Passive nonlinear FIR filters for data-driven control

TL;DR

This paper introduces a new class of passive nonlinear FIR filters designed for data-driven control, enabling efficient synthesis and guaranteed passivity for physical system applications.

Contribution

It presents a novel construction of passive nonlinear FIR operators using lifted space filters, with a focus on control synthesis and passivity guarantees.

Findings

Efficient control synthesis via constrained optimization.

Passivity achieved through linear frequency domain constraints.

Suitable for electromechanical system control.

Abstract

We propose a new class of passive nonlinear finite impulse response operators. This class is constructed by the action of finite impulse response filters in a lifted space. This allows for efficient control synthesis through constrained optimization. Closed-loop performance is taken into account through least-squares fitting, based on the theory of virtual reference feedback tuning. Passivity is established through efficient linear constraints, based on sampling in the frequency domain. Because of passivity, this class of operators is particularly suited for the control of physical systems, such as electromechanical systems.