Adaptive sliding mode control based on local recurrent neural networks for underwater robot

Zhang, Mingjun; Chu, Zhenzhong

doi:10.1016/j.oceaneng.2012.02.004

Cited by 113 publications

(52 citation statements)

References 17 publications

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“…21 Zhang et al have issued an adaptive sliding control method, switch gain adjustment method has been employed to deal with chattering problem, and network has been employed to estimate unknown items online. 22 But the feedforward network requires a great number of neurons to represent dynamical responses; moreover, the approximation function of neuron is difficult to interpret. Recently, petri network has been successfully applied for system modelling and analysis.…”

Section: Introductionmentioning

confidence: 99%

Autonomous underwater vehicle precise motion control for target following with model uncertainty

Huang

Zhang

Hongde

et al. 2017

International Journal of Advanced Robotic Systems

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Target following plays an important role in oceanic detection and target capturing for autonomous underwater vehicles. Due to the model nonlinearity and external disturbance, the dynamic model of a portable autonomous underwater vehicle was usually established with parameter uncertainties. In this article, a petri-based recurrent type 2 fuzzy neural network has been built to approximate the unknown autonomous underwater vehicle dynamics. The type 2 fuzzy logic system has been applied to the network to improve the approximation accuracy for systematic nonlinearity, and the petri layer in the network can improve estimation speed and reduce energy consumption. A petri-based recurrent type 2 fuzzy neural network-based adaptive robust controller has been proposed for target tracking. In the offshore experiments, the proposed controller has not only realized stable position and pose control but also successfully followed mobile target on the surface. In the tank underwater experiments, the pipeline target has been successfully followed to further verify the controller performance.

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Section: Introductionmentioning

confidence: 99%

Autonomous underwater vehicle precise motion control for target following with model uncertainty

Huang

Zhang

Hongde

et al. 2017

International Journal of Advanced Robotic Systems

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“…In this paper, inspired by the work by Allibert et al [20], we propose a hierarchical IBVS framework for a fully actuated underwater vehicle consisting of two control loops: 1) in the kinematic control loop, NMPC is applied to generate a desired velocity while satisfying the constraints of the visibility of the features and the maximum velocity of the vehicle; 2) in the dynamic control loop, an NN-based model reference adaptive control (NN-MRAC) is designed to achieve stable velocity tracking. The capability of NNs to be used as universal approximators has been proven in adaptive trajectory tracking control of mobile robots by Bugeja et al [22], surface vessels by Du et al [23], and underwater vehicles by Zhang et al [24] and Zhang and Chu et al [25].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Model Predictive Image-Based Visual Servoing of Underwater Vehicles With Adaptive Neural Network Dynamic Control

Gao

Proctor

Shi

et al. 2016

IEEE Trans. Cybern.

125

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This paper proposes a hierarchical image-based visual servoing (IBVS) strategy for dynamic positioning of a fully actuated underwater vehicle. In the kinematic loop, the desired velocity is generated by a nonlinear model predictive controller, which optimizes a cost function of the predicted image trajectories under the constraints of visibility and velocity. A velocity reference model, representing the desired closed-loop vehicle dynamics, is integrated with an IBVS kinematic model to predict the future trajectories. In the dynamic velocity tracking loop, a neural-network-based model reference adaptive controller is designed to ensure the convergence of the velocity tracking error in the presence of uncertainties associated with vehicle dynamic parameters, water velocity, and thrust forces. Comparative simulations with different control and system configurations are performed to verify the effectiveness of the proposed scheme and to illustrate the influences of the prediction horizon, cost function, closed-loop vehicle dynamics, and predictive velocity reference model on the IBVS system performance.

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“…Some experts have combined the control algorithm and conventional sliding mode method to solve the chattering phenomenon. The novel adaptive sliding mode controllers can adjust the control torque based on real-time position tracking errors, which alleviates the chattering phenomenon of the sliding mode controller [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

Tao

Zheng

Lin

2016

International Journal of Advanced Robotic Systems

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A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network parameters are derived by a Koski function algorithm to ensure the network convergences and enacts stable control. The simulations and experimental results of the deburring robot system are provided to illustrate the effectiveness of the proposed RBFNN-SMC control method. The advantages of the proposed RBFNN-SMC method are also evaluated by comparing it to existing control schemes.

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Adaptive sliding mode control based on local recurrent neural networks for underwater robot

Cited by 113 publications

References 17 publications

Autonomous underwater vehicle precise motion control for target following with model uncertainty

Autonomous underwater vehicle precise motion control for target following with model uncertainty

Hierarchical Model Predictive Image-Based Visual Servoing of Underwater Vehicles With Adaptive Neural Network Dynamic Control

A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

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