Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints
In this paper, an adaptive neural fault-tolerant tracking control scheme is presented for the yaw control of an unmanned-aerial-vehicle helicopter. The scheme incorporates a non-affine nonlinear system that manages actuator faults, input saturation, full-state constraints, and external disturbances....
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MDPI AG
2020-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/4/1404 |
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| author | Qiang Zhang Xia Chen Dezhi Xu |
| author_facet | Qiang Zhang Xia Chen Dezhi Xu |
| author_sort | Qiang Zhang |
| collection | DOAJ |
| description | In this paper, an adaptive neural fault-tolerant tracking control scheme is presented for the yaw control of an unmanned-aerial-vehicle helicopter. The scheme incorporates a non-affine nonlinear system that manages actuator faults, input saturation, full-state constraints, and external disturbances. Firstly, by using a Taylor series expansion technique, the non-affine nonlinear system is transformed into an affine-form expression to facilitate the desired control design. In comparison with previous techniques, the actuator efficiency is explicit. Then, a neural network is considered to approximate unknown nonlinear functions, and a time-varying barrier Lyapunov function is employed to prevent transgression of the full-state variables using a backstepping technique. Robust adaptive control laws are designed to handle parameter uncertainties and unknown bounded disturbances to cut down the number of learning parameters and simplify the computational burden. Moreover, an auxiliary system is constructed to guarantee the pitch angle of the UAV helicopter yaw control system to satisfy the input constraint. Uniform boundedness of all signals in a closed-loop system is ensured via Lyapunov theory; the tracking error converges to a small neighborhood near zero. Finally, when the numerical simulations are applied to a yaw control of helicopter, the adaptive neural controller demonstrates the effectiveness of the proposed technique. |
| first_indexed | 2024-12-21T10:58:48Z |
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| id | doaj.art-fcc62c813e5944a3872291f0d131f79f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-21T10:58:48Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-fcc62c813e5944a3872291f0d131f79f2022-12-21T19:06:26ZengMDPI AGApplied Sciences2076-34172020-02-01104140410.3390/app10041404app10041404Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State ConstraintsQiang Zhang0Xia Chen1Dezhi Xu2School of Electrical Engineering, University of Jinan, Jinan 250000, ChinaSchool of Electrical Engineering, University of Jinan, Jinan 250000, ChinaInstitute of Electrical Engineering and Intelligent Equipment, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, ChinaIn this paper, an adaptive neural fault-tolerant tracking control scheme is presented for the yaw control of an unmanned-aerial-vehicle helicopter. The scheme incorporates a non-affine nonlinear system that manages actuator faults, input saturation, full-state constraints, and external disturbances. Firstly, by using a Taylor series expansion technique, the non-affine nonlinear system is transformed into an affine-form expression to facilitate the desired control design. In comparison with previous techniques, the actuator efficiency is explicit. Then, a neural network is considered to approximate unknown nonlinear functions, and a time-varying barrier Lyapunov function is employed to prevent transgression of the full-state variables using a backstepping technique. Robust adaptive control laws are designed to handle parameter uncertainties and unknown bounded disturbances to cut down the number of learning parameters and simplify the computational burden. Moreover, an auxiliary system is constructed to guarantee the pitch angle of the UAV helicopter yaw control system to satisfy the input constraint. Uniform boundedness of all signals in a closed-loop system is ensured via Lyapunov theory; the tracking error converges to a small neighborhood near zero. Finally, when the numerical simulations are applied to a yaw control of helicopter, the adaptive neural controller demonstrates the effectiveness of the proposed technique.https://www.mdpi.com/2076-3417/10/4/1404non-affine nonlinear systemadaptive neural controlactuator faultfull-state constraintsinput saturation |
| spellingShingle | Qiang Zhang Xia Chen Dezhi Xu Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints Applied Sciences non-affine nonlinear system adaptive neural control actuator fault full-state constraints input saturation |
| title | Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints |
| title_full | Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints |
| title_fullStr | Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints |
| title_full_unstemmed | Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints |
| title_short | Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints |
| title_sort | adaptive neural fault tolerant control for the yaw control of uav helicopters with input saturation and full state constraints |
| topic | non-affine nonlinear system adaptive neural control actuator fault full-state constraints input saturation |
| url | https://www.mdpi.com/2076-3417/10/4/1404 |
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