Scheduled Composite Off-Line Output Feedback Model Predictive Control for a Constrained Hypersonic Vehicle Using Polyhedral Invariant Sets
Publication: Journal of Aerospace Engineering
Volume 31, Issue 4
Abstract
This paper presents a scheduled composite off-line output feedback model predictive control strategy for a constrained hypersonic vehicle (HV) in the presence of external persistent disturbances. First, multiple linear time invariant (LTI) models are constructed to represent the nominal longitudinal dynamics of HV without external persistent disturbances. Then, by combining the construction of asymptotically stable polyhedral invariant sets for explicitly handling asymmetric constraints of control inputs and angle of attack and the state estimator for estimating partial unmeasured states, a set of local off-line output feedback model predictive control schemes are first developed for multiple LTI models. Additionally, based on the strong nonlinear approximation ability of a recurrent cerebellar model articulation controller (RCMAC), a RCMAC disturbance observer (RCMACDO) is presented to estimate the actual disturbances, and then an auxiliary compensation controller is appended to attenuate the influences of external persistent disturbances. Furthermore, by designing a proper scheduling strategy, the proposed control strategy with the overlapped stable regions is proposed for the wide tracking task. Finally, the comparative simulation results for tracking reference commands of velocity and altitude verify the effectiveness of the proposed control strategy.
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Acknowledgments
The authors would like to express their sincere gratitude for the support of the National Natural Science Foundation of China (Grant Nos. 61463029 and 61308120).
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Published In
Journal of Aerospace Engineering
Volume 31 • Issue 4 • July 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Jul 20, 2016
Accepted: Nov 30, 2017
Published online: Apr 18, 2018
Published in print: Jul 1, 2018
Discussion open until: Sep 18, 2018
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