An Adaptive Neural Network Fuzzy Sliding Mode Controller for Tracking Control of Deep-Sea Mining Vehicles - Shidong Wang; Zida Shan; Jialuan Xiao; Junjun Cao; He Zhang; Nan Sun (Journal of Marine Science and Engineering)
Traditional track-driven deep-sea nodule mining solutions significantly disrupt seabed ecosystems, making them unsuitable for commercial application. In contrast, ROV-like alternatives, such as the hovering mining vehicle, or HMV, offer substantial improvement in this regard and are deemed to be a viable way forward. This paper proposes an adaptive neural network fuzzy sliding mode controller architecture for the underwater trajectory tracking of HMV. The algorithm, named the Adaptive Radial Basis Function Neural Network Fuzzy Sliding Mode Controller (ARFSMC), replaces modeled vehicle dynamics with a radial basis function neural network (RBFNN). To enhance disturbance rejection, an adaptive mechanism is applied to the RBFNN output weighting matrix. Additionally, a fuzzy inference system (FIS) is implemented as the switching term, replacing the traditional signum function, to reduce high-frequency oscillations in the control signal. The stability of the algorithm under unknown external disturbance was confirmed via Lyapunov stability analysis. To validate the ARFSMC’s performance, three numerical simulation cases were conducted, each designed to reflect an expected operation scenario of the HMV, through which the tracking performance of the ARFSMC under time-varying system inertia is validated and benchmarked against conventional sliding mode control (CSMC) and double-loop sliding mode control (DSMC). The simulation results confirm that comparing the above two controllers, the root mean square error (RMSE) of the ARFSMC is reduced by 15.0% and 11.4%, respectively. And when comparing the CSMC, the chattering is reduced by 97.8%. Both indicate their high robustness and superior performance in tracking control. The controller development and numerical validation in this work are aimed at the trajectory tracking challenge of the HMV in deep-sea mining operation. The dynamical modeling of the vehicle is based on parameters of the HaiMa ROV. External disturbance from currents were considered as sinusoidal functions modified with random noise.