Automated MIMO Motion Feedforward Control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation

TL;DR

This paper introduces an efficient data-driven method for learning MIMO system feedforward control parameters using stochastic gradient descent, leveraging system experiments to estimate gradients with reduced experimental cost.

Contribution

It develops a novel approach that estimates gradients from single experiments for MIMO systems, improving convergence speed and efficiency over deterministic methods.

Findings

Superior convergence speed compared to deterministic methods

Single-experiment gradient estimation reduces experimental cost

Effective for large-scale MIMO systems

Abstract

Parameterized feedforward control is at the basis of many successful control applications with varying references. The aim of this paper is to develop an efficient data-driven approach to learn the feedforward parameters for MIMO systems. To this end, a cost criterion is minimized using a stochastic gradient descent algorithm, in which both the search direction and step size are determined through system experiments. In particular, the search direction is chosen as an unbiased estimate of the gradient which is obtained from a single experiment, regardless of the size of the MIMO system. The approach is illustrated using a simulation example, in which it is shown to be superior to a deterministic method in terms of convergence speed and thus experimental cost.