Event-Triggered Control Strategies for Networked Fuzzy Systems
Summary
Event-triggered control strategies for networked fuzzy systems represent a paradigm shift in the management of communication resources within distributed control architectures. By transmitting data only when a predefined event criterion is met, these schemes mitigate the burden on limited bandwidth and reduce energy consumption in sensor–actuator networks. Fuzzy modelling, often based on Takagi–Sugeno structures, accommodates nonlinearities and uncertainties by interpolating between local linear models. The integration of event-triggered mechanisms with fuzzy controllers addresses issues such as time-varying delays, quantisation effects and packet losses. Rigorous stability and performance analyses employ Lyapunov–Krasovskii functionals together with linear matrix inequality techniques to derive sufficient conditions for asymptotic or exponential convergence. Practical implementations span industrial automation, cooperative robotics and autonomous vehicles, where reliable operation under constrained communication is critical. Recent advances have introduced adaptive triggering thresholds, dynamic delay compensation and quantisation-aware filtering, enhancing robustness and global applicability.
Research from Nature Portfolio
Recent studies have developed a discrete-time dynamic event-triggered control framework for networked predictive systems subject to random delays and disturbances. In this approach, sensor data are updated only when necessary, modelled as a time-delay singular Markovian jump system with time-varying switching. A dynamic delay-compensation control strategy is proposed, and asymptotic stability is guaranteed via a tailored Lyapunov–Krasovskii functional coupled with linear matrix inequalities. Simulation results confirm that communication load is significantly reduced without sacrificing robustness. Other foundational work within the past two years has explored the synthesis of event-triggered fuzzy controllers under varying network conditions, illustrating how dynamic threshold adjustment can balance performance and resource utilisation.
Event-Triggered Control Strategies for Networked Fuzzy Systems publication trend
The graph below shows the total number of articles in event-triggered control strategies for networked fuzzy systems across all publications each year (not limited to Nature Index journals).
Technical terms
Event-triggered control: A control paradigm in which signals are transmitted only when a specified condition is met, reducing communication load.
Fuzzy system: A model that represents nonlinear systems by interpolating multiple local linear models, often via membership functions.
Takagi–Sugeno model: A fuzzy modelling framework in which the system output is a weighted sum of linear submodels.
Lyapunov–Krasovskii functional: A generalised energy-like functional used to assess stability in systems with time delays.
Linear matrix inequality (LMI): A convex constraint on matrices used to derive tractable stability and performance conditions.
References
- Dynamic event-triggered delay compensation control for networked predictive control systems with random delay. Scientific Reports (2023).
- Event-Triggered H $\infty$ Fuzzy Filtering for Networked Control Systems With Quantization and Delays. IEEE Access (2018).
- Adaptive event-triggered dynamic output feedback H ∞ control for networked T-S fuzzy systems. Systems Science & Control Engineering (2020).
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