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Tracking control of multi-input affine nonlinear dynamical systems with unknown nonlinearities using dynamical neural
1Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania.
Summary
This study introduces a dynamic neural network tracking controller for complex nonlinear systems. The controller ensures stability and accuracy without needing prior knowledge of system parameters.
Area of Science:
- Control Systems Engineering
- Nonlinear Dynamics
- Artificial Intelligence
Background:
- Nonlinear dynamical systems present significant control challenges.
- Accurate modeling and control are crucial for system performance.
- Existing methods often require detailed system knowledge.
Purpose of the Study:
- To design a novel tracking controller for nonlinear systems.
- To utilize dynamic neural network models for unknown system dynamics.
- To ensure stability and robustness of the control system.
Main Methods:
- Development of a dynamic neural network model.
- Application of Lyapunov stability theory for analysis.
- Design of a smooth tracking controller architecture.
Main Results:
- Guaranteed uniform ultimate boundedness of tracking error.
- Stability of all closed-loop signals demonstrated.
- Controller performance validated through simulations.
Conclusions:
- The proposed controller effectively manages unknown nonlinear systems.
- Lyapunov stability ensures reliable system performance.
- The method avoids the need for prior bounds on neural network weights or errors.
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