Adaptive Neural Network-Based Asynchronous Control for Switching Cyber-Physical Systems With Unknown Dead Zone
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces adaptive neural network control for switching cyber-physical systems with unknown dead zones. The method ensures system stability despite asynchronous switching and unknown inputs, validated by simulations.
Area Of Science
- Control Systems Engineering
- Cyber-Physical Systems
- Artificial Intelligence
Background
- Switching cyber-physical systems (CPS) present control challenges due to asynchronous switching and unknown system parameters.
- Existing methods often rely on Markov/semi-Markov processes, which can be computationally intensive.
- Unknown dead zones in network environments further complicate control design for CPS.
Purpose Of The Study
- To develop an adaptive neural network control strategy for switching CPS with unknown dead zones.
- To analyze system behavior using a generalized switching rule, reducing computational load.
- To ensure the stability and boundedness of the closed-loop system under uncertain conditions.
Main Methods
- Utilized a generalized switching rule to model subsystem switching dynamics.
- Employed adaptive neural networks for control law design to handle unknown dead zones.
- Developed a dynamically adjusted saturation-based observer to mitigate unforeseen information effects.
- Applied a Lyapunov function approach for stability analysis, ensuring boundedness in probability.
Main Results
- Established sufficient criteria for ensuring the closed-loop system remains bounded in probability.
- Demonstrated the effectiveness of the adaptive neural network control law in managing unknown dead zones.
- Showcased the flexibility of the saturation-based observer through dynamic adjustment of its saturation level.
Conclusions
- The proposed adaptive neural network asynchronous control methodology is effective for switching CPS with unknown dead zones.
- The generalized switching rule and observer design offer practical advantages in terms of computational load and flexibility.
- Simulation results validate the robustness and practicality of the developed control strategy.
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