Global Adaptive Neural Network Control of Underactuated Autonomous Underwater Vehicles with Parametric Modeling Uncertainty

Abstract

This paper focuses on global adaptive neural network control for a class of underactuated autonomous underwater vehicles in the presence of possibly large modeling parametric uncertainty. As the control inputs cannot directly act in the sway and heave directions, two virtual velocities defined here, plus three actual control actions provided by the thrusters and rudders, are used to achieve the convergence of the system errors to around zero. Motivated by real-time characteristics in the trajectory tracking, the proposed controller presents a significant advantage because it contains only one adaptive parameter to be updated online rather than the neural network weights. In addition, we also consider the practical situation that the velocities of the vehicle may experience sharp speed jumps when the position tracking errors initially change suddenly, which always results in thruster saturation. The biologically inspired model is introduced to smooth the virtual velocity commands such that the vehicle satisfies the control input and velocity constraints. Finally, comparison simulations are given to show the effectiveness of the proposed scheme.

Filtered Extended State Observer Based Line-of-Sight Guidance for Path Following of Unmanned Surface Vehicles With Unknown Dynamics and Disturbances

“…The parameters to be designed will grow with the increasing nodes. Concerning the question mentioned above, an alternative approach is using adaptive law to estimate weights [31]- [33]. Regarding the physical properties of the actuator itself, the input saturation caused by physical constraint condition should be taken into account, and it can impact the stability of the whole system distinctly, such as overshoot, oscillate, etc.…”

Trajectory tracking for autonomous underwater vehicle: An adaptive approach

“…Finally, it is necessary to emphasize that the gains of the adaptive controller shown in equation 12depend directly over the sliding surface σ(t). Moreover, σ(t) is related to the underwater vehicle system state η as stated in (9). In practice, sensors which provide the data η, supply noisy measurements.…”

“…A broad class of robust controllers has been proposed for the trajectory tracking problem on AUV. For example, Fuzzy Logic Controllers (FLC) [6] [7], Neural-Network based control (NNC) [8,9], Predictive control [10], Adaptive control [11], Sliding Modes Control (SMC) [12], High Order Sliding Modes Control (HOSMC) [13,14] and so on. Each methodology has strengths and weaknesses.…”

Output Feedback Spatial Trajectory Tracking Control of Underactuated Unmanned Undersea Vehicles

“…The underactuated UUVs are not stabilized by any smooth or continuous time-invariant feedback controllers, in the light of the famous Brockett's necessary condition [15], [16]. Therefore, the trajectory tracking control problem of underactuated UUVs is extremely challenging and great meaningful, which is a hot and active research topic [3]- [11], [17]- [30].…”