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Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities
Mou Chen1, Shuzhi Sam Ge, Bernard Voon Ee How
1Department of Electrical and Computer Engineering, National University of Singapore, Singapore. chenmou@nuaa.edu.cn
IEEE Transactions on Neural Networks
|March 19, 2010
Summary
This study introduces robust adaptive neural network control for uncertain nonlinear systems. The novel approach guarantees system stability despite unknown parameters and input nonlinearities, enhancing control performance.
Area of Science:
- Control Systems Engineering
- Nonlinear Dynamics
- Artificial Intelligence in Control
Background:
- Advanced control strategies are crucial for uncertain nonlinear systems.
- Existing methods often struggle with unknown control matrices and input nonlinearities like saturation and deadzone.
- Neural networks offer powerful approximation capabilities for complex system dynamics.
Purpose of the Study:
- To develop a robust adaptive neural network control scheme for uncertain multiple-input-multiple-output (MIMO) nonlinear systems.
- To address challenges posed by unknown control coefficient matrices and nonsymmetric input nonlinearities.
- To ensure guaranteed stability and improved performance in closed-loop systems.
Main Methods:
- Integration of variable structure control (VSC) with backstepping and Lyapunov synthesis.
- Utilization of adaptive neural networks (NNs) for approximating unknown system functions.
- Implementation of command filters to manage virtual control law constraints and simplify computations.
- Elimination of restrictive assumptions on NN approximation and error bounds.
Main Results:
- The proposed adaptive NN control scheme effectively handles unknown control coefficient matrices and input nonlinearities.
- Guaranteed semiglobal uniform ultimate boundedness of all closed-loop system signals.
- Command filters successfully managed virtual control constraints, avoiding complex derivative calculations.
- Simulation results validated the effectiveness and robustness of the developed control strategy.
Conclusions:
- The presented robust adaptive neural network control provides a stable and effective solution for complex uncertain MIMO nonlinear systems.
- The method overcomes limitations of previous approaches by relaxing common assumptions and incorporating advanced control techniques.
- This work contributes a significant advancement in the field of adaptive control for challenging nonlinear systems.
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