Integrated Sliding Mode Control and Neural Networks Based Packet Disordering Prediction for Nonlinear Networked
IEEE Transactions on Neural Networks and Learning Systems
|December 19, 2018
Summary
This study introduces a novel neural network scheme for predicting packet disordering and uses sliding mode control (SMC) to stabilize nonlinear networked control systems (NCSs) with unknown disordering.
Area of Science:
- Control Engineering
- Artificial Intelligence
- Network Systems
Background:
- Nonlinear networked control systems (NCSs) face challenges due to unknown packet disordering.
- Packet disordering can significantly degrade system performance and stability.
Purpose of the Study:
- To develop a robust control scheme for NCSs with unknown packet disordering.
- To enhance the stability and predictive capabilities of nonlinear NCSs.
Main Methods:
- Proposed a novel scheme using stochastic configuration networks (SCNs) for predicting unknown packet disordering.
- Developed a new sliding mode control (SMC) strategy integrated with SCNs for advanced system learning and control.
- Introduced a novel measurement for quantifying packet disordering and leveraged a new signal principle for stochastic parameters, leading to a Markovian jumping system.
Main Results:
- The SCNs effectively learned and predicted unknown packet disordering.
- The integrated SMC scheme successfully stabilized the nonlinear NCSs.
- Simulation results verified the effectiveness of the proposed approach in handling packet disordering.
Conclusions:
- The proposed neural network-based prediction and SMC scheme offers an effective solution for stabilizing nonlinear NCSs with unknown packet disordering.
- This approach enhances system robustness and control performance in the presence of network-induced uncertainties.
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