Overshoot Reduction Using Adaptive Neuro-Fuzzy Inference System for an Autonomous Underwater Vehicle

In this paper, an adaptive depth and heading control of an autonomous underwater vehicle using the concept of an adaptive neuro-fuzzy inference system (ANFIS) is designed. The autonomous underwater vehicle dynamics have six degrees of freedom, which are highly nonlinear and time-varying. It is affected by environmental effects such as ocean currents and tidal waves. Due to nonlinear dynamics designing, a stable controller in an autonomous underwater vehicle is a difficult end to achieve. Fuzzy logic and neural network control blocks make up the proposed control design to control the depth and heading angle of autonomous underwater vehicle. The neural network is trained using the back-propagation algorithm. In the presence of noise and parameter variation, the proposed adaptive controller's performance is compared with that of the self-tuning fuzzy-PID and fuzzy logic controller. Simulations are conducted to obtain the performance of both controller models in terms of overshoot, and the rise time and the result of the proposed adaptive controller exhibit superior control performance and can eliminate the effect of uncertainty.

Automated summary

In this paper, an adaptive depth and heading control of an autonomous underwater vehicle using the concept of an adaptive neuro-fuzzy inference system (ANFIS) is designed. The proposed control design combines fuzzy logic and neural network control blocks to control the depth and heading angle of the vehicle. Simulations show that the proposed adaptive controller exhibits superior performance compared to other control methods.