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

• Published in: IEEE transactions on cybernetics Vol. 53; no. 11; pp. 6987 - 6997
• Main Authors: Zhang, Langwen, Wang, Bohui, Zheng, Yuanshi, Zemouche, Ali, Zhao, Xudong, Shen, Chao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Automatic Control Engineering; Closed loops; Computer Science; Control methods; Control systems design; Controllers; Cost function; Feedback control; Input saturation; Markov chains; networked systems; packet dropouts; Packet loss; packetized predictive control; Predictive control; Quantization (signal); quantized feedback; Robust control; Stability analysis; Uncertain systems; Uncertainty; Input saturation; quantized feedback; packet dropouts; networked systems; packetized predictive control
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2022.3166855

This article develops a robust packetized predictive control framework to deal with the quantized-feedback control problem of networked systems subject to Markovian packet dropouts and input saturation. In the proposed framework, the Markov chain model of packet dropout is established from the link of the controller to the actuator. To deal with the quantized measurements, a robust packetized predictive control method is presented with a quantized-feedback law. The problem of unreliable transmission is addressed by proposing a packet dropout compensation strategy with a forgetting factor. An augmented Markovian jump system model is established to take the packet dropouts into account. The synthesis of packetized predictive control is then developed by minimizing a worst case cost function with respect to the model uncertainties. The recursive feasibility of the proposed controller design problem and the mean-square stability of the closed-loop systems are proved, respectively. The proposed packetized predictive control method is demonstrated by simulating a four-tank process system.