Adaptive Neural Network Asymptotic Tracking for Nonstrict-Feedback Switched Nonlinear Systems

2021 ◽  
Author(s):  
Yongchao Liu ◽  
Qidan Zhu
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Switched Nonlinear Systems ◽  
Adaptive Neural Network ◽  
Asymptotic Tracking

scholarly journals Neural Network-Based Event-triggered Adaptive Asymptotic Tracking Control for Switched Nonlinear Systems

2021 ◽  
Author(s):  
Baomin Li ◽  
Jianwei Xia ◽  
Wei Sun ◽  
Hao Shen ◽  
Huasheng Zhang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Tracking Control ◽  
Tracking Error ◽  
Network Control ◽  
Switched Nonlinear Systems ◽  
Unknown Control Directions ◽  
Asymptotic Tracking ◽  
Unknown Control ◽  
Event Triggered

Abstract This paper addresses the event-triggered based adaptive asymptotic tracking control problem for switched nonlinear systems with unknown control directions based on neural network technique. A novel asymptotic tracking controller, in which Nussbaum functions are introduced to address the issue of unknown control directions, is designed by combining neural network control technology and event-triggered strategy. Different from the existing tracking control schemes, the proposed controller in this paper can guarantee that the tracking error ς 1 asymptotically converges to the origin and reduce the communication burden from the controller to the actuator. Finally, the effectiveness of the presented control design is proved by numerical examples.

scholarly journals Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

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