期刊
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
卷 30, 期 6, 页码 2450-2465出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2022.3154370
关键词
Hardware; Real-time systems; Tires; Computer architecture; Computational efficiency; Costs; System dynamics; Automotive control; gradient methods; nonlinear control systems; predictive control; trajectory optimization
Model predictive control (MPC) is a control strategy that can account for future response and complex control objectives. This study investigates the computational cost of solving nonlinear MPC problems and provides a framework for designing nonlinear MPC architectures compatible with real-time performance.
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.
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