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.
Model Predictive Convex Programming for Constrained Vehicle Guidance
IEEE Transactions on Aerospace and Electronic Systems ; 55 , 5 ; 2487-2500
2019-10-01
4093883 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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