Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool
This paper proposes a Takagi–Sugeno (TS) fuzzy sliding mode observer (SMO) for simultaneous actuator and sensor fault reconstruction in a class of nonlinear systems subjected to unknown disturbances. First, the nonlinear system is represented by a TS fuzzy model with immeasurable premise variables....
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/18/6866 |
| _version_ | 1797482632925151232 |
|---|---|
| author | Samira Asadi Mehrdad Moallem G. Gary Wang |
| author_facet | Samira Asadi Mehrdad Moallem G. Gary Wang |
| author_sort | Samira Asadi |
| collection | DOAJ |
| description | This paper proposes a Takagi–Sugeno (TS) fuzzy sliding mode observer (SMO) for simultaneous actuator and sensor fault reconstruction in a class of nonlinear systems subjected to unknown disturbances. First, the nonlinear system is represented by a TS fuzzy model with immeasurable premise variables. By filtering the output of the TS fuzzy model, an augmented system whose actuator fault is a combination of the original actuator and sensor faults is constructed. An <inline-formula><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>H</mi><mo>∞</mo></msub></mrow></semantics></math></inline-formula> performance criteria is considered to minimize the effect of the disturbance on the state estimations. Then, by using two further transformation matrices, a non-quadratic Lyapunov function (NQLF), and fmincon in MATLAB as a nonlinear optimization tool, the gains of the SMO are designed through the stability analysis of the observer. The main advantages of the proposed approach in comparison to the existing methods are using nonlinear optimization tools instead of linear matrix inequalities (LMIs), utilizing NQLF instead of simple quadratic Lyapunov functions (QLF), choosing SMO as the observer, which is robust to the uncertainties, and assuming that the premise variables are immeasurable. Finally, a practical continuous stirred tank reactor (CSTR) is considered as a nonlinear dynamic, and the numerical simulation results illustrate the superiority of the proposed approach compared to the existing methods. |
| first_indexed | 2024-03-09T22:35:13Z |
| format | Article |
| id | doaj.art-d13af9d88dff4a509485c3e2097c5c2d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:35:13Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d13af9d88dff4a509485c3e2097c5c2d2023-11-23T18:50:34ZengMDPI AGSensors1424-82202022-09-012218686610.3390/s22186866Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization ToolSamira Asadi0Mehrdad Moallem1G. Gary Wang2School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, CanadaSchool of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, CanadaSchool of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, CanadaThis paper proposes a Takagi–Sugeno (TS) fuzzy sliding mode observer (SMO) for simultaneous actuator and sensor fault reconstruction in a class of nonlinear systems subjected to unknown disturbances. First, the nonlinear system is represented by a TS fuzzy model with immeasurable premise variables. By filtering the output of the TS fuzzy model, an augmented system whose actuator fault is a combination of the original actuator and sensor faults is constructed. An <inline-formula><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>H</mi><mo>∞</mo></msub></mrow></semantics></math></inline-formula> performance criteria is considered to minimize the effect of the disturbance on the state estimations. Then, by using two further transformation matrices, a non-quadratic Lyapunov function (NQLF), and fmincon in MATLAB as a nonlinear optimization tool, the gains of the SMO are designed through the stability analysis of the observer. The main advantages of the proposed approach in comparison to the existing methods are using nonlinear optimization tools instead of linear matrix inequalities (LMIs), utilizing NQLF instead of simple quadratic Lyapunov functions (QLF), choosing SMO as the observer, which is robust to the uncertainties, and assuming that the premise variables are immeasurable. Finally, a practical continuous stirred tank reactor (CSTR) is considered as a nonlinear dynamic, and the numerical simulation results illustrate the superiority of the proposed approach compared to the existing methods.https://www.mdpi.com/1424-8220/22/18/6866actuator and sensor faultsTS fuzzy systemsliding mode observer (SMO)<i>H</i><sub>∞</sub> performancenon-quadratic Lyapunov function (NQLF)fmincon |
| spellingShingle | Samira Asadi Mehrdad Moallem G. Gary Wang Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool Sensors actuator and sensor faults TS fuzzy system sliding mode observer (SMO) <i>H</i><sub>∞</sub> performance non-quadratic Lyapunov function (NQLF) fmincon |
| title | Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool |
| title_full | Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool |
| title_fullStr | Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool |
| title_full_unstemmed | Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool |
| title_short | Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool |
| title_sort | simultaneous sensor and actuator fault reconstruction by using a sliding mode observer fuzzy stability analysis and a nonlinear optimization tool |
| topic | actuator and sensor faults TS fuzzy system sliding mode observer (SMO) <i>H</i><sub>∞</sub> performance non-quadratic Lyapunov function (NQLF) fmincon |
| url | https://www.mdpi.com/1424-8220/22/18/6866 |
| work_keys_str_mv | AT samiraasadi simultaneoussensorandactuatorfaultreconstructionbyusingaslidingmodeobserverfuzzystabilityanalysisandanonlinearoptimizationtool AT mehrdadmoallem simultaneoussensorandactuatorfaultreconstructionbyusingaslidingmodeobserverfuzzystabilityanalysisandanonlinearoptimizationtool AT ggarywang simultaneoussensorandactuatorfaultreconstructionbyusingaslidingmodeobserverfuzzystabilityanalysisandanonlinearoptimizationtool |