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

Abstract

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.

Position control for an autonomous underwater vehicle under noisy conditions

“…Then, we add and subtract M A J À1 € h(t À l) on the right-hand side of equation (24). Equation (24) becomes…”

“…In this work, the unknown dynamics are approximated utilizing a radial basis function neural network. Zhang et al 24 proposed an approach based on an improved active disturbance rejection control (ADRC) scheme that involves a nonlinear function to raise the trajectory tracking accuracy. Deep learning approaches have also been proposed.…”

“…However, it should be noted that all aforementioned researches 5–25 do not consider the combined nuisance factors of hydrodynamic parameter uncertainty along with process and measurement noise. Indeed, for deep-sea AUV platforms, the global positioning system (GPS) is not available, and therefore, AUVs obtain the velocity information for their controller from on-board navigation sensors, 26,27 such as acoustic and optical devices.…”

Linear quadratic regulator based on extended state observer–based active disturbance rejection control of autonomous vehicle path following control

“…ADRC has received wide application in the control of aircraft and underwater vehicle, resulting in many research results, which show that ADRC has a strong capability to resist external disturbance. [21][22][23][24][25] For the problem of complex ADRC parameter tuning, Teppa-Garran proposed the ESO-LQR control method, which was next applied in aircraft. 26,27 28 combined the control methods of ADRC and MPC to design an active steering controller for an autonomous vehicle.…”

Electrohydraulic and Electromechanical Buoyancy Change Device Unified Vertical Motion Model

“…Variable buoyancy systems (VBS) play a crucial role in developing the functionality and efficiency of underwater robotics [18,19]. By adjusting their buoyancy, these systems enable underwater vehicles to control their depth in water, mimicking the natural behavior of marine creatures.…”