Adaptive Neural Output Feedback Compensation Control for Intermittent Actuator Faults Using Command-Filtered
IEEE Transactions on Neural Networks and Learning Systems
|November 15, 2019
Summary
This study introduces an adaptive neural control scheme to manage unknown intermittent actuator faults in decentralized nonlinear systems. The method ensures system stability and tunable tracking error performance despite uncertainties.
Area of Science:
- Control Systems Engineering
- Nonlinear Dynamics
- Artificial Intelligence in Control
Background:
- Decentralized nonlinear systems face challenges with unknown intermittent actuator faults.
- Existing control strategies offer limited solutions for such complex fault scenarios.
Purpose of the Study:
- To develop an adaptive neural output feedback compensation control scheme.
- To effectively address unknown intermittent actuator faults in uncertain decentralized nonlinear systems.
Main Methods:
- Command-filtered backstepping combined with a bank of observers for state estimation.
- Neural networks with random hidden nodes to approximate unknown system functions.
- A smooth projection algorithm for online parameter updates and a modified Lyapunov function for stability analysis.
Main Results:
- Ensured boundedness of all closed-loop system signals.
- Tracking error bounds are dependent on design parameters and fault characteristics.
- Elimination of the peaking phenomenon and establishment of tunable transient performance bounds.
Conclusions:
- The proposed adaptive neural control scheme effectively compensates for unknown intermittent actuator faults.
- The control strategy guarantees system stability and offers tunable performance for uncertain decentralized nonlinear systems.
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