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Control Systems

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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Iterative learning robust security predictive tracking control for small delay batch processes: Deception attacks on

Hui Li¹, Shiqi Wang¹, Ping Li¹

¹School of Electronics and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China.

ISA Transactions

|October 15, 2024

Summary

This summary is machine-generated.

This study introduces a novel iterative learning robust security predictive tracking control for batch processes facing deception attacks. The method enhances security and efficiency by incorporating attack data and online stability calculations, improving control input adjustment.

Keywords:

Batch process Deception attacks Iterative learning control Predictive control

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Area of Science:

Control Engineering
Cybersecurity
Process Systems Engineering

Background:

Batch processes increasingly require robust security due to network control technology integration.
Deception attacks pose a significant threat to the stability and efficiency of these systems.
Existing iterative learning control methods fail to address deception attack masking and excessive input issues.

Purpose of the Study:

To develop an iterative learning robust security predictive tracking control approach for small time delay batch processes.
To enhance system robustness against deception attacks.
To improve energy and resource utilization by optimizing control inputs.

Main Methods:

Incorporation of robust security invariant sets to constrain system states within safe ranges.
Iterative learning controller design utilizing historical deception attack data for continuous optimization.
Online calculation of stability conditions to obtain real-time control law gains.
Input-to-state stability analysis to prove system robustness.

Main Results:

The developed approach effectively enhances system robustness against deception attacks.
Online gain calculation prevents excessive control inputs, improving energy and resource efficiency.
Simulations confirm the method's effectiveness and feasibility in real-time applications.
The system demonstrates input-to-state stability.

Conclusions:

The proposed iterative learning robust security predictive tracking control method offers a viable solution for securing batch processes against deception attacks.
The integration of robust invariant sets and online stability calculations leads to more efficient and secure control.
This research contributes to the advancement of secure and efficient control strategies in networked batch systems.