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Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm
Shiqin Wang1, Yuting Cao2, Tingwen Huang3
1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Summary
This study introduces event-triggered sliding mode control (SMC) for neural networks using continuous or periodic sampling. These methods ensure system stability and avoid the Zeno phenomenon for efficient control.
Area of Science:
- Control Theory
- Artificial Intelligence
- Systems Engineering
Background:
- Neural networks require robust control strategies for stable operation.
- Event-triggered control offers potential for reduced computational load compared to time-triggered systems.
- Sliding Mode Control (SMC) is a powerful technique for handling system uncertainties and disturbances.
Purpose of the Study:
- To develop and analyze sliding mode control (SMC) for neural networks using event-triggered algorithms.
- To investigate both continuous and periodic sampling event-triggered schemes.
- To ensure the stability and efficiency of the proposed control methods.
Main Methods:
- Development of an SMC strategy with a continuous sampling event-triggered scheme.
- Design of a more economical periodic sampling event-triggered SMC algorithm.
- Mathematical analysis to guarantee robust stability and prevent Zeno phenomenon.
Main Results:
- Achieved practical sliding mode with the continuous sampling event-triggered scheme.
- Ensured a positive lower bound on inter-event time intervals, avoiding Zeno behavior.
- Demonstrated robust stability of the augmented system using the periodic sampling event-triggered algorithm.
Conclusions:
- The proposed event-triggered SMC algorithms are effective for neural networks.
- Continuous and periodic sampling schemes offer viable solutions for robust and efficient control.
- Theoretical results are validated through illustrative examples.
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