Near-Optimal Resilient Control Strategy Design for State-Saturated Networked Systems Under Stochastic Communication...

Area of Science:

• Control Systems Engineering
• Systems Theory
• Stochastic Processes

Background:

• Discrete time-varying systems present challenges due to uncertainties.
• Stochastic communication protocols (SCPs) introduce randomness in data transmission.
• System complexities include state saturations, additive nonlinearities, and controller gain perturbations.

Purpose of the Study:

• To design a near-optimal resilient control strategy for discrete time-varying systems.
• To address simultaneous uncertainties including SCPs, gain perturbations, state saturations, and nonlinearities.
• To ensure stability and performance in complex system environments.

Main Methods:

• Deriving an upper bound for the system's cost function.
• Minimizing the cost function upper bound using completing-the-square and Moore-Penrose pseudo-inverse.
• Iterative solution via coupled backward Riccati-like recursions.

Main Results:

• A novel resilient control strategy is proposed.
• The infinite horizon case is analyzed, providing an explicit upper bound for the cost function.
• Numerical simulations on power systems validate the control algorithms' effectiveness.

Conclusions:

• The developed resilient control strategy effectively manages uncertainties in discrete time-varying systems.
• The approach provides a robust framework for systems with stochastic communication and complex dynamics.
• The findings are applicable to real-world systems like power grids.