RETRACTED ARTICLE: The docking guidance control of underwater glider with integration control algorithm

Su, Zhiqiang; Song, Dalei; Liu, Yuhai

doi:10.1007/s00773-017-0462-x

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…For flexible air-breathing hypersonic vehicles (FAHVs), a conditional disturbance negation (CDN)-based ADRC scheme was used for velocity and altitude tracking control in the presence of various uncertainties and disturbances [16]. The algorithm combined reinforcement learning with ADRC and was compared with classical ADRC by simulation based on the dynamic model of the OUC-III underwater glider [17].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Yin

et al. 2019

Electronics

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The control issue of Autonomous Underwater Vehicles (AUV) is very challenging since the precise mathematical model of AUV is hard to establish due to its strong coupling and time-varying features. Meanwhile, AUV movement is easily interfered with by ocean currents and waves, causing anti-interference performance of traditional Proportional-Integral-Derivative (PID) control to be unsatisfactory. Aiming to solve those problems, an algorithm of fuzzy optimized model-free adaptive control (MFAC) based on auto-disturbance rejection control (ADRC) was proposed and used in AUV heading control. The MFAC is used to overcome the difficulty with establishing a precise mathematical model, and the ADRC is introduced to handle the interference of currents and waves. In this paper, MFAC and ADRC are combined. First, the MFAC is performed based only on the I/O data of the controlled object, which is simple to implement with low calculation complexity and strong robustness. Then, a tracking differentiator (TD) is employed to track the input signal to overcome the antinomy of rapidity and hypertonicity in MFAC. After that, an extended-state observer (ESO) is added to control the variables of MFAC to estimate all the disturbances, which can greatly improve the anti-interference ability of the system. Due to the complexity and diversity of the marine environment, a fuzzy optimized MFAC based on ADRC is proposed to improve the adaptability of AUV to the marine environment. Simulations and experiments were carried out to verify the control effect of this algorithm in complex sea conditions.

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Section: Introductionmentioning

confidence: 99%