Robust Adaptive Fuzzy Control for Second-Order Euler-Lagrange Systems With Uncertainties and Disturbances via Nonlinear Negative-Imaginary Systems Theory
Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking perform...
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| Published in | IEEE transactions on cybernetics Vol. 54; no. 9; pp. 5102 - 5114 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.09.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2168-2267 2168-2275 2168-2275 |
| DOI | 10.1109/TCYB.2024.3365554 |
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| Abstract | Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking performance in normal conditions, designing and tuning fuzzy controllers can be a challenging task in highly uncertain environments. In this study, we investigate a novel approach that combines robust nonlinear negative-imaginary (NI) systems theory with a self-adaptive fuzzy control scheme and the Lyapunov synthesis to develop a robust adaptive negative-imaginary-fuzzy (RANIF) control scheme. We optimize the critical parameters of the proposed fuzzy system using a self-tuning technique with a proportional-derivative sliding manifold. Furthermore, unlike the existing adaptive fuzzy control methods, we propose a small number of membership functions and systematically derive the fuzzy rules by employing Lyapunov, nonlinear NI, and dissipativity theories, which simplify the tuning process, work out the matter of "explosion of complexity," and reduce computational complexity. We demonstrate the global stability of the closed-loop system using nonlinear NI theory. To evaluate the effectiveness of our proposed approach, we present simulation results for two examples involving uncertain MIMO second-order Euler-Lagrange systems. These systems, known for their capacity to represent a diverse range of practical physical systems, serve as suitable testbeds for our methodology. Our results show that RANIF outperforms other control methods, such as nonlinear strictly NI-Fuzzy, fuzzy-logic control, model predictive control, and conventional PID control, in terms of robustness to disturbances and inestimable faults, trajectory tracking performance, and computational complexity. |
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| AbstractList | Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking performance in normal conditions, designing and tuning fuzzy controllers can be a challenging task in highly uncertain environments. In this study, we investigate a novel approach that combines robust nonlinear negative-imaginary (NI) systems theory with a self-adaptive fuzzy control scheme and the Lyapunov synthesis to develop a robust adaptive negative-imaginary-fuzzy (RANIF) control scheme. We optimize the critical parameters of the proposed fuzzy system using a self-tuning technique with a proportional-derivative sliding manifold. Furthermore, unlike the existing adaptive fuzzy control methods, we propose a small number of membership functions and systematically derive the fuzzy rules by employing Lyapunov, nonlinear NI, and dissipativity theories, which simplify the tuning process, work out the matter of "explosion of complexity," and reduce computational complexity. We demonstrate the global stability of the closed-loop system using nonlinear NI theory. To evaluate the effectiveness of our proposed approach, we present simulation results for two examples involving uncertain MIMO second-order Euler-Lagrange systems. These systems, known for their capacity to represent a diverse range of practical physical systems, serve as suitable testbeds for our methodology. Our results show that RANIF outperforms other control methods, such as nonlinear strictly NI-Fuzzy, fuzzy-logic control, model predictive control, and conventional PID control, in terms of robustness to disturbances and inestimable faults, trajectory tracking performance, and computational complexity. Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking performance in normal conditions, designing and tuning fuzzy controllers can be a challenging task in highly uncertain environments. In this study, we investigate a novel approach that combines robust nonlinear negative-imaginary (NI) systems theory with a self-adaptive fuzzy control scheme and the Lyapunov synthesis to develop a robust adaptive negative-imaginary-fuzzy (RANIF) control scheme. We optimize the critical parameters of the proposed fuzzy system using a self-tuning technique with a proportional-derivative sliding manifold. Furthermore, unlike the existing adaptive fuzzy control methods, we propose a small number of membership functions and systematically derive the fuzzy rules by employing Lyapunov, nonlinear NI, and dissipativity theories, which simplify the tuning process, work out the matter of "explosion of complexity," and reduce computational complexity. We demonstrate the global stability of the closed-loop system using nonlinear NI theory. To evaluate the effectiveness of our proposed approach, we present simulation results for two examples involving uncertain MIMO second-order Euler-Lagrange systems. These systems, known for their capacity to represent a diverse range of practical physical systems, serve as suitable testbeds for our methodology. Our results show that RANIF outperforms other control methods, such as nonlinear strictly NI-Fuzzy, fuzzy-logic control, model predictive control, and conventional PID control, in terms of robustness to disturbances and inestimable faults, trajectory tracking performance, and computational complexity.Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking performance in normal conditions, designing and tuning fuzzy controllers can be a challenging task in highly uncertain environments. In this study, we investigate a novel approach that combines robust nonlinear negative-imaginary (NI) systems theory with a self-adaptive fuzzy control scheme and the Lyapunov synthesis to develop a robust adaptive negative-imaginary-fuzzy (RANIF) control scheme. We optimize the critical parameters of the proposed fuzzy system using a self-tuning technique with a proportional-derivative sliding manifold. Furthermore, unlike the existing adaptive fuzzy control methods, we propose a small number of membership functions and systematically derive the fuzzy rules by employing Lyapunov, nonlinear NI, and dissipativity theories, which simplify the tuning process, work out the matter of "explosion of complexity," and reduce computational complexity. We demonstrate the global stability of the closed-loop system using nonlinear NI theory. To evaluate the effectiveness of our proposed approach, we present simulation results for two examples involving uncertain MIMO second-order Euler-Lagrange systems. These systems, known for their capacity to represent a diverse range of practical physical systems, serve as suitable testbeds for our methodology. Our results show that RANIF outperforms other control methods, such as nonlinear strictly NI-Fuzzy, fuzzy-logic control, model predictive control, and conventional PID control, in terms of robustness to disturbances and inestimable faults, trajectory tracking performance, and computational complexity. |
| Author | Anavatti, Sreenatha G. Petersen, Ian R. Garratt, Matthew A. Tran, Vu Phi Mabrok, Mohamed A. |
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| Cites_doi | 10.1109/TCYB.2020.2978003 10.3390/s22010243 10.1016/j.neucom.2016.02.037 10.1016/j.amc.2019.124774 10.1109/TSMC.2020.3030078 10.1109/TCYB.2021.3050475 10.1016/j.isatra.2019.08.063 10.1109/INDIANCC.2016.7441125 10.1109/TAES.2020.3048778 10.1016/j.automatica.2019.108799 10.1109/TIE.2021.3118556 10.1007/s11633-014-0818-1 10.1109/TAC.2022.3200962 10.1007/978-981-19-3394-3_38 10.1109/ICIInfS.2013.6731982 10.1109/TFUZZ.2003.814845 10.1109/TAC.2022.3226703 10.1109/TAC.2008.919567 10.1109/TCYB.2019.2902868 10.1109/TIE.2020.2988219 10.1109/TSMC.2018.2877042 10.1109/TAC.2014.2325692 10.1109/ICEEOT.2016.7754868 10.1109/TCSII.2022.3149886 10.1109/TFUZZ.2020.2973955 10.1109/TAC.2010.2103415 10.1016/j.ifacol.2017.08.839 10.1109/VPPC55846.2022.10003416 10.1109/TAES.2022.3211252 10.1109/TCYB.2018.2794972 10.1109/TIE.2021.3059540 10.1109/TCYB.2022.3175366 10.1109/CDC.2018.8619130 10.23919/CCC52363.2021.9549626 10.1109/SCES.2013.6547577 10.1109/TIE.2020.3026302 10.1016/j.ifacsc.2021.100156 10.1016/j.automatica.2023.111127 10.2514/1.C035160 10.1109/TSMC.2018.2867061 10.1109/tcyb.2023.3263352 10.1109/TFUZZ.2012.2201728 10.1109/TMECH.2016.2614672 10.1007/s00521-020-04977-6 10.1109/tcyb.2023.3234320 10.1007/s40435-022-00960-2 10.1088/1757-899X/263/5/052007 10.1016/j.ifacsc.2020.100117 10.1109/RAAD.2014.7002242 10.1109/TCYB.2022.3204275 |
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| SubjectTerms | Adaptation models Adaptive fuzzy control Adaptive systems Fuzzy control Fuzzy logic MIMO multi-input–multi-output (MIMO) nonlinear systems nonlinear strictly negative imaginary (NI) theory quadcopter unmanned aerial vehicle robust and adaptive nonlinear control Robustness uncertainties Uncertainty |
| Title | Robust Adaptive Fuzzy Control for Second-Order Euler-Lagrange Systems With Uncertainties and Disturbances via Nonlinear Negative-Imaginary Systems Theory |
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