Stochastic model predictive control framework for resilient cyber-physical systems: review and perspectives.

Area of Science:

• Control Systems Engineering
• Cyber-Physical Systems
• Industrial 4.0

Background:

• Cyber-physical systems (CPS) are central to Industrial 4.0, integrating physical processes with computation and networking.
• Ensuring CPS robustness, safety, and resilience against uncertainties and cyber attacks is critical.
• Stochastic model predictive control (MPC) offers a framework for addressing these challenges.

Purpose of the Study:

• To provide a comprehensive overview of stochastic MPC algorithms for CPS.
• To discuss the integration of resilience mechanisms into stochastic MPC for enhanced security.
• To present a framework for resilient CPS and identify future research directions.

Main Methods:

• Review of existing stochastic MPC algorithms for linear and nonlinear systems with probabilistic constraints.
• Discussion on extending stochastic MPC to incorporate resilience against various malicious attacks.
• Development of an architectural framework for resilient CPS based on stochastic MPC.

Main Results:

• Existing stochastic MPC algorithms are suitable for constrained systems under uncertainty.
• Integration of resilience mechanisms enhances CPS security against cyber threats.
• A proposed framework integrates stochastic MPC for resilient CPS design.

Conclusions:

• Stochastic MPC is a promising approach for developing robust and resilient CPS.
• Further research is needed to address complex attack scenarios and system dynamics.
• The reviewed methods and proposed framework contribute to the advancement of symbiotic autonomous systems.