Adaptive neural network anti-disturbance control of a cable-driven flexible-joint-based underwater vehicle manipulator system with dead-zone input nonlinearities via multiple neural observers

Abstract

To achieve the requirements of lightweight, low energy consumption, and low inertia of an underwater vehicle manipulator system, a cable-driven manipulator is installed on the underwater vehicle to form a cable-driven flexible-joint-based underwater vehicle manipulator system (CDFJ–UVMS). The CDFJ–UVMS is a complex nonlinear system subject to model uncertainties, complex marine environment disturbances, and actuator dead-zone nonlinearity. To design track controllers, the CDFJ–UVMS dynamics is divided into two parts: known and unknown. Subsequently, a radial basis function neural network is adopted to approximate the unknown nonlinearity. A neural network performance observer is constructed, whose estimation error is then used to design a novel neural disturbance observer (NDO) to estimate the total disturbance. Finally, an adaptive neural network control method is proposed for the CDFJ–UVMS based on the NDO, neural network compensator, and neural performance observer. The stability of the closed-loop system is analyzed using the Lyapunov method. The proposed control algorithm is applied to a CDFJ–UVMS with two cable-driven joints and compared with other control methods to show the effectiveness of the proposed control algorithm.

Keywords

Adaptive neural network control / Cable-driven flexible-joint-based underwater vehicle manipulator system (CDFJ–UVMS) / Dead-zone input nonlinearity / Neural distirubance observer (NDO) / Neural network performance observer / Radial basis function (RBF) neural network

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Funding

Natural Science Foundation of Shandong Province(ZR2021 MF119)

Ajman University Internal Research(2022-IRG-ENIT-15)