Data-Driven Predictive Control for Autonomous Systems

Posted: 7 Feb 2019

See all articles by Ugo Rosolia

Ugo Rosolia

University of California, Berkeley - Department of Mechanical Engineering

Xiaojing Zhang

University of California, Berkeley - Department of Mechanical Engineering

Francesco Borrelli

University of California, Berkeley - Department of Mechanical Engineering

Date Written: May 2018

Abstract

In autonomous systems, the ability to make forecasts and cope with uncertain predictions is synonymous with intelligence. Model predictive control (MPC) is an established control methodology that systematically uses forecasts to compute real-time optimal control decisions. In MPC, at each time step an optimization problem is solved over a moving horizon. The objective is to find a control policy that minimizes a predicted performance index while satisfying operating constraints. Uncertainty in MPC is handled by optimizing over multiple uncertain forecasts. In this case, performance index and operating constraints take the form of functions defined over a probability space, and the resulting technique is called stochastic MPC. Our research over the past 10 years has focused on predictive control design methods that systematically handle uncertain forecasts in autonomous and semiautonomous systems. In the first part of this article, we present an overview of the approach we use, its main advantages, and its challenges. In the second part, we present our most recent results on data-driven predictive control. We show how to use data to efficiently formulate stochastic MPC problems and autonomously improve performance in repetitive tasks. The proposed framework is able to handle a large set of predicted scenarios in real time and learn from historical data.

Suggested Citation

Rosolia, Ugo and Zhang, Xiaojing and Borrelli, Francesco, Data-Driven Predictive Control for Autonomous Systems (May 2018). Annual Review of Control, Robotics, & Autonomous Systems, Vol, 1, pp. 259-286, 2018, Available at SSRN: https://ssrn.com/abstract=3330469 or http://dx.doi.org/10.1146/annurev-control-060117-105215

Ugo Rosolia (Contact Author)

University of California, Berkeley - Department of Mechanical Engineering ( email )

Berkeley, CA 94720-1740
United States

Xiaojing Zhang

University of California, Berkeley - Department of Mechanical Engineering ( email )

Berkeley, CA 94720-1740
United States

Francesco Borrelli

University of California, Berkeley - Department of Mechanical Engineering ( email )

Berkeley, CA 94720-1740
United States

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