Dynamics control algorithm of autonomous underwater vehicle by reinforcement learning and teaching method considering thruster failure under severe disturbance

A training algorithm for dynamics control of nonholonomic AUV (autonomous underwater vehicle) is proposed in this paper which can recover from thruster failure during cruising mission. It is based on Q-learning and teaching method. The back up data that represents dynamics model expressed in the form of Bayesian net can be used effectively in this case. In order to overcome difficulties due to, making discrete expression of continuous state space of AUV, the algorithm uses multiresolution Q-value tables which is combined in the form of subsumption architecture. Simulation results show high performance of the proposed algorithm for a vertical ascent mission in a severe current condition. It is shown that AUV users can conveniently and quickly train the control algorithm of the AUV by using simulation of dynamics of the vehicle.