An optimal consensus tracking control algorithm for autonomous underwater vehicles with disturbances

TL;DR

This paper develops an optimal control algorithm for autonomous underwater vehicles that effectively rejects disturbances and noise, ensuring consensus tracking in multi-agent systems.

Contribution

It introduces a recursive filtering approach combined with Riccati equations to derive a novel optimal control law for disturbance rejection in underwater vehicle systems.

Findings

The control algorithm effectively rejects persistent disturbances.

Simulations confirm robustness against noise.

The method ensures consensus tracking in multi-agent underwater systems.

Abstract

The optimal disturbance rejection control problem is considered for consensus tracking systems affected by external persistent disturbances and noise. Optimal estimated values of system states are obtained by recursive filtering for the multiple autonomous underwater vehicles modeled to multi-agent systems with Kalman filter. Then the feedforward-feedback optimal control law is deduced by solving the Riccati equations and matrix equations. The existence and uniqueness condition of feedforward-feedback optimal control law is proposed and the optimal control law algorithm is carried out. Lastly, simulations show the result is effectiveness with respect to external persistent disturbances and noise.