GPU-Accelerated PD-IPM for Real-Time Model Predictive Control in Integrated Missile Guidance and Control Systemsopen access
- Authors
- Lee, Sanghyeon; Lee, Heoncheol; Kim, Yunyoung; Kim, Jaehyun; Choi, Wonseok
- Issue Date
- Jun-2022
- Publisher
- MDPI
- Keywords
- graphics processing unit; primal-dual interior point method; model predictive control; real-time systems; integrated missile guidance and control
- Citation
- SENSORS, v.22, no.12
- Journal Title
- SENSORS
- Volume
- 22
- Number
- 12
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/26059
- DOI
- 10.3390/s22124512
- ISSN
- 1424-8220
1424-3210
- Abstract
- This paper addresses the problem of real-time model predictive control (MPC) in the integrated guidance and control (IGC) of missile systems. When the primal-dual interior point method (PD-IPM), which is a convex optimization method, is used as an optimization solution for the MPC, the real-time performance of PD-IPM degenerates due to the elevated computation time in checking the Karush-Kuhn-Tucker (KKT) conditions in PD-IPM. This paper proposes a graphics processing unit (GPU)-based method to parallelize and accelerate PD-IPM for real-time MPC. The real-time performance of the proposed method was tested and analyzed on a widely-used embedded system. The comparison results with the conventional PD-IPM and other methods showed that the proposed method improved the real-time performance by reducing the computation time significantly.
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