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Control of nonlinear dynamical systems using neural networks: controllability and stabilization
1Dept. of Comput. Sci. and Electr. Eng., Oregon Graduate Inst. of Sci. and Technol., Klamath Falls, OR.
IEEE Transactions on Neural Networks
|January 1, 1993
Summary
This study demonstrates practical neural network controller design for dynamical systems, leveraging nonlinear control theory. Simulation results validate the effectiveness of these controllers for system stabilization when the system
Area of Science:
- Control Engineering
- Artificial Intelligence
- Dynamical Systems
Background:
- Designing effective controllers for dynamical systems is crucial for stability.
- Neural networks offer potential for advanced control strategies.
- Nonlinear control theory provides foundational principles for system analysis.
Purpose of the Study:
- To explore the practical design of neural network controllers.
- To apply nonlinear control theory to neural network-based stabilization.
- To demonstrate controller viability through simulation.
Main Methods:
- Utilizing results from nonlinear control theory.
- Developing neural network architectures for control.
- Implementing and simulating control strategies for dynamical systems.
Main Results:
- Neural network controllers are shown to be practically viable.
- Effective stabilization of dynamical systems around equilibrium points is achieved.
- Simulation results confirm theoretical predictions.
Conclusions:
- Neural networks can be effectively designed as practical controllers.
- The integration of nonlinear control theory enhances controller design.
- The proposed methods offer a viable approach for system stabilization.
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