Model-based adaptive control system for autonomous underwater vehicles

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.

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