Prescribed-Time Performance-Based Data-Driven Control of Permanent Magnet Linear Motors: A Resilient Hierarchical...

Area of Science:

• Control Systems Engineering
• Robotics
• Cyber-Physical Systems

Background:

• Heterogeneous permanent magnet linear motors (PMLMs) face challenges from actuator faults and denial-of-service (DoS) attacks.
• These issues degrade system stability and tracking accuracy, necessitating robust control solutions.

Purpose of the Study:

• To develop a resilient adaptive control strategy for PMLMs under actuator faults and DoS attacks.
• To ensure precise velocity tracking and system stability despite parameter heterogeneity and communication failures.

Main Methods:

• A hierarchical resilient adaptive data-driven control strategy is proposed.
• A distributed resilient prescribed-time observer estimates virtual reference velocity during DoS attacks.
• Dynamic linearization (DL) and a fuzzy adaptive tracking controller approximate unknown fault terms using input/output data.

Main Results:

• The proposed observer effectively estimates velocity under intermittent communication failures.
• The data-driven controller achieves prescribed-time stabilization of observation and tracking errors.
• Simulation results validate the effectiveness of the resilient control scheme.

Conclusions:

• The developed control strategy enhances the robustness of PMLMs against actuator faults and DoS attacks.
• This approach ensures reliable velocity tracking in challenging operational environments.
• The data-driven methodology bypasses the need for precise system models, offering practical advantages.