Model Predictive Convex Programming for Constrained Vehicle Guidance

• Published in: IEEE transactions on aerospace and electronic systems Vol. 55; no. 5; pp. 2487 - 2500
• Main Authors: Hong, Haichao, Maity, Arnab, Holzapfel, Florian, Tang, Shengjing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TAES.2018.2890375

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.