Open AccessExtreme learning‐based non‐linear model predictive controller for an autonomous underwater vehicle: simulation and experimental results
Author(s) -
Rath Biranchi Narayan,
Subudhi Bidyadhar
Publication year - 2019
Publication title -
iet cyber‐systems and robotics
Language(s) - English
Resource type - Journals
ISSN - 2631-6315
DOI - 10.1049/iet-csr.2019.0014
Subject(s) - control theory (sociology) , pid controller , controller (irrigation) , rudder , open loop controller , computer science , control engineering , matlab , kinematics , model predictive control , engineering , artificial intelligence , control (management) , temperature control , agronomy , physics , closed loop , classical mechanics , marine engineering , biology , operating system
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.