GPU-Accelerated PD-IPM for Real-Time Model Predictive Control in Integrated Missile Guidance and Control Systems

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 per...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-06, Vol.22 (12), p.4512
Main Authors: Lee, Sanghyeon, Lee, Heoncheol, Kim, Yunyoung, Kim, Jaehyun, Choi, Wonseok
Format: Article
Language:English
Subjects:
Computing time
Construction
Control systems
Convex analysis
Design
Embedded systems
graphics processing unit
Graphics processing units
Guidance systems
integrated missile guidance and control
Kinematics
Methods
Missile control
Missile systems
model predictive control
Optimization algorithms
Optimization techniques
primal-dual interior point method
Real time
real-time systems
Sparsity
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22124512