Perturbed cooperative-state feedback strategy for model predictive networked control of interconnected systems

Area of Science:

• Control Systems Engineering
• Networked Systems
• Power Systems

Background:

• Interconnected systems face challenges with sensor data losses and communication network disruptions.
• Decentralized control is crucial for managing complex, large-scale systems like power grids.
• Existing control strategies may not adequately address combined data and network integrity issues.

Purpose of the Study:

• To introduce a novel perturbed cooperative-state feedback (PSF) strategy for robust control of interconnected systems.
• To address and resolve issues related to sensor data losses and communication network breaks.
• To demonstrate the effectiveness of the PSF strategy in a decentralized model predictive control (MPC) framework.

Main Methods:

• The proposed PSF strategy integrates a cooperative-state feedback component with a perturbation variable.
• Implementation within a decentralized model predictive control (MPC) scheme.
• Ensuring system stability using a stability constraint and a non-monotonic storage function (ΔV(x(k))≥0) derived from dissipative systems theory.

Main Results:

• The PSF strategy effectively manages sensor data losses and communication network breaks.
• The decentralized MPC scheme with the PSF strategy maintains system stability.
• Numerical simulations for automatic generation control in power systems validate the strategy's performance.

Conclusions:

• The perturbed cooperative-state feedback (PSF) strategy offers a robust solution for controlling interconnected systems with communication uncertainties.
• The integration of PSF within decentralized MPC provides a stable and effective control framework.
• The approach is particularly effective for applications like automatic generation control in power systems.