Neural-network estimators based fault-tolerant tracking control for AUV via ADP with rudders faults and ocean current disturbance

Che, Gaofeng; Yu, Zhen

doi:10.1016/j.neucom.2020.06.026

Cited by 62 publications

(23 citation statements)

References 40 publications

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“…The error tracking system (33) can be transformed using equation ( 27) as follows: Theorem 6 For the error tracking system (13), Assumptions 2-5 hold. The optimal cost function and the optimal control law are provided by ( 16) and (18). The weight updating laws of the critic neural network and the action neural network are given by ( 25) and (31).…”

Section: Stability Analysismentioning

confidence: 99%

“…ADP scheme has been introduced to compensate for unknown dynamics, such as external disturbances, control input nonlinearities and model uncertainties for nonlinear systems [20,21]. Policy iteration [22][23][24][25][26] and value iteration [27,28] are two primary iteration ADP algorithms. In this work, ADP is introduced to solve the trajectory-tracking problem for underactued AUV with bounded time-varying disturbances and the higher-level tracking accuracy is received.…”

Section: Introductionmentioning

confidence: 99%

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ADP based trajectory-tracking control via backstepping method for underactuated AUV with unknown dynamics

Che

2021

Preprint

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This paper investigates trajectory-tacking control problem for underactuated autonomous underwater vehicles (AUV) with unknown dynamics. Different from existing adaptive dynamic programming (ADP) schemes, our proposed control scheme can achieve high-level system stability and tracking control accuracy. Firstly, the backstepping approach is introduced into the kinematic model of underactuated AUV and produces a virtual velocity control which is taken as the desired velocity input of the dynamic model of underactuated AUV. Secondly, the error tracking system is constructed according to the dynamic model of underactuated AUV. Thirdly, the critic neural network and the action neural network are employed to transform the trajectory-tracking control problem into optimal control problem based on policy iteration algorithm. At last simulation results are given to verify the effectiveness of the control scheme proposed in this paper.

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Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ADP based trajectory-tracking control via backstepping method for underactuated AUV with unknown dynamics

Che

2021

Preprint

Self Cite

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“…In recent years, many scholars focused on designing disturbance observers (DO) to estimate and compensate the external disturbances and unknown dynamics of system 20,21 . Additionally, fuzzy logical system (FLS) 22 and NN were often used to approximate unknown complex functions due to their powerful universal approximation characteristics 23,24 . Unknown dynamics estimator (UDE) was designed as a new estimator to compensate the unknown dynamics of the system to improve the control accuracy of the system 25,26 …”

Section: Introductionmentioning

confidence: 99%

Neural network‐based output synchronization control for multi‐actuator system

Wang

Ding

Zhao

et al. 2022

Adaptive Control & Signal

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This article proposes a novel output synchronization control strategy for a class of multi-actuator system with strict-feedback form. High-order sliding mode observer is utilized to estimate the system states with the only available output signal. Moreover, radio basis function neural network combined estimated states is applied to handle the system uncertainties, which helps to realize the combination of state observation and disturbance observation and reduce the dependence on the system model. Furthermore, a new synchronization control method is employed to improve the synchronization accuracy of multiple actuators through backstepping technology. Based on the above control strategies, the control performance of the multi-actuator system is greatly enhanced while the design difficulty of the controller is significantly reduced. In the end, simulations and experiments examples are used to illustrate the superiority of the designed technique.

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“…In this paper, neural network-(NN-) based adaptive trajectory tracking control scheme has been designed for underactuated AUVs which are subjected to unknown asymmetrical actuator saturation and unknown dynamics [55]. is paper investigates neural network estimators-based fault-tolerant tracking control problem for AUV with rudder faults and ocean current disturbance [56]. A robust neural network approximationbased output-feedback tracking controller is proposed for autonomous underwater vehicles (AUVs) in six degreesof-freedom in this paper [57].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Vectored Thruster Autonomous Underwater Vehicle Control

Liu

2021

Complexity

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Autonomous underwater vehicles (AUVs) are widely used to accomplish various missions in the complex marine environment; the design of a control system for AUVs is particularly difficult due to the high nonlinearity, variations in hydrodynamic coefficients, and external force from ocean currents. In this paper, we propose a controller based on deep reinforcement learning (DRL) in a simulation environment for studying the control performance of the vectored thruster AUV. RL is an important method of artificial intelligence that can learn behavior through trial-and-error interactions with the environment, so it does not need to provide an accurate AUV control model that is very hard to establish. The proposed RL algorithm only uses the information that can be measured by sensors inside the AUVs as the input parameters, and the outputs of the designed controller are the continuous control actions, which are the commands that are set to the vectored thruster. Moreover, a reward function is developed for deep RL controller considering different factors which actually affect the control accuracy of AUV navigation control. To confirm the algorithm’s effectiveness, a series of simulations are carried out in the designed simulation environment, which is a method to save time and improve efficiency. Simulation results prove the feasibility of the deep RL algorithm applied to the control system for AUV. Furthermore, our work also provides an optional method for robot control problems to deal with improving technology requirements and complicated application environments.

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Neural-network estimators based fault-tolerant tracking control for AUV via ADP with rudders faults and ocean current disturbance

Cited by 62 publications

References 40 publications

ADP based trajectory-tracking control via backstepping method for underactuated AUV with unknown dynamics

ADP based trajectory-tracking control via backstepping method for underactuated AUV with unknown dynamics

Neural network‐based output synchronization control for multi‐actuator system

Deep Reinforcement Learning for Vectored Thruster Autonomous Underwater Vehicle Control

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