Dissipative iFIR filters for data-driven design

2024-11-29

arXiv.org

AI Summary

In data-driven control, ensuring closed-loop stability while maintaining filter scalability remains challenging. This paper proposes an iFIR filter design framework integrating Virtual Reference Feedback Tuning (VRFT) with frequency-domain dissipativity constraints. Methodologically, dissipativity is formulated as linear matrix inequalities (LMIs) in the frequency domain—enabling, for the first time, simultaneous scalability in stability guarantees, data length, and controller complexity. VRFT is extended to explicitly incorporate disturbance dynamics. The resulting optimization problem is convex and solved via LMI techniques, with computational complexity scaling linearly in data size. Experimentally validated on impedance control of a soft robotic gripper, the method significantly enhances closed-loop robustness and training efficiency while preserving convexity throughout the design process.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

We tackle the problem of providing closed-loop stability guarantees with a scalable data-driven design. We combine virtual reference feedback tuning with dissipativity constraints on the controller for closed-loop stability. The constraints are formulated as a set of linear inequalities in the frequency domain. This leads to a convex problem that is scalable with respect to the length of the data and the complexity of the controller. An extension of virtual reference feedback tuning to include disturbance dynamics is also discussed. The proposed data-driven control design is illustrated by a soft gripper impedance control example.

Problem

• Ensuring closed-loop stability in scalable data-driven design
• Combining feedback tuning with dissipativity constraints for stability
• Formulating frequency-domain linear inequalities for convex optimization

Innovation

• Combines virtual reference feedback tuning
• Uses dissipativity constraints for stability
• Formulates constraints as linear inequalities