Adaptive Neural Network Robust Control of FOG with Output Constraints
In this work, an adaptive robust control method based on Radial Basis Function Neural Network (RBFNN) is proposed. Inspired by the local response characteristics of biological neurons, this method can reduce the influence of nonlinear errors and unknown perturbations in the extreme working condition...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/6/372 |
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| author | Shangbo Liu Baowang Lian Jiajun Ma Xiaokun Ding Haiyan Li |
| author_facet | Shangbo Liu Baowang Lian Jiajun Ma Xiaokun Ding Haiyan Li |
| author_sort | Shangbo Liu |
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| description | In this work, an adaptive robust control method based on Radial Basis Function Neural Network (RBFNN) is proposed. Inspired by the local response characteristics of biological neurons, this method can reduce the influence of nonlinear errors and unknown perturbations in the extreme working conditions of the aircraft, such as high dynamics and strong vibration, so as to achieve high tracking accuracy. In this method, the dynamic model of the nonlinear error of the fiber optic gyroscope is proposed, and then the unknown external interference observer is designed for the system to realize the estimation of the unknown disturbances. The controller design method combines the design of the adaptive law outside the finite approximation domain of the achievable condition design of the sliding mode surface, and adjusts the controller parameters online according to the conditions satisfied by the real-time error state, breaking through the limitation of the finite approximation domain of the traditional neural network. In the finite approximation domain, an online adaptive controller is constructed by using the universal approximation ability of RBFNN, so as to enhance the robustness to nonlinear errors and external disturbances. By designing the output constraint mechanism, the dynamic stability of the system is further guaranteed under the constraints, and finally its effectiveness is verified by simulation analysis, which provides a new solution for high-precision inertial navigation. |
| format | Article |
| id | doaj-art-e008f1ce00cc4d19ac3d7aa6616dfb47 |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-e008f1ce00cc4d19ac3d7aa6616dfb472025-08-20T02:24:33ZengMDPI AGBiomimetics2313-76732025-06-0110637210.3390/biomimetics10060372Adaptive Neural Network Robust Control of FOG with Output ConstraintsShangbo Liu0Baowang Lian1Jiajun Ma2Xiaokun Ding3Haiyan Li4School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi’an 710072, ChinaElectronic and College of Big Data and Communication Engineering Information Engineering, Guizhou University, Guiyang 550025, ChinaAVIC Flight Automatic Control Research Institute, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi’an 710072, ChinaSchool of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, ChinaIn this work, an adaptive robust control method based on Radial Basis Function Neural Network (RBFNN) is proposed. Inspired by the local response characteristics of biological neurons, this method can reduce the influence of nonlinear errors and unknown perturbations in the extreme working conditions of the aircraft, such as high dynamics and strong vibration, so as to achieve high tracking accuracy. In this method, the dynamic model of the nonlinear error of the fiber optic gyroscope is proposed, and then the unknown external interference observer is designed for the system to realize the estimation of the unknown disturbances. The controller design method combines the design of the adaptive law outside the finite approximation domain of the achievable condition design of the sliding mode surface, and adjusts the controller parameters online according to the conditions satisfied by the real-time error state, breaking through the limitation of the finite approximation domain of the traditional neural network. In the finite approximation domain, an online adaptive controller is constructed by using the universal approximation ability of RBFNN, so as to enhance the robustness to nonlinear errors and external disturbances. By designing the output constraint mechanism, the dynamic stability of the system is further guaranteed under the constraints, and finally its effectiveness is verified by simulation analysis, which provides a new solution for high-precision inertial navigation.https://www.mdpi.com/2313-7673/10/6/372fiber optic gyroscopeneural networkadaptive controloutput constraints |
| spellingShingle | Shangbo Liu Baowang Lian Jiajun Ma Xiaokun Ding Haiyan Li Adaptive Neural Network Robust Control of FOG with Output Constraints Biomimetics fiber optic gyroscope neural network adaptive control output constraints |
| title | Adaptive Neural Network Robust Control of FOG with Output Constraints |
| title_full | Adaptive Neural Network Robust Control of FOG with Output Constraints |
| title_fullStr | Adaptive Neural Network Robust Control of FOG with Output Constraints |
| title_full_unstemmed | Adaptive Neural Network Robust Control of FOG with Output Constraints |
| title_short | Adaptive Neural Network Robust Control of FOG with Output Constraints |
| title_sort | adaptive neural network robust control of fog with output constraints |
| topic | fiber optic gyroscope neural network adaptive control output constraints |
| url | https://www.mdpi.com/2313-7673/10/6/372 |
| work_keys_str_mv | AT shangboliu adaptiveneuralnetworkrobustcontroloffogwithoutputconstraints AT baowanglian adaptiveneuralnetworkrobustcontroloffogwithoutputconstraints AT jiajunma adaptiveneuralnetworkrobustcontroloffogwithoutputconstraints AT xiaokunding adaptiveneuralnetworkrobustcontroloffogwithoutputconstraints AT haiyanli adaptiveneuralnetworkrobustcontroloffogwithoutputconstraints |