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

2024-11-10

arXiv.org

AI Summary

To address the susceptibility of fully actuated robot controllers to disturbances and the difficulty in simultaneously ensuring stability and high performance, this paper proposes a novel controller design framework integrating virtual mechanism modeling with passivity theory. Methodologically, the control law is physically modeled as a virtual mechanism, whose intrinsic passivity guarantees closed-loop stability; concurrently, a rigid-body dynamics ordinary differential equation model is constructed, and automatic differentiation enables end-to-end optimal tuning of controller parameters. The key contribution lies in the first deep coupling of virtual mechanism modeling with passivity analysis—overcoming the empirical parameter-tuning bottleneck inherent in conventional passive control—and achieving a unified design where stability is formally provable and performance is systematically optimizable. Simulation results demonstrate significant improvements in trajectory tracking accuracy and energy efficiency.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Mechanism Design

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

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.

Problem

• Robust Control
• Optimal Control
• Robot Stability

Innovation

• Virtual Mechanism
• Algorithmic Differentiation
• Stability and Efficiency