Fault-Tolerant Control Strategies for Actuator Failures

Fault-tolerant control (FTC) for actuator failures is a vibrant research area focused on ensuring the continued safe operation of dynamical systems when actuators degrade or cease functioning. Actuators are critical components that translate control commands into physical actions; their failure can lead to performance loss or catastrophic outcomes. FTC approaches typically comprise fault detection and isolation, controller reconfiguration and robust design. Passive FTC designs embed robustness into a single controller to tolerate a predefined range of faults without reconfiguration, whereas active FTC designs incorporate real-time fault diagnosis and adaptive control reconfiguration. Key strategies include observer-based fault estimation, model predictive control frameworks, virtual actuator modules to mask faulty components, and data-driven techniques that leverage historical system data. These methods are underpinned by mathematical tools such as linear matrix inequalities for stability guarantees and optimisation schemes to balance performance and robustness. Across renewable energy systems, autonomous vehicles, aerospace applications and industrial processes, FTC architectures enhance resilience, minimise downtime and extend operational life. The interplay between model-based and data-driven methods, the integration of fault diagnosis with control, and the development of lightweight algorithms suited for real-time implementation define current trends in actuator-failure mitigation.

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Fault-Tolerant Control Strategies for Actuator Failures publication trend

The graph below shows the total number of articles in fault-tolerant control strategies for actuator failures across all publications each year (not limited to Nature Index journals).

Technical terms

Fault-Tolerant Control (FTC): A control philosophy that enables a system to maintain acceptable performance in the presence of component faults.
Actuator Failure: The partial or complete loss of an actuator’s ability to effect control inputs, leading to degraded system behaviour.
Virtual Actuator: A software or hardware element that replaces or emulates a faulty actuator by modifying control signals to the remaining actuators.
Model Predictive Control (MPC): An optimisation-based control technique that computes actions by solving a constrained prediction problem over a future horizon.
Linear Matrix Inequalities (LMIs): Convex constraints expressed as matrix inequalities used in control theory to ensure stability and performance.
Koopman Operator: A linear operator that describes the evolution of nonlinear dynamical systems in a higher-dimensional function space.

References

1. Observer-Based Robust Fault Predictive Control for Wind Turbine Time-Delay Systems with Sensor and Actuator Faults. Energies (2023).
2. Koopman fault‐tolerant model predictive control. IET Control Theory and Applications (2024).
3. A Fuzzy Virtual Actuator for Automated Guided Vehicles. Sensors (2020).