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- 저자 : Tapabrata Ray (검색결과 2 건)
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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>
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A Computationally Efficient Approach for NN Based System Identification of a Rotary Wing UAV
Mahendra Kumar Samal,Sreenatha Anavatti,Tapabrata Ray,Matthew Garratt 제어·로봇·시스템학회 2010 International Journal of Control, Automation, and Vol.8 No.4
Neural Network (NN) models based on autoregressive structures have long been used for nonlinear system identification problems. Their application for on-line implementations, however require them to be trained within a prescribed time span, which is often related to the sampling time of the system. In this paper, we introduce a NN model that is embedded with a dimensionality reduction mechanism in order to reduce the size of the network. The dimensionality reduction is based on Principal Component Analysis (PCA) and the resulting smaller NN trains faster. The longitudinal and lateral dynamics of a rotary wing Unmanned Aerial Vehicle (UAV) is modelled using flight test data. The re-sults of system identification, error statistics and training times are provided to highlight the benefits of the proposed approach for NN based system identification models.
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