H _∞ Optimal Tracking Control for Two-Time-Scale Supply Chain Systems Subject to DoS Attack.

Area of Science:

• Control Systems Engineering
• Supply Chain Management
• Operations Research

Background:

• Supply chains face challenges with demand uncertainty amplification (bullwhip effect) and time-scale variations.
• Denial-of-service (DoS) attacks disrupt information flow, impacting control system performance.
• Traditional methods for handling time-scale variations can be computationally complex and sensitive.

Purpose of the Study:

• To develop an $H_{\infty}$ optimal tracking control strategy for dual-time-scale supply chains vulnerable to DoS attacks.
• To attenuate the bullwhip effect and enhance inventory tracking accuracy under perturbations.
• To address information loss from DoS attacks using real-time state compensation.

Main Methods:

• Singular perturbation theory to decompose the system into fast and slow subsystems.
• Policy iteration (PI)-based $H_{\infty}$ optimal control for bullwhip effect attenuation.
• Nonlinear autoregressive (NAR) neural network for real-time state compensation against DoS attacks.

Main Results:

• The proposed method reduced the bullwhip effect by 63% in a potassium carbonate production case study.
• Demonstrated superior steady-state accuracy and dynamic performance compared to existing methods.
• Effectively compensated for information loss caused by DoS attacks.

Conclusions:

• The integrated PI and NAR neural network approach offers an effective solution for robust supply chain control.
• The method enhances system resilience against demand uncertainty and cyber-attacks.
• Singular perturbation effectively simplifies complex dual-time-scale systems for control design.