Fault reconstruction and resilient control for discrete-time stochastic systems.

Area of Science:

• Control Systems Engineering
• Stochastic Systems Analysis
• Fault-Tolerant Control

Background:

• Stochastic Brownian systems often face challenges with unknown inputs and unexpected faults.
• Existing control techniques struggle with the generality of perturbations in states, control inputs, uncertainties, and faults.
• Decoupling unknown input uncertainties remains a significant challenge in complex systems.

Purpose of the Study:

• To propose a novel resilient control technique for discrete-time stochastic Brownian systems.
• To address systems with simultaneous unknown inputs and unexpected faults, including state, control, uncertainty, and fault perturbations.
• To develop a method that ensures reliable system output even under fault conditions.

Main Methods:

• An innovative observer using augmented system approach and decomposition observer for state and fault estimation.
• Optimization algorithms integrated for enhanced observer performance.
• Fault reconstruction-based signal compensation to mitigate actuator and sensor fault effects.
• Observer-based controller design for closed-loop stability and robustness.

Main Results:

• Simultaneous estimation of system states and faults achieved.
• Effective alleviation of actuator and sensor fault impacts through signal compensation.
• Enhanced stability and robustness of the closed-loop dynamic system demonstrated.
• Successful validation on both linear and Lipschitz nonlinear systems.

Conclusions:

• The proposed resilient control technique ensures reliable system performance under faults.
• The method is applicable to a general class of discrete-time stochastic Brownian systems.
• Validated through simulations on electromechanical servo and aircraft systems, proving practical applicability.