An Observer-Based Adaptive Neural Network Finite-Time Tracking Control for Autonomous Underwater Vehicles via Command Filters

Abstract

Due to the hostile marine environment, there will inevitably be unpredictable factors during the operation of unmanned underwater vehicles, including changes in ocean currents, hull dimensions, and velocity measurement uncertainties. An improved finite-time adaptive tracking control issue is considered for autonomous underwater vehicles (AUVs) with uncertain dynamics, unknown external disturbances, and unavailable speed information. A state observer is designed to estimate the position and velocity of the vehicle via a neural network (NN) approach. The NN is used to estimate uncertainties and external disturbances. A finite-time controller is designed via backstepping and command filter techniques. A multi-input multi-output (MIMO) filter for AUVs is established, and the corresponding MIMO filter compensation signal is constructed to eliminate the effect of filtering error. All the signals of the closed-loop system are proved to be finite-time bounded. An example with comparison is given to show the effectiveness of our method.

Fast Finite-Time Super-Twisting Sliding Mode Control with an Extended State Higher-Order Sliding Mode Observer for UUV Trajectory Tracking

“…A tremendous amount of research efforts has been spent on the development and test of advanced control approaches for unmanned vehicles, such as sliding mode control [1][2][3][4][5][6][7][8], model predictive control [9][10][11][12], backstepping control [13][14][15][16][17], active disturbance rejection control [18][19][20][21], adaptive control [22][23][24][25] and fixed-time control [26][27][28]. Among them, sliding mode control has shown excellent robustness to external disturbances and model uncertainties, and it is convenient in the design and debugging of control parameters.…”

Extended State Observer-Based Command-Filtered Safe Flight Control for Unmanned Helicopter under Time-Varying Path Constraints and Disturbances

“…To reduce the computing complexity, the command filter technique [19,20] was proposed. Due to the avoidance of differentiation and the satisfaction of estimation effect, command-filtered backstepping technologies are widely adopted in a number of control schemes [21][22][23][24][25]. In [26], based the backstepping technique, a finite-time commandfiltered control scheme was adopted for quadcopter UAVs, and the problem of integration explosion was handled well.…”

Robust Path-Following Control for AUV under Multiple Uncertainties and Input Saturation

“…In [15], a backstepping SMC based on an adaptive slow-varying observer was designed to estimate and eliminate the multiple disturbances. In [16], a neural network-based state observer is designed and a finite time controller is designed using backstepping and command filtering techniques to realize the finite time adaptive tracking control problem for AUVs. In [7], the H ∞ control method is combined with the DOBC method to deal with the multiple disturbances according to their main characteristics.…”