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Neural Network-Based Sliding Mode Control for Semi-Markov Jumping Systems With Singular Perturbation
IEEE Transactions on Cybernetics
|October 30, 2024
Summary
This study introduces a dynamic event-triggered protocol for semi-Markov jumping systems, enhancing control performance and reducing triggers. The sliding mode control ensures system stability and reachability.
Area of Science:
- Control Systems Engineering
- Stochastic Systems Analysis
Background:
- Semi-Markov jumping systems (SMSPSs) exhibit complex mode-switching dynamics.
- Event-triggered protocols (ETPs) aim to optimize control resource utilization.
Purpose of the Study:
- To develop a novel dynamic ETP for SMSPSs with singular perturbation.
- To ensure system stability and performance while minimizing control signal transmissions.
Main Methods:
- A parameter-based dynamic ETP incorporating radial basis function neural network (RBFNN) weight estimation and internal dynamic variables.
- Lyapunov's theory to establish stability criteria.
- Sliding mode control (SMC) design with a convergence factor for reachability.
Main Results:
- Sufficient criteria for mean-square exponential stability of the closed-loop system were derived.
- The proposed dynamic ETP effectively reduces the triggering frequency.
- The SMC scheme guarantees system reachability.
Conclusions:
- The developed control methodology is effective and applicable for semi-Markov singularly perturbed systems.
- The integration of dynamic ETP and SMC offers a robust control solution.
- The approach balances performance with reduced communication load.
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