Adaptive predictive path following control based on least squares support vector machines for underactuated autonomous vessels

Since vessel dynamics could vary during maneuvering because of load changes, speed changing, environmental disturbances, aging of mechanism, etc., the performance of model-based path following control may be degraded if the controller uses the same motion model all the time. This article proposes an adaptive path following control method based on least squares support vector machines (LS-SVM) to deal with parameter changes of the motion model. The path following controller consists of two components: the online identification of varying parameters and model predictive control (MPC) using the adaptively identified models. For the online parameter identification, an improved online LS-SVM identification method is proposed based on weighted LS-SVM. Specifically, the objective function of LS-SVM is modified to decrease the errors of parameter estimation, an index is proposed to detect the possible model changes, which speeds up the rate of parameter convergence, and the sliding data window strategy is used to realize the online identification. MPC is combined with the line-of-sight guidance to track straight line reference paths. Finally, case studies are conducted to verify the effectiveness of the proposed path following adaptive controller. Typical parameter varying scenarios, such as rudder aging, current variations and changes of the maneuverability are considered. Simulation results show that the proposed method can handle the above situations effectively.