Three-dimensional neural network tracking control of autonomous underwater vehicles with input saturation

This paper addresses the problem of three-dimensional trajectory tracking control for underactuated autonomous underwater vehicles in the presence of parametric uncertainties, environmental disturbances and input saturation. First, a virtual guidance control strategy is established on the basis of tracking error kinematics, which resolves the overall control system into two cascade subsystems. Then, a first-order sliding mode differentiator is introduced in the derivation to avoid tedious analytic calculation, and a Gaussian error function-based continuous differentiable symmetric saturation model is explored to tackle the issue of input saturation. Combined with backstepping design techniques, the neural network control method and an adaptive control approach are used to estimate composite items of the unknown uncertainty and approximation errors. Meanwhile, Lyapunov-based stability analysis guarantees that control error signals of the closed-loop system are uniformly ultimately bounded. Finally, simulation studies are conducted for the trajectory tracking of a moving target and a spiral line to validate the effectiveness of the proposed controller.