An optimal consensus tracking control algorithm for autonomous underwater vehicles with disturbances
This work addresses control challenges for autonomous underwater vehicles in noisy environments, but it appears incremental as it builds on existing methods like Kalman filtering and Riccati equations.
The paper tackled the problem of optimal disturbance rejection control for consensus tracking in autonomous underwater vehicles affected by external persistent disturbances and noise, and the result showed effectiveness in simulations.
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.