Robust adaptive self-organizing neuro-fuzzy tracking control of UUV with system uncertainties and unknown dead-zone nonlinearity

Abstract

In this paper, a robust adaptive self-organizing neuro-fuzzy control (RASNFC) scheme for tracking of unmanned underwater vehicle with uncertainties and the unknown dead-zone nonlinearity is proposed. The proposed RASNFC scheme comprises an estimation-based adaptive controller (EBAC) using a self-organizing neuro-fuzzy network (SNFN) and a robust controller. The EBAC controller is constructed with a novel sliding mode reaching law control framework, and the unknown dynamic function is identified by the SNFN approximator which is able to online self-construct a neuro-fuzzy network with dynamic structure by generating and pruning fuzzy rule. The robust controller is employed to provide the finite $$L_{2}$$ -gain property to cope with reconstruction errors such that the robustness of the entire closed-loop control system is enhanced. Theoretical analysis shows that tracking errors and their derivatives are asymptotically stable and all signals in the closed-loop system are bounded. Comparative simulation results demonstrate the effectiveness and superiority of the proposed RASNFC scheme.