Convergent Estimation Mechanism Design for Nonlinear Fuzzy Systems With Faults
IEEE Transactions on Cybernetics
|December 22, 2018
Summary
A new convergent estimation mechanism (CEM) estimates states and faults in nonlinear fuzzy systems. This method proves error convergence, improving upon existing bounded-error techniques for time-varying faults and disturbances.
Area of Science:
- Control Systems Engineering
- Fuzzy Logic Systems
- Nonlinear System Analysis
Background:
- Nonlinear Takagi-Sugeno fuzzy systems often face challenges with time-varying process faults and input disturbances.
- Existing estimation methods struggle to prove convergence for time-varying faults, often only achieving uniformly ultimately bounded errors.
Purpose of the Study:
- To develop a novel convergent estimation mechanism (CEM) for nonlinear Takagi-Sugeno fuzzy systems with both time-varying faults and input disturbances.
- To prove the convergence of estimation errors for both system states and faults to zero.
Main Methods:
- Construction of a convergent estimation mechanism (CEM) using a set of fuzzy iterative estimation observers.
- Application of a suitable isolation technique to effectively separate system disturbances within the fuzzy iterative error dynamics.
Main Results:
- The proposed CEM demonstrates the convergence of the mean sequence of estimation errors (for states and faults) to zero.
- The method effectively isolates input disturbances in the error dynamics, a key improvement over prior work.
- Numerical simulations validate the effectiveness and advantages of the developed CEM.
Conclusions:
- The developed CEM provides a robust approach for state and fault estimation in complex nonlinear fuzzy systems.
- This work advances fault estimation by proving error convergence, surpassing previous uniformly ultimately bounded results for time-varying faults.
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