Prescribed finite-time adaptive fuzzy-based fault-tolerant control of robotic manipulators using dynamic scaling factor
Robotic manipulators play a critical role in modern automation; however, their efficacy can be significantly restricted by actuator faults, which present challenges to both stability and reliability. Current control strategies, including finite-time and fixed-time methods, encounter difficulties in...
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| Published in | The Journal of supercomputing Vol. 81; no. 6 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
23.04.2025
Springer Nature B.V |
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| Abstract | Robotic manipulators play a critical role in modern automation; however, their efficacy can be significantly restricted by actuator faults, which present challenges to both stability and reliability. Current control strategies, including finite-time and fixed-time methods, encounter difficulties in achieving precise system state convergence within a designated timeframe. Furthermore, their structure relies on fractional power terms, which complicate the implementation process. To address these challenges, this paper proposes a novel prescribed-time control strategy that guarantees system stability within a predetermined time, regardless of initial conditions. The key innovation lies in the integration of a dynamic time-varying scaling factor into a sliding mode controller, acting as an infinite gain to enforce strict prescribed-time convergence. Additionally, a fuzzy logic system is integrated to adaptively approximate actuator faults, thereby enhancing the robustness of the system while reducing computational complexity. The boundedness of the system states is proved through the Lyapunov stability theory. Moreover, the simulation result illustrates the performance of the proposed approach in the presence of actuator faults. |
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| AbstractList | Robotic manipulators play a critical role in modern automation; however, their efficacy can be significantly restricted by actuator faults, which present challenges to both stability and reliability. Current control strategies, including finite-time and fixed-time methods, encounter difficulties in achieving precise system state convergence within a designated timeframe. Furthermore, their structure relies on fractional power terms, which complicate the implementation process. To address these challenges, this paper proposes a novel prescribed-time control strategy that guarantees system stability within a predetermined time, regardless of initial conditions. The key innovation lies in the integration of a dynamic time-varying scaling factor into a sliding mode controller, acting as an infinite gain to enforce strict prescribed-time convergence. Additionally, a fuzzy logic system is integrated to adaptively approximate actuator faults, thereby enhancing the robustness of the system while reducing computational complexity. The boundedness of the system states is proved through the Lyapunov stability theory. Moreover, the simulation result illustrates the performance of the proposed approach in the presence of actuator faults. |
| ArticleNumber | 773 |
| Author | Serajgah, Saman Amini Ahmadi, Ali-Akbar |
| Author_xml | – sequence: 1 givenname: Saman Amini surname: Serajgah fullname: Serajgah, Saman Amini organization: Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University – sequence: 2 givenname: Ali-Akbar surname: Ahmadi fullname: Ahmadi, Ali-Akbar email: ahmadi@khu.ac.ir organization: Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University |
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| SubjectTerms | Actuators Compilers Computer Science Convergence Fault tolerance Faults Fuzzy logic Fuzzy systems Initial conditions Interpreters Manipulators Processor Architectures Programming Languages Robot arms Robot control Scaling factors Sliding mode control Supercomputers Systems stability |
| Title | Prescribed finite-time adaptive fuzzy-based fault-tolerant control of robotic manipulators using dynamic scaling factor |
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