Adaptive MPC trajectory tracking for AUV based on Laguerre function

When the autonomous underwater vehicle (AUV) traces the trajectory under complex hydrological conditions, the real-time performance of system is slow and the accuracy of tracking is poor. To solve this problem, an adaptive predictive control method based on Laguerre function is designed in this paper. This method contains two parts: one is the adaptive MPC module which is used to track the trajectory accurately; the other is the Laguerre function module which is applied to reduce computations significantly. In the adaptive MPC module, the recursive least squares algorithm is introduced to identify the model parameters of the system to improve the accuracy and the robustness. However, this method may cause a great increase in computation when AUV works in the complex environment. Therefore, in Laguerre function, one reconstruction of the controller input variables is introduced to reduce the matrix order of the objective function. The simulation results show that the designed method has a good performance in the respects of the dynamic, the anti-interference and the robustness when AUV tracks the trajectory with the less amount of calculation.

Automated summary

An adaptive predictive control method based on Laguerre function is designed in this paper to solve the problem of slow real-time performance and poor tracking accuracy of autonomous underwater vehicle (AUV) under complex hydrological conditions. The method includes an adaptive model predictive control (MPC) module for accurate trajectory tracking and a Laguerre function module for significant computation reduction. The simulation results show that the designed method has good performance in terms of dynamics, anti-interference, and robustness when AUV tracks the trajectory with less computation.