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

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.