Adaptive Resilient Control in Cyber-Physical Systems

As digital and physical domains converge in networked environments, cyber-physical systems (CPSs) have become integral to critical infrastructures such as power grids, manufacturing, transportation and autonomous platforms. Adaptive resilient control addresses the twin challenges of uncertainty and malicious interference by combining real-time parameter adjustment with fault-tolerant strategies. Adaptive methods compensate for unknown dynamics and time-varying disturbances, while resilient architectures detect and mitigate cyber-attacks, actuator faults or sensor deception. Techniques including backstepping, neural‐network observers, fuzzy logic approximators and barrier Lyapunov functions ensure stability despite unmodeled nonlinearities, time delays and stochastic perturbations. Event-triggered schemes reduce communication overhead by updating control actions only upon significant state changes, and prescribed performance designs allow preconfigured transient responses. Recent advances emphasise data-driven estimation, finite‐time convergence guarantees and multi‐channel security, paving the way for robust, scalable CPS deployments that maintain safety and reliability under adversarial conditions.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Adaptive Resilient Control in Cyber-Physical Systems publication trend

The graph below shows the total number of articles in adaptive resilient control in cyber-physical systems across all publications each year (not limited to Nature Index journals).

Technical terms

Cyber-Physical System (CPS): An integrated system where computational elements monitor and control physical processes through networks of sensors and actuators.

Adaptive Control: A methodology that adjusts controller parameters in real time to manage uncertainties and time-varying dynamics.

Resilient Control: Strategies designed to maintain system stability and performance despite faults, attacks or unexpected disturbances.

Backstepping: A recursive, Lyapunov-based design technique for stabilising nonlinear systems by constructing intermediate control laws.

Fuzzy Logic System: A rule-based framework that uses approximate reasoning to handle uncertainty and approximate nonlinear dynamics.

Luenberger Observer: A state estimation algorithm that reconstructs unmeasured system states from output measurements.

Lyapunov Function: A scalar function used to assess and guarantee the stability of dynamical systems.

Event-Triggered Control: A control paradigm where updates occur only when system states meet predefined criteria, reducing communication and computation loads.

Nussbaum Gain: A special adaptive gain function that addresses unknown control directions or efficiency in uncertain systems.

References

1. Adaptive Fuzzy Control for State-Constrained Nonlinear Cyber-Physical Systems With Unmodeled Dynamics Against Malicious Attacks. IEEE Transactions on Industrial Cyber-Physical Systems (2023).
2. Event-Triggered Adaptive Finite-Time Control for Switched Cyberphysical Systems With Uncertain Deception Attacks. IEEE Transactions on Industrial Cyber-Physical Systems (2023).
3. Adaptive Neural Network-Based Resilient Output Feedback Control of Cyber-Physical Systems Under Multi-Channel Stochastic False Data Injection. IEEE Access (2024).