SLIDING MODE CONTROL WITH RBFNN COMPENSATION FOR DYNAMIC POSITIONING OF SHIP WITH DISTURBANCES AND INPUT SATURATION

This paper proposed a sliding mode controller with radial basis function neural network (RBFNN) compensation for dynamic positioning (DP) ships to steer ship to desired position under the problems of external environmental disturbances and input saturation. Using RBFNN to compensate the uncertainties in input saturation, the sliding mode control (SMC) law is designed under the disturbances, and then, the stability of the designed control law is proved uniform asymptotic stability on the basis of Laypunov stability theory. To validate the performance of designed control scheme, three groups of simulation results of the proposed control law compared to proportion integration differentiation (PID) control law are demonstrated to verify the effectiveness of the proposed control law.