A New Intelligent Dynamic Control Method for a Class of Stochastic Nonlinear Systems

This paper presents a new method for a comprehensive stabilization and backstepping control system design for a class of stochastic nonlinear systems. These types of systems are so abundant in practice that the control system designer must assume that random noise with a definite probability distrib...

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Bibliographic Details
Published in:Mathematics (Basel) 2022-05, Vol.10 (9), p.1406
Main Authors: Huang, Haifeng, Shirkhani, Mohammadamin, Tavoosi, Jafar, Mahmoud, Omar
Format: Article
Language:English
Subjects:
backstepping control method
Control methods
Control systems design
Controllers
Design techniques
Dynamic control
Explosions
Failure
Fuzzy logic
Innovations
Mathematics
Neural networks
Nonlinear systems
Optimal control
parameter estimation
Random noise
Stochastic processes
Stochastic systems
Technological change
type-3 fuzzy neural network
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Summary:This paper presents a new method for a comprehensive stabilization and backstepping control system design for a class of stochastic nonlinear systems. These types of systems are so abundant in practice that the control system designer must assume that random noise with a definite probability distribution affects the dynamics and observations of state variables. Stochastic control is intended to determine the time course of control variables so that the control target is achievable even with minimal cost. Since the mathematical equations of stochastic nonlinear systems are not always constant, not every model-based controller can be accurate. Therefore, in this paper, a type-3 fuzzy neural network is used to estimate the parameters of the backstepping control method. In the simulation, the proposed method is compared with the Type-1 fuzzy and RBFN methods. Results clearly show that the proposed method has a very good performance and can be used for any system in this class.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10091406