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

Autor:	Aarnoudse, Leontine, Oomen, Tom
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: To appear in Modeling, Estimation and Control Conference (MECC) 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.05139 Zobrazit plný text záznamu View this record from Arxiv