Predictive guidance algorithms for maximal downrange maneuvrability with application to low-lift re-entry |
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| Authors: | Alexander I Kozynchenko |
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| Institution: | 1. Centre for Sports and Exercise Medicine, Barts and the London School of Medicine and Dentistry, Mile End Hospital, London E1 4DG, UK;2. Pathology Group, Institute of Cell and Molecular Science, Barts and the London School of Medicine and Dentistry, Queen Mary, University of London, E1 2ES, UK;1. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia;2. Department of Electronic and Electrical Engineering, Ladoke Akintola University of Technology, P.M.B 4000, Ogbomoso, Nigeria;3. Department of Electrical and Electronic Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia;1. Department of Pediatrics, Dongguk University Ilsan Hospital, Goyang, Republic of Korea;2. Department of Pediatrics, Seoul National University College of Medicine, Seoul, Republic of Korea;1. School of Astronautics, Beihang University, Beijing 100191, China;2. Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, CA 92697, USA;3. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China |
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| Abstract: | The paper concerns the general problem of a bounded final state control of non-linear dynamic systems with reference to near-optimal predictive guidance for low lift-to-drag ratio re-entry vehicles. More specifically, it addresses deriving guidance strategies capable to provide a maximal downrange maneuvrability for a maximal remaining flight time. Such robust, “guaranteed”, or assured, guidance keeps the remaining range-to-go to be coincident with the center of instant attainability domain. The paper discusses the existing guaranteed guidance strategy, and presents more general approach that provides an on-board planning of the entry trajectory, thus giving future state and control profiles. As a consequence the proposed guidance law is able to satisfy not only specified terminal conditions but also typical inequality constraints such as the maximal load factor and heat load. Computer simulations show that the algorithm can generate the feasible trajectories with equal downrange margins, using simple two-parametric families of control functions. |
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