Sampled-Data Control of Fuzzy Systems
Summary
Sampled-data control of fuzzy systems addresses the challenge of implementing digital controllers for continuous‐time processes whose dynamics are described by fuzzy logic. By discretising system measurements at specified sampling instants, one can apply fuzzy inference rules—often cast as Takagi–Sugeno models—to capture nonlinear or uncertain plant behaviour. Core issues include the treatment of input and communication delays, nonuniform or aperiodic sampling patterns, and the derivation of stability guarantees under these sampling constraints. Modern solutions exploit Lyapunov–Krasovskii functionals tailored to sampled intervals, combined with linear matrix inequality techniques to obtain tractable controller design conditions. Advances in this field have enabled more efficient bandwidth usage, reduced conservatism in stability criteria and robust handling of switching, saturation or stochastic effects. Practical applications span chaotic circuit regulation, autonomous vehicle positioning, networked neural network synchronization and fault-tolerant energy systems, illustrating the global significance of ensuring both performance and reliability in digitally controlled fuzzy processes.
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Sampled-Data Control of Fuzzy Systems publication trend
The graph below shows the total number of articles in sampled-data control of fuzzy systems across all publications each year (not limited to Nature Index journals).
Technical terms
Sampled-data control: A control strategy where continuous-time signals are measured and updated at discrete sampling instants for digital implementation.
Fuzzy system: A modelling framework that represents complex or uncertain dynamics through a set of fuzzy inference rules and membership functions.
Takagi–Sugeno model: A fuzzy representation in which local linear models are blended according to fuzzy membership values to approximate nonlinear dynamics.
Lyapunov–Krasovskii functional: A generalisation of Lyapunov functions that accounts for system delays by integrating state information over past intervals.
Linear matrix inequality (LMI): A convex constraint on matrix variables used to formulate tractable conditions for stability and controller synthesis.
References
- Mixed-Delay-Dependent Augmented Functional for Synchronization of Uncertain Neutral-Type Neural Networks with Sampled-Data Control. Mathematics (2023).
- Sampled-Data Control for a Class of Singular Takagi-Sugeno Fuzzy Systems with Application in Truck-Trailer System. Symmetry (2022).
- Aperiodic Sampled‐Data Control for Chaotic System Based on Takagi–Sugeno Fuzzy Model. Complexity (2021).
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