Model-free adaptive control method with variable forgetting factor for unmanned surface vehicle control

We present a new type of model-free adaptive control (MFAC) method based on an adaptive forgetting factor for unmanned surface vehicle (USV) heading control under uncertain influence. Firstly, we analyze the compact form dynamic linearization based MFAC (CFDL-MFAC) method and its main problems with regard to USV heading control. Secondly, in order to address the problems of overshoot, oscillation, and slow convergence of the heading control with the MFAC method and considering the dynamics of the USV heading control subsystem, we introduce an adaptive forgetting factor into the CFDL-MFAC to arrive at the CFDL-MFAC with variable forgetting factor (CFDL-MFAC-VFF) method. Our simulation studies show that the CFDL-MFAC-VFF method yields low overshoot and low oscillations and is insensitive to changes in the system parameters and output error. Finally, our field experiments with the small USV “Dolphin-I” demonstrate the effectiveness and engineering practicability of our proposed method.