Robust model reference adaptive output feedback tracking for uncertain linear systems with actuator fault based on...

Area of Science:

• Control Systems Engineering
• Adaptive Control Theory
• Robotics and Automation

Background:

• Linear systems often face challenges like modeling uncertainties, disturbances, and actuator faults.
• Conventional adaptive controllers may cease parameter adaptation within dead-zone regions, leading to tracking errors.
• Existing methods often require separate Fault Detection and Diagnosis (FDD) units.

Purpose of the Study:

• To develop robust model reference adaptive tracking controllers for Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) linear systems.
• To improve controller performance in the presence of modeling uncertainties, unknown disturbances, and actuator faults.
• To eliminate the need for a Fault Detection and Diagnosis (FDD) unit.

Main Methods:

• Proposed two new lemmas for both SISO and MIMO systems to improve the dead-zone modification rule.
• Introduced an additive term to the control signal based on tracking error within the dead-zone.
• Utilized Lyapunov functions to prove closed-loop system stability and zero tracking error.

Main Results:

• The improved dead-zone modification rule ensures tracking error tends to zero for any reference signal.
• The proposed scheme achieves full reference tracking by reinforcing the control signal within the dead-zone.
• Demonstrated bounded signals for the closed-loop system under faulty conditions without an FDD unit.
• Numerical simulations confirmed the validity and performance for SISO and MIMO systems.

Conclusions:

• The novel adaptive control approach effectively handles uncertainties, disturbances, and actuator faults.
• The proposed method guarantees robust tracking performance and system stability.
• Eliminating the FDD unit simplifies the control architecture and enhances practical applicability.