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N-step model predictive control with persistent bounded disturbances under AF cooperation protocol.

Wei Zhang1, Tianhao Su1, Hao Wang2

  • 1Shanghai Key Lab of Modern Optical System, and Engineering Research Center of Optical Instrument and System, Ministry of Education, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

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|May 1, 2026
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Summary
This summary is machine-generated.

This study introduces amplify-and-forward cooperation protocol (AFCP) into N-step model predictive control (MPC) for uncertain systems with disturbances. The novel approach ensures mean-square input-to-state stability (MS-ISS) using an observer and a combined optimization algorithm.

Keywords:
-step model predictive controlAmplify-and-forward cooperation protocolMean-square input-to-state stabilityPersistent bounded disturbances

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Area of Science:

  • Control Systems Engineering
  • Uncertain Systems Control
  • Cooperative Control

Background:

  • Model Predictive Control (MPC) is crucial for complex systems.
  • Polytopic uncertainties and persistent disturbances challenge MPC performance.
  • Cooperative control enhances robustness and signal integrity.

Purpose of the Study:

  • To develop a novel N-step model predictive control (MPC) strategy.
  • To integrate the amplify-and-forward cooperation protocol (AFCP) into N-step MPC.
  • To ensure mean-square input-to-state stability (MS-ISS) for uncertain systems with disturbances.

Main Methods:

  • Introduction of the amplify-and-forward cooperation protocol (AFCP) into the N-step MPC framework.
  • Design of a Luenberger observer-based system for state estimation.
  • Formulation of an objective function using mathematical expectation for disturbance handling.
  • Development of a hybrid offline/online optimization algorithm for controller design.

Main Results:

  • Successful integration of AFCP into N-step MPC for the first time.
  • Effective mitigation of signal distortion through cooperative transmission.
  • Demonstration of mean-square input-to-state stability (MS-ISS) under uncertainties and disturbances.
  • Validation of the proposed N-step MPC strategy via simulation.

Conclusions:

  • The proposed N-step MPC strategy with AFCP enhances control performance for uncertain systems.
  • The Luenberger observer and hybrid optimization algorithm effectively address practical challenges.
  • The approach guarantees MS-ISS, providing robust and stable control solutions.