On Optimal Input Design for Feed-forward Control

TL;DR

This paper develops a framework for designing minimal power excitation signals for system identification that ensure effective feed-forward control with specified output variance constraints.

Contribution

It introduces a novel optimal input design method tailored for feed-forward control, incorporating noise models and transfer functions, with analytical solutions and a numerical temperature control example.

Findings

Optimal input signals depend on noise and transfer functions.

Analytical solutions for low order models are provided.

Numerical evaluation demonstrates the framework's effectiveness.

Abstract

This paper considers optimal input design when the intended use of the identified model is to construct a feed-forward controller based on measurable disturbances. The objective is to find a minimum power excitation signal to be used in system identification experiment, such that the corresponding model-based feed-forward controller guarantees, with a given probability, that the variance of the output signal is within given specifications. To start with, some low order model problems are analytically solved and fundamental properties of the optimal input signal solution are presented. The optimal input signal contains feed-forward control and depends of the noise model and transfer function of the system in a specific way. Next, we show how to apply the partial correlation approach to closed loop optimal experiment design to the general feed-forward problem. A framework for optimal input signal design for feed-forward control is presented and numerically evaluated on a temperature control problem.