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Adaptive NN output-feedback stabilization for a class of stochastic nonlinear strict-feedback systems
Jing Li1, Weisheng Chen, Junmin Li
1Department of Applied Mathematics, Xidian University, Xi'an, PR China. xidianjing@126.com
ISA Transactions
|June 30, 2009
Summary
This study stabilizes stochastic nonlinear systems using adaptive neural networks (NNs). A simplified controller ensures asymptotic stability in probability, validated by simulations.
Area of Science:
- Control Systems Engineering
- Nonlinear Dynamics
- Stochastic Systems
Background:
- Investigating adaptive neural network (NN) output-feedback stabilization for stochastic nonlinear strict-feedback systems.
- Addressing challenges with unknown nonlinear terms dependent solely on system output.
- Current methods often require complex compensation for unknown functions.
Purpose of the Study:
- To develop a simplified adaptive NN controller for stochastic nonlinear strict-feedback systems.
- To compensate for unknown nonlinearities using a single NN.
- To achieve asymptotic stability in probability for the closed-loop system.
Main Methods:
- Utilizing the backstepping design technique.
- Employing nonlinear observer design principles.
- Implementing an adaptive neural network to approximate unknown bounding functions.
Main Results:
- A novel output-feedback controller is designed using a single neural network.
- The controller effectively compensates for unknown nonlinear terms.
- The closed-loop system is proven to be asymptotically stable in probability.
Conclusions:
- The proposed control scheme offers a simplified approach to stabilizing complex stochastic nonlinear systems.
- The adaptive NN controller demonstrates effectiveness and robustness.
- This method advances the field of adaptive control for uncertain nonlinear systems.
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