ADRC-Based Underwater Navigation Control and Parameter Tuning of an Amphibious Multirotor Vehicle

IEEE JOURNAL OF OCEANIC ENGINEERING(2024)

Cited 0|Views7
No score
Abstract
This article investigates the underwater navigation control of a water-air amphibious multirotor vehicle. We use active disturbance rejection control (ADRC) to construct a tandem-level ADRC motion controller for the water-air multirotor vehicle and introduce particle swarm optimization (PSO) to quickly tune controller parameters. First, the amphibious multirotor vehicle's governing kinematic and dynamic equations are derived. Then, the hydrodynamics of the underwater navigation process is analyzed and estimated. Accordingly, ADRC-based position and attitude controllers are designed and compared with a traditional proportional-integral-derivative (PID) controller and a sliding mode controller (SMC). In addition, PSO is introduced to adjust the gain parameters of the PID, SMC controller, and the parameters of the nonlinear state error feedback law and extended state observer of the ADRC controller. Finally, to verify the stability and robustness of the ADRC controller, simulations are performed under strong external disturbances with a water-air multirotor vehicle. The results demonstrate that controller performance can be improved by introducing PSO to tune the controller parameters and that it is more beneficial for the self-adjacent controller with many control parameters and strong interparameter nonlinearity. ADRC responds faster, rejects external disturbances better, and is more robust than SMC and PID, which permits it to meet the performance requirements of the controller in complex underwater environments.
More
Translated text
Key words
Gravity,Vehicle dynamics,Hydrodynamics,Damping,Buoyancy,Power system stability,Heuristic algorithms,Active disturbance rejection control (ADRC),extended state observer (ESO),nonlinear state error feedback (NLSEF),particle swarm optimization (PSO),proportional-integral-derivative (PID),sliding mode control (SMC)
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined