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Model-based adaptive control system for autonomous underwater vehicles
Hassanein, Osama,Anavatti, Sreenatha G.,Shim, Hyungbo,Ray, Tapabrata Elsevier 2016 Ocean engineering Vol.127 No.-
<P><B>Abstract</B></P> <P>The paper deals with the development of indirect adaptive controllers based on Hybrid Neuro-Fuzzy Network (HNFN) approach for Autonomous Underwater Vehicles (AUVs). The non-linear, coupled and time-varying dynamics of AUVs necessitates the development of adaptive controllers. The on-line identification and adaptation of the controller is carried out using the HNFN approach. The methodology uses the input-output data to come up with a structure for the controller and optimal adaptation of the parameters to achieve the required accuracy. The Semi-Serial-Parallel-Model is employed both for identification and control. Initial validation of the identification results are carried out numerically using a mathematical model. Hardware-in-loop (HIL) simulations are presented to validate the controller before carrying out the experiments. Experimental results show that the proposed controller is capable of suitably controlling the AUV in real environment and demonstrate its robust characteristics.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The paper deals with the development of intelligent controllers. </LI> <LI> Non-linear dynamic systems is used as the example to illustrate the system. </LI> <LI> Experimental results are provided to validate the algorithms. </LI> </UL> </P>
Attitude Dynamics Identification of Unmanned Aircraft Vehicle
Shaaban Ali Salman,Anavatti G. Sreenatha,Jin Young Choi 대한전기학회 2006 International Journal of Control, Automation, and Vol.4 No.6
The role of Unmanned Aircraft Vehicles (UAVs) has been increasing significantly in both military and civilian operations. Many complex systems, such as UAVs, are difficult to model accurately because they exhibit nonlinearity and show variations with time. Therefore, the control system must address the issues of uncertainty, nonlinearity, and complexity. Hence, identification of the mathematical model is an important process in controller design. In this paper, attitude dynamics identification of UAV is investigated. Using the flight data, nonlinear state space model for attitude dynamics of UAV is derived and verified. Real time simulation results show that the model dynamics match experimental data.