Robust Packetized MPC for Networked Systems Subject to Packet Dropouts and Input Saturation With Quantized Feedback.

Area of Science:

• Control Systems Engineering
• Networked Systems
• Stochastic Systems

Background:

• Networked control systems face challenges from packet dropouts and input saturation.
• Quantized feedback complicates control design due to information loss.

Purpose of the Study:

• To develop a robust packetized predictive control framework for networked systems.
• To address Markovian packet dropouts and input saturation in control loops.

Main Methods:

• A Markov chain model for packet dropout was established.
• A quantized-feedback law and a packet dropout compensation strategy were designed.
• An augmented Markovian jump system model was used for analysis.

Main Results:

• The framework ensures recursive feasibility of the controller design.
• Mean-square stability of the closed-loop systems was proven.
• The method was validated through simulation on a four-tank process system.

Conclusions:

• The proposed robust packetized predictive control framework effectively handles quantized feedback, packet dropouts, and input saturation.
• The developed control strategy guarantees system stability and performance in unreliable network conditions.