Extreme learning‐based non‐linear model predictive controller for an autonomous underwater vehicle: simulation and experimental results. Issue 2 (8th July 2019)

• Record Type: Journal Article
• Authors: Rath, Biranchi Narayan; Subudhi, Bidyadhar
• Is Part Of: IET cyber-systems and robotics. Volume 1:Issue 2(2019)
• Page Start: 45
• Page End: 54
• Publication Date: 2019-07-08
• Subjects: adaptive control -- self‐adjusting systems -- state feedback -- control system synthesis -- nonlinear control systems -- three‐term control -- predictive control -- H∞ control -- learning systems -- autonomous underwater vehicles -- optimisation -- vehicle dynamics -- path planning -- control nonlinearities; NMPC approach -- AUV -- ELM structure -- control algorithms -- inverse optimal self‐tuning proportional–integral–derivative controller -- inverse optimal self‐tuning PID controller -- autonomous underwater vehicle -- kinematic controller design -- dynamic controller design -- extreme learning‐based nonlinear model predictive controller -- H∞ state feedback controller -- horizontal way‐points -- back‐stepping approach -- extreme learning machine structure -- Jaya optimisation algorithm -- rudder planes -- Matlab; Robotics -- Periodicals; Cybernetics -- Periodicals; Cybernetics; Robotics; Periodicals; 629
• DOI: 10.1049/iet-csr.2019.0014
• Languages: English
• ISSNs: 2631-6315

Abstract

Abstract : In this study, an extreme learning‐based non‐linear model predictive controller (NMPC) is proposed for path following planning of an autonomous underwater vehicle (AUV) using horizontal way‐points. The proposed controller comprises a kinematic controller and a dynamic controller. The kinematic controller is designed by using back‐stepping approach whilst the dynamic controller is designed by employing the NMPC approach. The dynamics of the AUV is identified in real‐time by employing an extreme learning machine (ELM) structure. In view of achieving improved performance of the ELM structure, its hidden layer parameters are optimally determined by applying Jaya optimisation algorithm. The resulting ELM model is then used to design a NMPC considering the constraint on rudder planes. The tracking performance of the proposed controller is compared with that of two recently reported control algorithms namely, H ∞ state feedback controller and inverse optimal self‐tuning proportional–integral–derivative (PID) controller. The proposed controller is implemented using MATLAB and then in real‐time on a prototype AUV developed in the authors' laboratory. From both the simulation and experimental results obtained, it is observed that the proposed controller exhibits superior tracking performance compared to both H ∞ state feedback controller and inverse optimal self‐tuning PID controller.