Adaptive neural output‐feedback tracking control for a class of stochastic nonlinear systems with output constraint and unknown control coefficients

2021 ◽  
Author(s):  
Fei Shen ◽  
Xinjun Wang ◽  
Xinghui Yin
Keyword(s):  
Nonlinear Systems ◽  
Output Feedback ◽  
Tracking Control ◽  
Stochastic Nonlinear Systems ◽  
Unknown Control Coefficients ◽  
Unknown Control ◽  
Feedback Tracking ◽  
Output Constraint ◽  
Control Coefficients

scholarly journals Bounded Analysis and Practical Tracking Control of Complex Stochastic Nonlinear Systems with Unknown Control Coefficients

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Long-Chuan Guo ◽  
Xiang-Kun Fang
Keyword(s):  
Nonlinear Systems ◽  
Tracking Error ◽  
Upper And Lower Bounds ◽  
Design Parameters ◽  
Stochastic Nonlinear Systems ◽  
Tracking Controller ◽  
Unknown Control Coefficients ◽  
Unknown Control ◽  
Theoretical Results ◽  
Control Coefficients

This paper mainly focuses on the output practical tracking controller design for a class of complex stochastic nonlinear systems with unknown control coefficients. In the existing research results, most of the complex systems are controlled in a certain direction, which leads to the disconnection between theoretical results and practical applications. The authors introduce unknown control coefficients, and the values of the upper and lower bounds of the control coefficients are generalized by constants to allow arbitrary values to be arbitrarily large or arbitrarily small. In the control design program, the design problem of the controller is transformed into a parameter construction problem by introducing appropriate coordinate transformation. Moreover, we construct an output feedback practical tracking controller based on the dynamic and static phase combined by Ito stochastic differential theory and selection of appropriate design parameters, ensuring that the system tracking error can be made arbitrarily small after some large enough time. Finally, a simulation example is provided to illustrate the efficiency of the theoretical results.

scholarly journals Adaptive NN output-feedback control for stochastic time-delay nonlinear systems with unknown control coefficients and perturbations

2016 ◽  
Vol 21 (4) ◽  
pp. 515-530 ◽  
Author(s):  
Hui Fang Min ◽  
Na Duan
Keyword(s):  
Feedback Control ◽  
Nonlinear Systems ◽  
Output Feedback ◽  
Output Feedback Control ◽  
Function Technique ◽  
Time Varying Delay ◽  
Unknown Control Coefficients ◽  
Unknown Control ◽  
Stochastic Time ◽  
Control Coefficients

This paper addresses the problem of adaptive output-feedback control for more general class of stochastic time-varying delay nonlinear systems with unknown control coefficients and perturbations. By using Lyapunov–Krasovskii functional, backstepping and tuning function technique, a novel adaptive neural network (NN) output-feedback controller is constructed with fewer learning parameters. The designed controller guarantees that all the signals in the closed-loop system are 4-moment (or mean square) semi-globally uniformly ultimately bounded (SGUUB). Finally, a simulation example is shown to demonstrate the effectiveness of the proposed control scheme.

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