Real time adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural network proportional-integral-derivative controller for nonlinear systems
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an adaptive fuzzy neural PID controller that effectively manages uncertainties in nonlinear systems without needing a mathematical model. It enhances control performance and stability for engineering applications.
Area Of Science
- Control Engineering
- Artificial Intelligence
- Nonlinear System Dynamics
Background
- Nonlinear systems present significant challenges due to inherent uncertainties.
- Traditional controllers often struggle with stochastic uncertainties and require accurate system models.
- Adaptive control strategies are crucial for robust performance in dynamic environments.
Purpose Of The Study
- To develop an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller.
- To address stochastic uncertainties in nonlinear systems without relying on a mathematical model.
- To enhance controller performance and ensure system stability.
Main Methods
- Integration of probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system.
- Utilization of Lyapunov functions for adaptive parameter tuning and stability assurance.
- Tuning of controller probability parameters for enhanced control precision.
Main Results
- The proposed controller effectively handles external disturbances, random noise, and system uncertainties.
- Demonstrated superior performance compared to existing controllers in nonlinear dynamical plants.
- Validated applicability in engineering domains through simulations and experiments.
Conclusions
- The adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller offers a robust solution for uncertain nonlinear systems.
- The model-free nature and adaptive capabilities make it highly versatile for practical engineering applications.
- The controller significantly improves system robustness and performance in the presence of various uncertainties.
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