Abstract
For collision avoidance and maneuvering control in bridge areas, an adaptive fractional sliding mode control with fractional recurrent neural network (FRNN-AFSMC) is proposed. The uncertainties are estimated by FRNN, and the fractional gradient is adopted to improve the recurrent neural network (RNN). Its convergence has been proven. The influence of fractional order on algorithm performance is analyzed, and the simulation platform of ship collision avoidance control is built. Dynamic collision avoidance of multiple ships is simulated and verified. The results show the feasibility and effectiveness of dynamic autonomous collision avoidance motion control in a dynamic ocean environment.
Highlights
Collision avoidance and maneuvering control in bridge areas are important for the autonomous intelligent navigation in ships
Li discussed the unsteady hydrodynamic loads of ships passing near bridge piers [2]
FRNN Algorithm. e theoretical basis of gradient descent method is the concept of gradient. e relationship between gradient and directional derivative is as follows: the direction of gradient is consistent with the direction of obtaining the maximum value of directional derivative, and the modulus of gradient is the maximum value of directional derivative of function at this point. e weight is updated using the gradient method: wkj(t + 1) wkj(t) − ηDαwkj L(t), (24)
Summary
Collision avoidance and maneuvering control in bridge areas are important for the autonomous intelligent navigation in ships. Fei introduced a fractional-order sliding mode control method based on recursive neural network approximation [7]. To improve the output power quality of a permanent magnet synchronous generator, Xiong proposed a fractional-order sliding mode control method [8]. Sharafian proposed a new fractional-order observer based on a sliding mode method and radial basis function neural network to distinguish the uncertainty of a fractional-order human immunodeficiency virus mathematical dynamic model [9]. Moezi proposed a new adaptive interval type 2 fuzzy fractional backstepping sliding mode control method [11]. (1) Fractional calculus is adopted to improve RNN for approximating unknown parameters of ship model and environmental disturbance (2) FRNN-AFSMC is designed, and its convergence is proven (3) e proposed algorithm is used for collision avoidance of underactuated ships through course alterations
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