Andrew Gray
University of California, Berkeley
13 Papers
53 Citations
Andrew Gray is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Model predictive control & Obstacle avoidance. The author has an hindex of 10, co-authored 13 publications.
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Papers
An auto-generated nonlinear MPC algorithm for real-time obstacle avoidance of ground vehicles
Janick V. Frasch,Andrew Gray,Mario Zanon,Hans Joachim Ferreau,Sebastian Sager,Francesco Borrelli,Moritz Diehl +6 more
- 17 Jul 2013
TL;DR: This work addresses the problem of real-time obstacle avoidance on low-friction road surfaces using spatial Nonlinear Model Predictive Control (NMPC) using a nonlinear four-wheel vehicle dynamics model that includes load transfer and proposes to use the ACADO Code Generation tool which generates NMPC algorithms based on the real- time iteration scheme for dynamic optimization.
A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles
TL;DR: In this article, a robust nonlinear model predictive controller (RNMPC) is used to help the driver navigating the vehicle in order to avoid obstacles and track the road centre line, and a robust invariant set is used in the RNMPC design to guarantee that state and input constraints are satisfied in the presence of disturbances and model error.
228
Predictive control for agile semi-autonomous ground vehicles using motion primitives
Andrew Gray,Yiqi Gao,Theresa Lin,J. Karl Hedrick,H. Eric Tseng,Francesco Borrelli +5 more
- 27 Jun 2012
TL;DR: This paper presents a hierarchical control framework for the obstacle avoidance of autonomous and semi-autonomous ground vehicles based on motion primitives created from a four-wheel nonlinear dynamic model.
225
Predictive control of an autonomous ground vehicle using an iterative linearization approach
Ashwin Carvalho,Yiqi Gao,Andrew Gray,H. Eric Tseng,Francesco Borrelli +4 more
- 01 Oct 2013
TL;DR: The focus of this work is on the development of a tailored algorithm for solving the nonlinear MPC problem and hardware-in-the-loop simulations show a reduction in the computational time as compared to general purpose nonlinear solvers.
162
Robust Predictive Control for semi-autonomous vehicles with an uncertain driver model
Andrew Gray,Yiqi Gao,J. Karl Hedrick,Francesco Borrelli +3 more
- 23 Jun 2013
TL;DR: A robust control design is proposed for the lane-keeping and obstacle avoidance of semiautonomous ground vehicles and a robust Model Predictive Controller is used in order to enforce safety constraints with minimal control intervention.
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