A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems
A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/1/62 |
| _version_ | 1797543727304015872 |
|---|---|
| author | Bin Zhang Yung C. Shin |
| author_facet | Bin Zhang Yung C. Shin |
| author_sort | Bin Zhang |
| collection | DOAJ |
| description | A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy models can closely approximate the original unknown nonlinear system with a reduced number of linearizations. Based on T-S fuzzy models, optimal controllers are designed and implemented for a nonlinear two-link flexible joint robot, which demonstrates the possibility of implementing the well-established model-based optimal control method onto unknown nonlinear dynamic systems. |
| first_indexed | 2024-03-10T13:49:47Z |
| format | Article |
| id | doaj.art-cc6bd3d15b1649fc97eaad008671d511 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T13:49:47Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cc6bd3d15b1649fc97eaad008671d5112023-11-21T02:17:08ZengMDPI AGApplied Sciences2076-34172020-12-011116210.3390/app11010062A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear SystemsBin Zhang0Yung C. Shin1School of Mechanical Engineering, Purdue University, West Lafayette, IN 47906, USASchool of Mechanical Engineering, Purdue University, West Lafayette, IN 47906, USAA novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy models can closely approximate the original unknown nonlinear system with a reduced number of linearizations. Based on T-S fuzzy models, optimal controllers are designed and implemented for a nonlinear two-link flexible joint robot, which demonstrates the possibility of implementing the well-established model-based optimal control method onto unknown nonlinear dynamic systems.https://www.mdpi.com/2076-3417/11/1/62Takagi-Sugeno modeldata-driven system identificationneuro-fuzzy modeloptimal controlflexible robot |
| spellingShingle | Bin Zhang Yung C. Shin A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems Applied Sciences Takagi-Sugeno model data-driven system identification neuro-fuzzy model optimal control flexible robot |
| title | A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems |
| title_full | A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems |
| title_fullStr | A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems |
| title_full_unstemmed | A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems |
| title_short | A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems |
| title_sort | data driven approach of takagi sugeno fuzzy control of unknown nonlinear systems |
| topic | Takagi-Sugeno model data-driven system identification neuro-fuzzy model optimal control flexible robot |
| url | https://www.mdpi.com/2076-3417/11/1/62 |
| work_keys_str_mv | AT binzhang adatadrivenapproachoftakagisugenofuzzycontrolofunknownnonlinearsystems AT yungcshin adatadrivenapproachoftakagisugenofuzzycontrolofunknownnonlinearsystems AT binzhang datadrivenapproachoftakagisugenofuzzycontrolofunknownnonlinearsystems AT yungcshin datadrivenapproachoftakagisugenofuzzycontrolofunknownnonlinearsystems |