Open AccessConvex model predictive control for collision avoidance
Author(s) -
Raković Saša V.,
Zhang Sixing,
Dai Li,
Hao Yanye,
Xia Yuanqing
Publication year - 2021
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/cth2.12121
Subject(s) - collision avoidance , model predictive control , mathematical optimization , collision , control theory (sociology) , computer science , stability (learning theory) , a priori and a posteriori , regular polygon , control (management) , convex optimization , quadratic programming , mathematics , artificial intelligence , machine learning , philosophy , geometry , computer security , epistemology
This manuscript proposes a model predictive control for collision avoidance for the regulation problem of deterministic linear systems, which provides a priori guarantees of strong system theoretic properties, such as positive invariance and asymptotic stability, and high computational efficiency. Notion of safe distance sets is introduced, and also utilized as a novel approach to ensure collision avoidance via suitably defined convex constraints. The proposed convex model predictive control for collision avoidance is obtained by employing interactive strategic‐tactical structure for overall decision‐making. The strategic stage of the overall algorithm employs direct algebraic manipulations in order to construct safe distance sets that ensure collision avoidance. The tactical stage of the overall algorithm employs strictly convex quadratic programs for the optimization of local finite horizon predicted control processes. The dynamically compatible interaction of strategic and tactical stages of the overall algorithm is ensured by construction, which guarantees structural and computational benefits. These novel and unique features effectively enable both real time implementation and real life utilization of model predictive control for collision avoidance.