Dissipative iFIR filters for data-driven design

TL;DR

This paper introduces a scalable data-driven control design method using dissipative iFIR filters combined with virtual reference feedback tuning, ensuring closed-loop stability through convex linear inequality constraints, demonstrated on a soft gripper.

Contribution

It presents a novel approach integrating dissipativity constraints into virtual reference feedback tuning for scalable, data-driven control design with stability guarantees.

Findings

Convex formulation of stability constraints as linear inequalities.

Scalable control design with respect to data length and controller complexity.

Successful application to a soft gripper impedance control example.

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.