Fault Detection and Control Strategies in Nonlinear Systems

Fault detection and control in nonlinear systems address the identification, isolation and accommodation of unexpected changes in system behaviour that arise from sensor or actuator malfunctions. Nonlinear dynamics, characterised by state‐dependent parameters and interactions, pose significant challenges to conventional linear methods. Model-based observers, particularly sliding mode observers, exploit discontinuous control actions to enforce system trajectories onto known manifolds, enabling robust reconstruction of faults even in the presence of disturbances. Complementary approaches employ machine-learning techniques, such as recurrent neural networks, to learn complex residual patterns for simultaneous state and fault estimation without requiring an explicit physical model. Fault-tolerant control schemes integrate these detection mechanisms with control allocation strategies that redistribute or reconfigure actuator commands to maintain safe operation. Applications span aerospace systems, microgrids, robotics and automotive platforms, where high reliability and rapid response to faults are critical. Recent advances focus on balancing sensitivity to incipient faults against robustness to uncertainties, achieving finite-time convergence of fault estimates and reducing chattering or spurious alarms.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Fault Detection and Control Strategies in Nonlinear Systems publication trend

The graph below shows the total number of articles in fault detection and control strategies in nonlinear systems across all publications each year (not limited to Nature Index journals).

Technical terms

Nonlinear system: A dynamic system in which output response is not directly proportional to input, often exhibiting state-dependent behaviour and complex interactions.

Observer: A computational algorithm that infers unmeasured states or fault signals from available input and output measurements.

Sliding mode observer (SMO): An observer design using discontinuous control actions to drive estimation errors to zero in finite time, enabling robust fault reconstruction.

Fault-tolerant control (FTC): A control methodology that maintains acceptable system performance by detecting faults and reconfiguring control actions or resources.

Linear parameter-varying (LPV) system: A representation of a nonlinear plant as a linear model whose matrices vary with measurable scheduling parameters.

References

1. Sliding Mode Observer-Based Fault-Tolerant Secondary Control of Microgrids. Electronics (2020).
2. Flight evaluation of a sliding mode online control allocation scheme for fault tolerant control. Automatica (2020).
3. Recurrent Neural Network Based Robust Actuator and Sensor Fault Estimation for Satellite Attitude Control System. IEEE Access (2020).