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Neural-network-based approximate output regulation of discrete-time nonlinear systems
1Department of Automation, Xiamen University, Fujian 361005, China. wylan@xmu.edu.cn
IEEE Transactions on Neural Networks
|August 3, 2007
Summary
This study introduces a novel approach for discrete nonlinear output regulation using feedforward neural networks (NNs) and online optimization. This method overcomes limitations of traditional techniques for complex systems and uncertainty.
Area of Science:
- Control Theory
- Applied Mathematics
- Artificial Intelligence
Background:
- Traditional discrete nonlinear output regulation requires solving complex discrete regulator equations offline.
- These methods struggle with complex systems and are unreliable for systems with uncertainty.
Purpose of the Study:
- To develop a new approach for discrete nonlinear output regulation.
- To overcome the limitations of existing methods by avoiding explicit solutions of discrete regulator equations.
Main Methods:
- Combining feedforward neural networks (NNs) for approximation capabilities.
- Implementing an online parameter optimization mechanism.
- Developing a discrete-time counterpart to continuous-time methods.
Main Results:
- Successfully addresses the discrete nonlinear output regulation problem.
- Avoids the explicit solution of discrete regulator equations.
- Offers a potential solution for systems with uncertainty.
Conclusions:
- The proposed NN-based approach provides an effective alternative for discrete nonlinear output regulation.
- This method is more robust for complex systems and systems with uncertainty.
- It extends previous work on continuous-time nonlinear output regulation.
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