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Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter
Navid Vafamand1, Mohammad Mehdi Arefi1, Alireza Khayatian1
1Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz 71348-51154, Iran.
ISA Transactions
|February 20, 2018
Summary
Two new Kalman-based algorithms identify Takagi-Sugeno (TS) fuzzy models online. Using the unscented Kalman filter (UKF), these methods effectively handle nonlinear systems and non-differentiable functions for broader TS model applicability.
Area of Science:
- Control Systems
- Fuzzy Logic Systems
- Machine Learning
Background:
- Online identification of Takagi-Sugeno (TS) fuzzy models is crucial for adaptive control systems.
- Existing methods, often based on the Extended Kalman Filter (EKF), struggle with non-differentiable membership functions and severe nonlinearities.
- There is a need for robust algorithms capable of handling a wider range of TS models and complex dynamics.
Purpose of the Study:
- To propose two novel Kalman-based learning algorithms for online TS fuzzy model identification.
- To leverage the Unscented Kalman Filter (UKF) for improved handling of nonlinear dynamics and non-differentiable functions.
- To enhance the applicability and performance of online TS fuzzy model identification compared to existing approaches.
Main Methods:
- Development of two new algorithms based on the Unscented Kalman Filter (UKF) and dual estimation.
- Utilizing the unscented transformation within the UKF, avoiding the need for derivatives of nonlinear functions.
- Application to online parameter calculation for a wider class of TS models, including those with non-differentiable membership functions.
Main Results:
- The proposed UKF-based algorithms demonstrate effectiveness in approximating nonlinear systems.
- The methods are applicable to a broader range of TS models due to the UKF's ability to handle non-differentiable functions.
- Numerical and practical examples confirm the advantages and performance improvements over existing methods, particularly in reducing root mean square (RMS) estimation error.
Conclusions:
- The novel Kalman-based algorithms offer a significant advancement in online TS fuzzy model identification.
- The UKF's inherent strengths enable robust identification of complex nonlinear systems and diverse TS model structures.
- These approaches provide a more effective and versatile solution for real-world adaptive control applications.
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