Decentralized Fuzzy Control of Nonlinear Systems
Summary
Decentralized fuzzy control of nonlinear systems integrates the interpretability of fuzzy logic with the modularity of distributed control architectures. Nonlinear plants are modelled using fuzzy inference systems—often in the Takagi–Sugeno form—where local linear models are blended according to membership functions that capture operating conditions. In a decentralised framework, each subsystem is equipped with its own fuzzy controller, designed to stabilise local dynamics while accommodating interconnections, uncertainties and time delays. Stability and robust performance are typically ensured via Lyapunov-based analyses and linear matrix inequality techniques, which yield tractable conditions for controller synthesis. By decoupling a large-scale network into manageable fuzzy subsystems, this approach mitigates conservatism and reduces computational burden, facilitating real‐time implementation. Applications span power grids, chemical process networks, networked robotics and aerospace systems, where decentralised fuzzy controllers deliver resilience against disturbances, adaptability to varying operating regimes and simplified tuning through linguistic rules. Recent advances have targeted less conservative delay‐dependent stability conditions, free‐gain formulations and H∞ performance guarantees, underscoring the global significance of this paradigm for complex, interconnected nonlinear systems.
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Decentralized Fuzzy Control of Nonlinear Systems publication trend
The graph below shows the total number of articles in decentralized fuzzy control of nonlinear systems across all publications each year (not limited to Nature Index journals).
Technical terms
Decentralized control: A strategy where multiple local controllers operate independently to regulate subsystems while ensuring overall system stability.
Takagi–Sugeno fuzzy model: A representation of a nonlinear system by interpolating between local linear models weighted by fuzzy membership functions.
Lyapunov function: A scalar function used to assess the stability of dynamic systems by demonstrating that energy-like measures decay over time.
Linear matrix inequality (LMI): A convex constraint expressed as a matrix inequality, commonly employed in control design to derive feasible controller gains.
H∞ performance: A criterion ensuring robust disturbance attenuation by limiting the worst-case gain from disturbance inputs to controlled outputs.
Membership function: A curve that quantifies the degree of truth or belonging of a variable to a fuzzy set, central to fuzzy inference.
References
- Nonlinear Pseudo State-Feedback Controller Design for Affine Fuzzy Large-Scale Systems with H∞ Performance. International Journal of Fuzzy Systems (2022).
- Decentralized Control of Uncertain Fuzzy Large‐Scale System with Time Delay and Optimization. Journal of Applied Mathematics (2012).
- Delay‐Dependent Robust Stabilization for Nonlinear Large Systems via Decentralized Fuzzy Control. Mathematical Problems in Engineering (2011).
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