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Adaptive neural control of uncertain MIMO nonlinear systems
1Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Republic of Singapore. elegesz@nus.edu.sg
IEEE Transactions on Neural Networks
|September 24, 2004
Summary
This study introduces adaptive neural control for uncertain nonlinear multi-input/multi-output (MIMO) systems. The proposed method ensures stability and precise trajectory tracking without controller singularity.
Area of Science:
- Control Systems Engineering
- Nonlinear Dynamics
- Artificial Intelligence in Control
Background:
- Uncertain nonlinear multi-input/multi-output (MIMO) systems pose significant control challenges.
- Existing control schemes often struggle with unknown nonlinearities and parametric uncertainties.
Purpose of the Study:
- To develop novel adaptive neural control schemes for two classes of uncertain MIMO nonlinear systems.
- To address controller singularity and ensure system stability and performance.
Main Methods:
- Utilizing block-triangular structure properties for nested iterative stability analysis.
- Exploiting affine term properties to avoid controller singularity without projection algorithms.
- Employing adaptive neural networks for robust control design.
Main Results:
- Achieved semiglobal uniform ultimate boundedness (SGUUB) for all closed-loop signals.
- Demonstrated convergence of system outputs to desired trajectories within a small neighborhood.
- Verified control performance through parameter selection and simulation.
Conclusions:
- The proposed neural control schemes provide systematic design procedures for uncertain MIMO nonlinear systems.
- The methods effectively handle unknown nonlinearities and parametric uncertainties.
- Simulation results confirm the practical effectiveness of the developed adaptive control approach.