Model Predictive Convex Programming for Constrained Vehicle Guidance

A new model predictive convex programming is proposed in this paper for state and input constrained vehicle guidance design. The proposed method defines a convex optimization framework considering a flexibly designed cost function subject to inequality constraints and a sensitivity relation between...

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Veröffentlicht in:IEEE transactions on aerospace and electronic systems Jg. 55; H. 5; S. 2487 - 2500
Hauptverfasser: Hong, Haichao, Maity, Arnab, Holzapfel, Florian, Tang, Shengjing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Aerodynamics
Aircraft guidance
Aircraft landing
Computational geometry
Computer simulation
constrained guidance
Constraints
Convex analysis
Convex functions
Convexity
Cost function
Mathematical programming
Missile control
missile guidance
model predictive convex programming (MPCP)
Optimization
Predictive models
Programming
Sensitivity
terrain constraints
ISSN:0018-9251, 1557-9603
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Zusammenfassung:A new model predictive convex programming is proposed in this paper for state and input constrained vehicle guidance design. The proposed method defines a convex optimization framework considering a flexibly designed cost function subject to inequality constraints and a sensitivity relation between state increments and input corrections. This formulated convex optimization problem can be solved in a computationally efficient manner. Simulation studies of nonlinear missile and aircraft landing guidance problems demonstrate the effectiveness of the proposed approach.
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ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2018.2890375