Robust adaptive radial‐basis function neural network‐based backstepping control of a class of perturbed nonlinear systems with unknown system parameters

2021 ◽  
Author(s):  
Xiao‐Zheng Jin ◽  
Shao‐Yu Lü ◽  
Wei‐Wei Che ◽  
Chao Deng ◽  
Jing Chi
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Basis Function ◽  
Backstepping Control ◽  
System Parameters ◽  
Radial Basis ◽  
Unknown System ◽  
Robust Adaptive

Global fast terminal sliding mode based radial basis function neural network for accurate fault estimation in nonlinear systems

2021 ◽  
pp. 1-17
Author(s):  
Wasif Shabbir ◽  
Li Aijun ◽  
Muhammad Taimoor ◽  
Cui Yuwei
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Real Time ◽  
Basis Function ◽  
Sliding Mode ◽  
Fault Estimation ◽  
Terminal Sliding Mode ◽  
Fast Terminal Sliding Mode ◽  
Radial Basis

The problem of quick and accurate fault estimation in nonlinear systems is addressed in this article by combining the technique of radial basis function neural network (RBFNN) and global fast terminal sliding mode control (GFTSMC) concept. A new strategy to update the neural network weights, by using the global fast terminal sliding surface instead of conventional error back propagation method, is introduced to achieve real time, quick and accurate fault estimation which is critical for fault tolerant control system design. The combination of online learning ability of RBFNN, to approximate any nonlinear function, and finite time convergence property of GFTSMC ensures quick detection and accurate estimation of faults in real time. The effectiveness of the proposed strategy is demonstrated through simulations using a nonlinear model of a commercial aircraft and considering a wide range of sensors and actuators faults. The simulation results show that the proposed method is capable of quick and accurate online fault estimation in nonlinear systems and shows improved performance as compared to conventional RBFNN and other techniques existing in literature.

Direct Adaptive Control for a Certain Class of Nonlinear Systems Using Modified Radial Basis Function Neural Network

Volume 1 ◽  
2004 ◽  
Author(s):  
Hsuan-Ju Chen ◽  
Rongshun Chen
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Basis Function ◽  
Controller Design ◽  
Rbf Neural Network ◽  
Design Parameters ◽  
State Variables ◽  
Rbf Network ◽  
Radial Basis

This paper proposes a direct adaptive controller for SISO affine nonlinear systems using Gaussian radial basis function (RBF) neural network (NN). The exact plant model is not necessary for composing the controller. If the plant is SISO, of affine form, without zero dynamics, and all the state variables are available, the controller is applicable under several mild assumptions. In this paper, the Gaussian RBF network (GRBFN) is modified to include pre-scale weights as its parameters for the input variables, which are also adapted in the control law. Pre-scaling the inputs is equivalent to extending or contracting the spectrum of the approximated function. With the modification, the spectrum along each coordinate of the domain can be scaled separately for approximating. The adaptation of the nonlinear parameters, including the variances, centers, and pre-scaling weights, are derived. Appropriate modification techniques are applied to the adaptation laws to ensure the robustness. The stability is analyzed with Lyapunov’s Theory. From the analysis, the effect of the controller design parameters is also examined. A simulation of an inverted pendulum control is demonstrated to show the effectiveness.

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