Depth Control Of Autonomous Underwater Vehicle Research Articles

Model-Based Design (MBD) for Autonomous Underwater Vehicle

This project is to enhanced and upgraded a depth controller for Autonomous Underwater Vehicle (AUV) to submerge precisely at the certain depth. This poster demonstrated an AUV equipped with integrated sensor and depth controller based on the pressure sensing which able to continuously sending the depth data to controller. The depth Simulink Arduino algorithm is implemented on an Arduino Mega using ModelBased Design (MBD) with MATLAB and Simulink. MBD used to model, simulate and verify the Simulink control algorithm after obtained data through open-loop experiment test. Then, it deploys and tests the algorithm on the embedded AUV hardware. The focus was in controlling the AUV vertical trajectory as the AUV tried to remain stationary at the selected depth and consuming its rise time Tr , overshoot Os , and settling time Ts are minimized. The comparative study for the AUV depth-control by On-Off, Proportional Integral Derivative (PID) controller and Fuzzy Logic Controller (FLC) controllers. MBD has faster implementation with fewer coding error when deploy to AUV hardware.

Autonomous underwater vehicle depth control based on an improved active disturbance rejection controller

Large fluctuation, large overshoot, and uncertain external disturbance that occur when an autonomous underwater vehicle is in deep motion are difficult to address using the traditional control method. An optimal control strategy based on an improved active disturbance rejection control technology is proposed to enhance the trajectory tracking accuracy of autonomous underwater vehicles in actual bathymetric operations and resist external and internal disturbances. First, the depth motion and mathematical models of an autonomous underwater vehicle and propeller are established, respectively. Second, the control rate of the extended state observer and the nonlinear error feedback of the traditional active disturbance rejection control are improved by using a new nonlinear function. The nonlinearity, model uncertainty, and external disturbance of the autonomous underwater vehicle depth control system are extended to a new state, which is realized by an improved extended state observer. Third, the improved nonlinear state error feedback is used to suppress residual errors and provide high-quality control for the system. Simulation and experimental results show that under the same parameters, the traditional active disturbance rejection control has a small overshoot, fast tracking ability, and strong anti-interference ability. The optimized active disturbance rejection control and traditional active disturbance rejection control are applied to the deep-variation motion of autonomous underwater vehicles. Results show that the proposed optimal control strategy is not only simple and feasible but also demonstrates good control performance.