Autotune control algorithm based on relay feedback and adaptive neural network for attitude tracking of nonlinear AUG system

Due to the complexity and uncertainty of the nonlinear autonomous underwater glider (AUG) system, the control algorithms for attitude tracking of the AUG system are very difficult to directly design. In this paper, a novel autotuning control algorithm (ATCA) based on relay feedback and adaptive neural network is proposed to effectively implement the attitude tracking of the AUG system. The proposed algorithm only utilizes the online input/output (I/O) data to achieve the AUG system attitude control, ignoring the mathematical system model. The ATCA control parameters are initialized by relay feedback and adjusted online based on gradient descent algorithm with the partial derivative of the AUG system provided by adaptive neural network. Besides, in the ATCA, the fast adaptive learning factor is employed to make the AUG system respond quickly to the evolving reference trajectory. Furthermore, the complete stability of the closed-loop AUG system with the ATCA has been proven via the Lyapunov stability theory. The simulation studies illustrate the correctness the proposed algorithm. Compared with three popular data driven control algorithms, the proposed algorithm has superiority in terms of system response time, integral squared error (ISE) and integral absolute error (IAE). (c) 2014 xxxxxxxx. Hosting by Elsevier B.V. All rights reserved.