A Robust Fault-Tolerant Predictive Control for Discrete-Time Linear Systems Subject to Sensor and Actuator Faults

Area of Science:

• Control Systems Engineering
• Systems Theory
• Fault Diagnosis and Tolerance

Background:

• Model predictive control (MPC) is widely used for controlling complex systems.
• Systems are vulnerable to sensor and actuator faults, external disturbances, and input constraints.
• Existing fault-tolerant control methods may have limitations in handling multiple uncertainties simultaneously.

Purpose of the Study:

• To develop a robust fault-tolerant model predictive control (RFTPC) approach for discrete-time linear systems.
• To address challenges posed by sensor faults, actuator faults, disturbances, and input constraints.
• To enhance system observation accuracy and reduce the impact of faults.

Main Methods:

• A virtual observer is designed to improve observation accuracy and mitigate fault effects.
• A real observer is established based on the virtual observer for enhanced performance.
• An optimized cost function is integrated into the RFTPC design for robust stability and disturbance rejection.
• A linear matrix inequality (LMI) approach is utilized to derive stability conditions.

Main Results:

• The proposed RFTPC approach effectively handles discrete-time linear systems with sensor and actuator faults.
• The observer-based strategy improves state estimation accuracy in the presence of faults.
• The LMI formulation provides sufficient conditions for robust stability of the closed-loop system.
• Simulation results validate the controller's applicability and efficiency.

Conclusions:

• The developed RFTPC method offers a robust solution for controlling linear systems with sensor and actuator faults.
• The combination of virtual/real observers and optimized MPC enhances system resilience.
• The LMI-based design ensures guaranteed stability and disturbance rejection capabilities.