Fuzzy neural network-based robust adaptive control for dynamic positioning of underwater vehicles with input dead-zone

In this paper, a robust-adaptive-fuzzy-neural-network controller (RAFNNs) bases on dead zone compensator for industrial robot manipulators (RM) is proposed to dead the unknown model and external disturbance. Here, the unknown dynamics of the robot system is deal by using fuzzy neural network to approximate the unknown dynamics. The online training laws and estimation of the dead-zone are determined by Lyapunov stability theory and the approximation theory. In this proposal, the robust sliding-mode-control (SMC) is constructed to optimize parameter vectors, solve the approximation error and higher order terms. Therefore, the stability, robustness, and desired tracking performance of RAFNNs for RM are guaranteed. The simulations and experiments performed on three-link RM are provided in comparison with neural-network (NNs) and proportional-integral-derivative (PID) to demonstrate the robustness and effectiveness of the RAFNNs.

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Robust Adaptive Control for Nonlinear Uncertain Systems Using Type-2 Fuzzy Neural Network System

Mathematical Problems in Engineering ◽

10.1155/2011/604391 ◽

2011 ◽

Vol 2011 ◽

pp. 1-25

Author(s):

Ching-Hung Lee ◽

Yu-Ching Lin

Keyword(s):

Neural Network ◽

Uncertain Systems ◽

Fuzzy Neural Network ◽

Learning Algorithm ◽

Robust Adaptive Control ◽

Control Scheme ◽

Fuzzy Neural ◽

Nonlinear Uncertain Systems ◽

Robust Adaptive

This paper proposes a novel intelligent control scheme using type-2 fuzzy neural network (type-2 FNN) system. The control scheme is developed using a type-2 FNN controller and an adaptive compensator. The type-2 FNN combines the type-2 fuzzy logic system (FLS), neural network, and its learning algorithm using the optimal learning algorithm. The properties of type-1 FNN system parallel computation scheme and parameter convergence are easily extended to type-2 FNN systems. In addition, a robust adaptive control scheme which combines the adaptive type-2 FNN controller and compensated controller is proposed for nonlinear uncertain systems. Simulation results are presented to illustrate the effectiveness of our approach.

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Robust Adaptive Control for Nonlinear Discrete-Time Systems by Using Multiple Models

Mathematical Problems in Engineering ◽

10.1155/2013/679039 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Xiao-Li Li ◽

De-Xin Liu ◽

Jiang-Yun Li ◽

Da-Wei Ding

Keyword(s):

Neural Network ◽

Adaptive Control ◽

Discrete Time ◽

Bp Neural Network ◽

Dead Zone ◽

Robust Adaptive Control ◽

Stability And Convergence ◽

Multiple Models ◽

Multiple Weights ◽

Robust Adaptive

Back propagation (BP) neural network is used to approximate the dynamic character of nonlinear discrete-time system. Considering the unmodeling dynamics of the system, the weights of neural network are updated by using a dead-zone algorithm and a robust adaptive controller based on the BP neural network is proposed. For the situation that jumping change parameters exist, multiple neural networks with multiple weights are built to cover the uncertainty of parameters, and multiple controllers based on these models are set up. At every sample time, a performance index function based on the identification error will be used to choose the optimal model and the corresponding controller. Different kinds of combinations of fixed model and adaptive model will be used for robust multiple models adaptive control (MMAC). The proof of stability and convergence of MMAC are given, and the significant efficacy of the proposed methods is tested by simulation.

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