Compensated model-free adaptive tracking control scheme for autonomous underwater vehicles via extended state observer

Li, Xiaohan; Ren, Chao; Ma, Shugen; Zhu, Xiaoyan

doi:10.1016/j.oceaneng.2020.107976

Cited by 28 publications

(12 citation statements)

References 37 publications

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“…Thus, it is highly desirable to estimate the unknown input gain and lumped disturbance synchronously. Existing methods to handle the unknown input gains contain Nussbaum function [21] and adaptive parameter estimation [10], [20], [22], [23]. In [10] and [20], the adaptive parameter projection method featured with given bound is applied to estimate the input gain.…”

Section: Dear Editormentioning

confidence: 99%

“…However, the estimation could not be assured of converging to the true value. In [21], Nussbaum functions are introduced to deal with the control coefficients in the scenario of unknown control input gains and directions. It is worthwhile mentioning that both the aforementioned methods [10], [20], [21] could not estimate the control input gain accurately, especially in the scenario with unknown internal dynamic and external disturbance.…”

Section: Dear Editormentioning

confidence: 99%

“…In [21], Nussbaum functions are introduced to deal with the control coefficients in the scenario of unknown control input gains and directions. It is worthwhile mentioning that both the aforementioned methods [10], [20], [21] could not estimate the control input gain accurately, especially in the scenario with unknown internal dynamic and external disturbance. In [22], a data-driven second-order ESO based on pseudo Jacobian matrix estimation is derived to estimate the model approximation error.…”

Section: Dear Editormentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Learning Extended State Observers for Nonlinear Systems: Design, Analysis and Hardware-in-Loop Simulations

Peng

Liu

et al. 2023

IEEE/CAA J. Autom. Sinica

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Section: Dear Editormentioning

confidence: 99%

Section: Dear Editormentioning

confidence: 99%

Section: Dear Editormentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Learning Extended State Observers for Nonlinear Systems: Design, Analysis and Hardware-in-Loop Simulations

Peng

Liu

et al. 2023

IEEE/CAA J. Autom. Sinica

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“…Interferences caused by environmental disturbances and parameter uncertainties are considered as unknown and time-varying lumped disturbances. The yaw angle and linear velocity control system can be regarded as second-order nonlinear single input single output system with uncertain disturbances, which is shown as follows: (24) where x = x 1 x 2 T , x 1 , and x 2 are system states.g(x), and f (x) are smooth functions.…”

Section: Asmibc For Yaw Angle and Linear Velocity Of The Cdrmentioning

confidence: 99%

“…An extended state observer (ESO) is used to obtain the uncertainty of the WMR dynamics model [23]. In [24], external disturbances are observed by a linear ESO with data-driven structural improvement. A radial basis function neural network is designed to approximate the system model uncertainty [25].…”

Section: Introductionmentioning

confidence: 99%

Water Surface and Ground Control of a Small Cross-Domain Robot Based on Fast Line-of-Sight Algorithm and Adaptive Sliding Mode Integral Barrier Control

et al. 2022

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This paper focuses on the control method of small cross-domain robots (CDR) on the water surface and the ground. The maximum size of the robot is 85 cm and the weight of the robot is 6.5 kg. To solve the problem that CDRs cannot handle the lateral velocity, which leads to error in tracking the desired trajectory, a fast line of sight (FLOS) algorithm is proposed. In this method, an exponential term is introduced to plan the yaw angle, and a fast-extended state observer (FESO) is designed to observe the side slip angle without small angle assumption. The performances and working environments of CDRs are different on the ground and the water surface. Therefore, to avoid the driver saturation and putting risk, an adaptive sliding mode integral barrier control (ASMIBC) is proposed to constrain the robot state. This control method solves the constraint failure of the traditional integral barrier control (IBC) when the desired state is a constant. The gain of the sliding mode is adaptively adjusted by the error between the limit state and the actual state. In addition, the adaptive rate is designed for uncertain time-varying lumped disturbances, such as water resistance, currents and wind. Simulation results demonstrate the effectiveness of the proposed control method.

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Adaptive anti‐disturbance sampling control of autonomous surface vehicles based on discrete‐time concurrent learning extended state observers

Cui

et al. 2023

Intl J Robust & Nonlinear

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Summary This paper investigates the surge speed tracking control of an autonomous surface vehicle (ASV) subject to fully unknown internal dynamic, external disturbance, and unknown control input gain. An adaptive anti‐disturbance sampling control method is proposed for an ASV without using any model parameters. Specifically, by utilizing real‐time and historical data, discrete‐time reduced‐order and full‐order concurrent learning extended state observers (CLESOs) are designed to estimate the unknown ASV model parameters and ensure estimation convergence without requiring persistent excitation. Then, a surge speed tracking control law is designed based on the discrete‐time full‐order CLESO. Through Lyapunov stability analysis, the closed‐loop system is proven to be stable, and the estimation and tracking errors are bounded. Simulation and hardware‐in‐loop experimental results validate the effectiveness of the proposed discrete‐time CLESOs for the surge speed tracking of an ASV with fully unknown dynamic model.

show abstract

Compensated model-free adaptive tracking control scheme for autonomous underwater vehicles via extended state observer

Cited by 28 publications

References 37 publications

Data-Driven Learning Extended State Observers for Nonlinear Systems: Design, Analysis and Hardware-in-Loop Simulations

Data-Driven Learning Extended State Observers for Nonlinear Systems: Design, Analysis and Hardware-in-Loop Simulations

Water Surface and Ground Control of a Small Cross-Domain Robot Based on Fast Line-of-Sight Algorithm and Adaptive Sliding Mode Integral Barrier Control

Adaptive anti‐disturbance sampling control of autonomous surface vehicles based on discrete‐time concurrent learning extended state observers

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