Combined neural network and PD adaptive tracking controller for ship steering system

Abstract EN

In this paper, a combined RBF neural network sliding mode control and PD adaptive tracking controller is proposed for controlling the directional heading course of a ship.

Due to the high nonlinearity and uncertainty of the ship dynamics as well as the effect of wave disturbances a performance evaluation and ship controller design is stay difficult task.

The Neural network used for adaptively learn the uncertain dynamics bounds of the ship and their output used as part of the control law moreover the PD term is used to reduce the effect of the approximation error inherited in the RBF networks.

The stability of the system with the combined control law guaranteed through Lyapunov analysis.

Numeric simulation results confirm the proposed controller provide good system stability and convergence.