Event-Triggered Control Strategies in Networked Systems
Summary
Event-triggered control represents a paradigm shift in the management of networked systems by transmitting data only when predefined conditions are met, rather than at fixed sampling intervals. This reduces communication demands, conserves energy in wireless sensors and actuators, and mitigates congestion in shared networks. Such strategies balance the trade-off between resource utilisation and control performance by monitoring the system state or output and initiating updates when the deviation exceeds a threshold. Variants include purely event-triggered schemes, where the controller reacts to threshold crossings, and self-triggered mechanisms, which predict the next transmission instant based on current state estimates. Recent advances address challenges such as time-varying delays, quantisation effects, nonlinearity and stochastic disturbances. Applications span industrial automation, robotics, smart grids and autonomous vehicles, where reliable performance under limited bandwidth, network-induced latency and packet loss is essential for safety and efficiency.
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Event-Triggered Control Strategies in Networked Systems publication trend
The graph below shows the total number of articles in event-triggered control strategies in networked systems across all publications each year (not limited to Nature Index journals).
Technical terms
Networked control systems: Feedback control architectures in which sensors, controllers and actuators communicate over shared digital networks.
Event-triggered control: A sampling strategy where control updates are sent only when a monitored signal violates a predefined threshold.
Self-triggered control: A scheme that computes the next sampling instant in advance, based on current state estimates and known system dynamics, without continuous monitoring.
Quantisation: The process of mapping continuous-valued signals into a finite set of levels, introducing bounded error in digital communication.
Lyapunov-Krasovskii functional: A generalised energy-like function used to assess stability of systems with delays and to derive sufficient conditions via inequalities.
Takagi-Sugeno fuzzy model: A representation of nonlinear systems as a weighted combination of linear subsystems, facilitating controller or filter design through convex optimisation.
ℓ₂-gain: A performance metric quantifying the worst-case amplification of energy from disturbances to system outputs.
References
- Self-Triggered Stabilization of Discrete-Time Linear Systems With Quantized State Measurements. IEEE Transactions on Automatic Control (2022).
- Event-Triggered L2–L∞ Filtering for Network-Based Neutral Systems With Time-Varying Delays via T-S Fuzzy Approach. IEEE Access (2021).
- An ℓ 2 -consistent event-triggered control policy for linear systems. Automatica (2021).
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