal Virtual Model Control for Robotics: Design and Tuning of Passivity-Based Controllers

TL;DR

This paper introduces an intuitive and optimal tuning method for passivity-based control in robotics, leveraging virtual mechanisms and algorithmic differentiation to enhance stability and performance in fully actuated robots.

Contribution

It presents a novel design and tuning framework for passivity-based controllers using virtual mechanisms and algorithmic differentiation, improving control performance and stability.

Findings

Control behavior aligns with physical principles.

Optimal tuning enhances stability and performance.

Framework applicable to fully actuated robots.

Abstract

Passivity-based control is a cornerstone of control theory and an established design approach in robotics. Its strength is based on the passivity theorem, which provides a powerful interconnection framework for robotics. However, the design of passivity-based controllers and their optimal tuning remain challenging. We propose here an intuitive design approach for fully actuated robots, where the control action is determined by a `virtual-mechanism' as in classical virtual model control. The result is a robot whose controlled behavior can be understood in terms of physics. We achieve optimal tuning by applying algorithmic differentiation to ODE simulations of the rigid body dynamics. Overall, this leads to a flexible design and optimization approach: stability is proven by passivity of the virtual mechanism, while performance is obtained by optimization using algorithmic differentiation.