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A Constrained Predictive Controller for AUV and Computational Optimization Using Laguerre Functions in Unknown Environments

  • Control Theory and Applications
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Abstract

In this paper, a predictive controller approach is proposed for depth and steer control of an Autonomous Underwater Vehicle (AUV). The predictive controller is an advanced control technique that performs control in form of online at any sampling time. AUV control has a lot of complexity due to the coupled nonlinear dynamics, parametric uncertainty and external disturbances due to underwater conditions. In addition, the AUV in this paper has constraints on actuators, which make its control more complicated. One of the challenges against implementing of predictive controller is their computational burden and the time consuming control operations at each time step. In this research, the Laguerre orthogonal functions are used for the predictive controller design to optimize and educe computational burden in time interval. The designed controller has several advantages such as being online and optimized, high accuracy, implementation capability, interaction with the constraints and robustness to disturbances. In order to demonstrate the efficiency of the method, the proposed controller is simulated for the AUV and the calculation time of the controllers with and without the Laguerre functions is compared with each other. Using Laguerre functions, the simulation results and their implementation on the board show the favorable efficiency and effectiveness of the proposed controller. Additionally, we have compared the proposed method with the LQR method. The obtained results confirm the superiority of various predictive controller methods.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Malek-Ashtar University of Technology, Tehran, 15875-17740, Iran

    Ali Jabar Rashidi & Bahram Karimi

  2. Department of Electroceramics and Electrical Engineering, Malek-Ashtar University of Technology, Isfahan, 83145-11500, Iran

    Ayoub Khodaparast

Authors
  1. Ali Jabar Rashidi
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  2. Bahram Karimi
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  3. Ayoub Khodaparast
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Correspondence to Ali Jabar Rashidi.

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Ali Jabar Rashidi received his M.Sc. and Ph.D. degrees from the Tarbiat Modares University in Tehran, Iran, in 1997 and 2002, respectively, both in Communication System Engineering. He is currently an associate professor of electrical engineering department of Malek-Ashtar University of Technology, Tehran, Iran. He is engaged in research and teaching on information fusion, decentralized systems, multi-agent systems, distributed processing, and communications networks.

Bahram Karimi received his M.Sc. degree in control engineering from Isfahan University of Technology, Isfahan, Iran, in 2000 and his Ph.D. degree in control engineering from Amirkabir University of Technology, Tehran, Iran, in 2009. He is currently an associate professor of electrical engineering department of Malek-Ashtar University of Technology, Tehran, Iran. His research interests include large-scale systems, multiagent systems, adaptive control, nonlinear control, and intelligent systems.

Ayoub Khodaparast received his M.Sc. degree in mechatronics engineering from Malek-Ashtar University of Technology, Isfahan, Iran, in 2014. He is currently working toward a Ph.D. degree with the Department of Electrical Engineering, Malek-Ashtar University of Technology, Isfahan, Iran. His research interests include robotics, multi agent systems, nonlinear control, optimal control, unmanned vehicles control, and mechatronics.

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Rashidi, A.J., Karimi, B. & Khodaparast, A. A Constrained Predictive Controller for AUV and Computational Optimization Using Laguerre Functions in Unknown Environments. Int. J. Control Autom. Syst. 18, 753–767 (2020). https://doi.org/10.1007/s12555-018-0946-4

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  • DOI: https://doi.org/10.1007/s12555-018-0946-4

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