A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties
This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not requ...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Robotics |
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| Online Access: | https://www.mdpi.com/2218-6581/12/2/40 |
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| author | Ayad Al-Mahturi Fendy Santoso Matthew A. Garratt Sreenatha G. Anavatti |
| author_facet | Ayad Al-Mahturi Fendy Santoso Matthew A. Garratt Sreenatha G. Anavatti |
| author_sort | Ayad Al-Mahturi |
| collection | DOAJ |
| description | This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one initial fuzzy rule. The rules are then continuously updated and pruned in an online fashion to achieve the desired set point. For the phase of learning parameters, all adjustable parameters of the fuzzy system are tuned by using a sliding surface method, which is based on the gradient descent algorithm. This method is used to minimize the difference between the expected and actual signals. Our proposed control method is model-free and does not require prior knowledge of the plant’s dynamics or constraints. Instead, data-driven control utilizes artificial intelligence-based techniques, such as fuzzy logic systems, to learn the dynamics of the system and adapt to changes in the system, and account for complex interactions between different components. A robustness term is incorporated into the control effort to deal with external disturbances in the system. The proposed technique is applied to regulate the dynamics of a mobile robot in the presence of multiple external disturbances, demonstrating the robustness of the proposed control systems. A rigorous comparative study with respect to three different controllers is performed, where the outcomes illustrate the superiority of the proposed learning method as evidenced by lower RMSE values and fewer fuzzy parameters. Lastly, stability analysis of the proposed control method is conducted using the Lyapunov stability theory. |
| first_indexed | 2024-03-11T04:34:30Z |
| format | Article |
| id | doaj.art-ec78feb7e9204f12b9fca0f910ac3aed |
| institution | Directory Open Access Journal |
| issn | 2218-6581 |
| language | English |
| last_indexed | 2024-03-11T04:34:30Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj.art-ec78feb7e9204f12b9fca0f910ac3aed2023-11-17T21:14:12ZengMDPI AGRobotics2218-65812023-03-011224010.3390/robotics12020040A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large UncertaintiesAyad Al-Mahturi0Fendy Santoso1Matthew A. Garratt2Sreenatha G. Anavatti3School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, AustraliaSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, AustraliaSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, AustraliaSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, AustraliaThis paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one initial fuzzy rule. The rules are then continuously updated and pruned in an online fashion to achieve the desired set point. For the phase of learning parameters, all adjustable parameters of the fuzzy system are tuned by using a sliding surface method, which is based on the gradient descent algorithm. This method is used to minimize the difference between the expected and actual signals. Our proposed control method is model-free and does not require prior knowledge of the plant’s dynamics or constraints. Instead, data-driven control utilizes artificial intelligence-based techniques, such as fuzzy logic systems, to learn the dynamics of the system and adapt to changes in the system, and account for complex interactions between different components. A robustness term is incorporated into the control effort to deal with external disturbances in the system. The proposed technique is applied to regulate the dynamics of a mobile robot in the presence of multiple external disturbances, demonstrating the robustness of the proposed control systems. A rigorous comparative study with respect to three different controllers is performed, where the outcomes illustrate the superiority of the proposed learning method as evidenced by lower RMSE values and fewer fuzzy parameters. Lastly, stability analysis of the proposed control method is conducted using the Lyapunov stability theory.https://www.mdpi.com/2218-6581/12/2/40evolving type-2 fuzzy systemsrobotic controluncertaintiesLyapunov stability |
| spellingShingle | Ayad Al-Mahturi Fendy Santoso Matthew A. Garratt Sreenatha G. Anavatti A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties Robotics evolving type-2 fuzzy systems robotic control uncertainties Lyapunov stability |
| title | A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties |
| title_full | A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties |
| title_fullStr | A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties |
| title_full_unstemmed | A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties |
| title_short | A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties |
| title_sort | novel evolving type 2 fuzzy system for controlling a mobile robot under large uncertainties |
| topic | evolving type-2 fuzzy systems robotic control uncertainties Lyapunov stability |
| url | https://www.mdpi.com/2218-6581/12/2/40 |
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