Abstract
This paper studies the stabilization problem for the non-diagonal inertia and damping matrices underactuated unmanned surface vessels (USVs) in the presence of unknown time varying environment disturbances and state constraints. In this framework, we first convert the mathematical model into a form of two subsystems, which is easier amenable for stabilization, by applying several transformations. It is proved that the investigated problem can be reduced to one of the second subsystem. A novel time-varying state feedback control scheme based on backstepping method and prescribed Lyapunov function is proposed to ensure state constraints and guarantee the global stability of the reduced control system, as well as sufficient conditions to ensure controller feasibility are given. With adaptive neural networks (NNs), the controller can be easily extended to compensate uncertainty induced by unknown time-varying external disturbances (e.g., wind, waves, and currents). It is proved that all the states and stabilization functions in the overall closed-loop are globally uniformly bounded. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.
Highlights
Underactuated unmanned surface vessels (USVs) can be used to deploy various missions related to drilling, pipe laying, diving support [1], dynamic positioning [2], [3] etc., autonomously
Since Brockett,s necessary condition cannot be satisfied, the USVs cannot be stabilized by any smooth static state feedback [4]
In [29], an adaptive neural network control based on disturbance observers was developed to guarantee transient and steady-state tracking performance
Summary
Underactuated USVs can be used to deploy various missions related to drilling, pipe laying, diving support [1], dynamic positioning [2], [3] etc., autonomously. Considering fully actuated system, an adaptive controller based on time-varying barrier Lyapunov function was proposed in [23], which can ensure output constraint satisfaction and achieve asymptotically tracking. In [29], an adaptive neural network control based on disturbance observers was developed to guarantee transient and steady-state tracking performance. Disturbances and guarantee the system stability, we derive several transformations to convert the USVs system into a cascade system, and apply NN to approximate the unknown disturbance online, and borrow BLF to constrain the transient and steady-state performance. The control objective is to design robust control laws τu and τr which can guarantee the global uniform asymptotic convergence and prescribed transient performance regardless of the unknown time-varying external disturbances for the non-diagonal underactuated USVs. Fig. illustrates the control structure of this paper
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