Robust data‐driven dynamic optimization using a set‐based gradient estimator

Author:

Kashani Ali1ORCID,Panahi Shirin1,Chakrabarty Ankush2,Danielson Claus1

Affiliation:

1. Department of Mechanical Engineering University of New Mexico Albuquerque New Mexico USA

2. Mitsubishi Electric Research Laboratories Cambridge Massachusetts USA

Abstract

AbstractThis article presents an extremum‐seeking control (ESC) algorithm for unmodeled nonlinear systems with known steady‐state gain and generally non‐convex cost functions with bounded curvature. The main contribution of this article is a novel gradient estimator, which uses a polyhedral set that characterizes all gradient estimates consistent with the collected data. The gradient estimator is posed as a quadratic program, which selects the gradient estimate that provides the best worst‐case convergence of the closed‐loop Lyapunov function. We show that the polyhedral‐based gradient estimator ensures the stability of the closed‐loop system formed by the plant and optimization algorithm. Furthermore, the estimated gradient provably produces the optimal robust convergence. We demonstrate our ESC controller through three benchmark examples and one practical example, which shows our ESC has fast and robust convergence to the optimal equilibrium.

Funder

National Science Foundation of Sri Lanka

Publisher

Wiley

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