Title
Design for Real-Time Nonlinear Model Predictive Control With Application to Collision Imminent Steering
Abstract
Model predictive control (MPC) is a competitive option in modern control systems due to its ability to account for future response and incorporation of complex control objectives. As applications become more intricate, nonlinearities limit the utility of linear control strategies, thus requiring more sophisticated architectures, often at a significant computational cost. This article investigates the computational cost of solving nonlinear MPC problems and provides a framework for designing nonlinear MPC architectures compatible with real-time performance. To motivate the computational complexity associated with nonlinear MPC, the design of an automotive collision imminent steering system and the controller is considered. Various trajectory optimization strategies are examined and compared for this application, identifying multiple-shooting-based Runge–Kutta explicit integration as the most suitable. The control algorithm is then mapped into a graphics processor unit-based hardware system, where special considerations of the parallel hardware architecture are discussed. Compared to the single-shooting solution as the benchmark, multiple shootings on parallel hardware achieve three orders of magnitude improvement in wall time, supporting real-time implementation.
Year
DOI
Venue
2022
10.1109/TCST.2022.3154370
IEEE Transactions on Control Systems Technology
Keywords
DocType
Volume
Automotive control,gradient methods,nonlinear control systems,predictive control,trajectory optimization
Journal
30
Issue
ISSN
Citations 
6
1063-6536
0
PageRank 
References 
Authors
0.34
16
3
Name
Order
Citations
PageRank
John Wurts111.71
Jeffrey L. Stein215827.02
Tulga Ersal33315.63