Neural Network Control of Buoyancy-Driven Autonomous Underwater Glider

  • Khalid IsaEmail author
  • M. R. Arshad
Part of the Studies in Computational Intelligence book series (SCI, volume 480)


This chapter presents a mathematical model and motion control analysis of a buoyancy-driven underwater glider. The glider mathematical model, which includes the presence of disturbance from the water currents, has been designed by using the Newton-Euler method. In order to predict and control the glider motion, a neural network control has been used as a model predictive control (MPC) as well as a gain tuning algorithm. The motion has been controlled by six control inputs: two forces of a sliding mass, a ballast pumping rate, and three velocities of water currents. The simulation results show the analysis of the motion control system for both neural network control approaches, and a comparison with the Linear Quadratic Regulator (LQR) controller is also included. The results show that the model is stable, and the neural network controller of MPC produced better control performance than the neural network gain tuner and the LQR, where the accuracy value of the MPC is 94.5 %.


Control Input Pitch Angle Model Predictive Control Linear Quadratic Regulator Neural Network Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author would like to thank the Malaysia Ministry of Higher Education (MOHE), ERGS-203/PELECT/6730045, Universiti Sains Malaysia (USM) and Universiti Tun Hussein Onn Malaysia (UTHM) for supporting the research.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Underwater Robotics Research Group (URRG)School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM)Pulau PinangMalaysia

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