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Kernel-based predictive control allocation for a class of thrust vectoring systems with singular points
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DOI：10.1016/j.automatica.2025.112270.png)
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Abstract
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This paper considers a class of thrust vectoring systems, which are nonlinear, overactuated, and timeinvariant. We assume that the system is composed of two subsystems and there exist singular points around which the linearized system is uncontrollable. Furthermore, we assume that the system is stabilizable through a two-level control allocation. In this particular setting, we cannot do much with the linearized system, and a direct nonlinear control approach must be used to analyze the system stability. Under adequate assumptions and a suitable nonlinear continuous control-allocation law, we can prove uniform asymptotic convergence of the points of equilibrium using Lyapunov input-tostate stability and the small gain theorem. This control allocation, however, requires the design of an allocated mapping and introduces two exogenous inputs. In particular, the closed-loop system is cascaded, and the output of one subsystem is the disturbance of the other, and vice versa. In general, it is difficult to find a closed-form solution for the allocated mapping; it needs to satisfy restrictive conditions, among which Lipschitz continuity to ensure that the disturbances eventually vanish. Additionally, this mapping is in general nontrivial and non-unique. In this paper, we propose a new kernel-based predictive control allocation to substitute the need for designing an analytic mapping, and assess if it can produce a meaningful mapping on-the-fly by solving online an optimization problem. The simulations include two examples, which are the manipulation of an object through an unmanned aerial vehicle in three dimensions, and the control of a surface vessel actuated by two azimuthal thrusters. (c) 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords:
Control allocation
Model predictive control
Application of nonlinear analysis and design
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