A novel adaptive second-order sliding mode controller for autonomous underwater vehicles

Navigating, directing, and controlling autonomous underwater vehicles (AUVs) are demanding and considered complicated compared to the autonomous surface-level performance. In such vehicles, the motion can be controlled depending on the estimation of indefinite hydrodynamic forces and the disturbances that occurs in this vehicle in the underwater background. In this article, Gray Wolf optimization (GWO) is performed along with the second-order sliding mode control (GW-SoSMC) approach for controlling the yaw angle in AUV. The main purpose of the article is to reduce the error that occurs in the system between the controlled signal and desired signal corresponding to yaw angle. Using this proposed model, both the occurrence of chattering and the controlling performance of AUV system can be diminished. Moreover, the proposed model is compared with the existing approaches like, FireFly-SoSMC (FF-SoSMC), Genetic Algorithm-SoSMC (GA-SoSMC), Gray Wolf-SMC (GW-SMC), Group search optimization-SoSMC (GSO-SoSMC) and Artificial Bee Colony-SoSMC (ABC-SoSMC). From the simulation results, it shows that the performance of the implemented technique in terms of steady-state response, error analysis, yaw angle analysis, and controller response is enhanced while comparing with the existing approaches.