Abstract
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.
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