Model predictive control of autonomous underwater vehicles based on the simplified dual neural network

Publisher:
IEEE, Institute of Electrical and Electronics Engineers
Publication Type:
Conference Proceeding
Citation:
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2012, pp. 2551 - 2556
Issue Date:
2012-12-01
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Based on a recurrent neural network, a model predictive control (MPC) method for control of a class of autonomous underwater vehicles (AUVs) is presented. A coupled nonlinear kinematic model with constrains is considered. The model predictive control problem of AUVs is formulated as a time-varying quadratic programming problem, and a one-layer recurrent neural network called the simplified dual network is applied for real-time optimization. It is able to converge to the global optimal solution of the constrained optimization problem. Simulation results are discussed to demonstrate the effectiveness and characteristics of the proposed model predictive control method. © 2012 IEEE.
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