Adaptive neuro fuzzy tracking control of UUV using sliding mode control theory based online learning algorithm
This paper presents the control of an Unmanned Underwater Vehicle(UUV) with five degrees of freedom by using an adaptive neuro fuzzy controller combined with a sliding mode control (SMC) theory based learning algorithm. The proposed control structure is composed of an adaptive fuzzy neural network(AFNN) and a conventional PD controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC theory based learning algorithm is able to not only decrease the steady state error but also improve the transient response performance of the UUV.
2016 12th World Congress on Intelligent Control and Automation (WCICA)