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.

Highlights

  • Autonomous underwater vehicles (AUVs) are submersible robots that operate without human intervention and perform various functions.[1]

  • The results revealed the effectiveness of the optional internal model control

  • Where m is the quality of the underwater robot; xG, yG, and zG are the barycenter coordinates of the AUV; Ix, Iz, and Iz are the moments of inertia of mass m of the AUV on Ox, Oy, and Oz axes, respectively; u, v, w, p, q, and r are the angular velocities of the six degrees of freedom; u_, v_, w_, p_, q_, and r_are the angular acceleration of the six degrees of freedom; X, Y, Z, K, M, and N are the torques of the six degrees of freedom, and they pertain to gravity, buoyancy, propeller thrust, rudder force, fluid power, and environmental disturbance force, respectively

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Summary

Introduction

Autonomous underwater vehicles (AUVs) are submersible robots that operate without human intervention and perform various functions.[1]. ADRC is a promising nonlinear control method proposed by Jingqing Han in the 1990s.17 This method has strong anti-interference capability that does not completely depend on the mathematical model of the system, strong robustness, and dynamic compensation for internal and external system disturbances; ADRC demonstrates good control performance, such as small overshoot and fast response.[18,19,20] ADRC comprises a tracking differentiator (TD), an extended state observer (ESO), and nonlinear state error feedback (NSEF). Another study proposed fractional-order fuzzy ADRC, which solves the shortcomings (e.g. low precision and slow response) of the traditional control method, to deal with multi-joint motion trajectory control of robot arms.[26] Lamraoui and Qidan[27] developed the LADRC tracking control method to improve the robustness and tracking performance of the controller and address the problems of environmental disturbance and model uncertainty in single-wheeled mobile robots.

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Design of the improved ADRC controller
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Findings
Conclusion
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