Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker
In the human-in-loop (HIL) guidance mode, a pilot quickly identifies and flexibly locks on to a target through a real-time image signal transmitted by the aircraft. Then, the line-of-sight (LOS) angle error in the viewing field is tracked and compensated for in order to improve the guidance and cont...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/9/806 |
| _version_ | 1797581720608833536 |
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| author | Yi Zhang Tao Li Yanning Li Gen Wang |
| author_facet | Yi Zhang Tao Li Yanning Li Gen Wang |
| author_sort | Yi Zhang |
| collection | DOAJ |
| description | In the human-in-loop (HIL) guidance mode, a pilot quickly identifies and flexibly locks on to a target through a real-time image signal transmitted by the aircraft. Then, the line-of-sight (LOS) angle error in the viewing field is tracked and compensated for in order to improve the guidance and control performance of the image-guided aircraft. Based on the physical structure and device parameters of the image seeker, an appropriate correction network is designed to improve the performance of the seeker stability loop. Aiming at a precise-extended crossover (PEC) pilot model, the structure of the dynamic model is optimized, and the maximum likelihood estimation (MLE) method of the output error structure is used to identify the dynamic parameters. This makes up for the deficiency of the existing modeling. In order to solve the nonlinear optimization problems encountered in the identification process, a hybrid strategy of a genetic algorithm (GA) and Gauss–Newton optimization algorithm is used to improve the probability of finding the global optimal solution. The simplex method is also used to improve the robustness of the algorithm. In addition, a hardware-in-the-loop simulation is designed and multi-round HIL experiment flow is performed. Moreover, based on the adaptability of the pilot to different image signal delays, the effects of different image signal delays on the stability and disturbance rejection rate (DRR) of the seeker control system are studied. The results demonstrate that the hybrid gradient optimization algorithm (HGOA) can find the global optimal value, and the identification model can accurately reflect the dynamic characteristics of the pilot. In the HIL guidance mode, the tracking compensation behavior of the pilot can reduce the influence of image signal delay on the disturbance of the aircraft body isolated by the seeker. The optimized PEC model and the identified dynamic parameters improve the efficiency of pilot training and screening. |
| first_indexed | 2024-03-10T23:09:36Z |
| format | Article |
| id | doaj.art-8485d2281b5541c98ef29408ec06fdf3 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-10T23:09:36Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-8485d2281b5541c98ef29408ec06fdf32023-11-19T09:05:07ZengMDPI AGAerospace2226-43102023-09-0110980610.3390/aerospace10090806Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image SeekerYi Zhang0Tao Li1Yanning Li2Gen Wang3School of Automation, Northwest Polytechnic University, Xi’an 710072, ChinaXi’an Modern Control Technology Research Institute, Xi’an 710065, ChinaSchool of Automation, Northwest Polytechnic University, Xi’an 710072, ChinaSchool of Automation, Northwest Polytechnic University, Xi’an 710072, ChinaIn the human-in-loop (HIL) guidance mode, a pilot quickly identifies and flexibly locks on to a target through a real-time image signal transmitted by the aircraft. Then, the line-of-sight (LOS) angle error in the viewing field is tracked and compensated for in order to improve the guidance and control performance of the image-guided aircraft. Based on the physical structure and device parameters of the image seeker, an appropriate correction network is designed to improve the performance of the seeker stability loop. Aiming at a precise-extended crossover (PEC) pilot model, the structure of the dynamic model is optimized, and the maximum likelihood estimation (MLE) method of the output error structure is used to identify the dynamic parameters. This makes up for the deficiency of the existing modeling. In order to solve the nonlinear optimization problems encountered in the identification process, a hybrid strategy of a genetic algorithm (GA) and Gauss–Newton optimization algorithm is used to improve the probability of finding the global optimal solution. The simplex method is also used to improve the robustness of the algorithm. In addition, a hardware-in-the-loop simulation is designed and multi-round HIL experiment flow is performed. Moreover, based on the adaptability of the pilot to different image signal delays, the effects of different image signal delays on the stability and disturbance rejection rate (DRR) of the seeker control system are studied. The results demonstrate that the hybrid gradient optimization algorithm (HGOA) can find the global optimal value, and the identification model can accurately reflect the dynamic characteristics of the pilot. In the HIL guidance mode, the tracking compensation behavior of the pilot can reduce the influence of image signal delay on the disturbance of the aircraft body isolated by the seeker. The optimized PEC model and the identified dynamic parameters improve the efficiency of pilot training and screening.https://www.mdpi.com/2226-4310/10/9/806image seekerpilot modelmaximum likelihood estimationGauss–Newton optimizationimage signal delaydisturbance rejection |
| spellingShingle | Yi Zhang Tao Li Yanning Li Gen Wang Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker Aerospace image seeker pilot model maximum likelihood estimation Gauss–Newton optimization image signal delay disturbance rejection |
| title | Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker |
| title_full | Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker |
| title_fullStr | Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker |
| title_full_unstemmed | Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker |
| title_short | Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker |
| title_sort | parameter identification of pilot model and stability analysis of human in loop image seeker |
| topic | image seeker pilot model maximum likelihood estimation Gauss–Newton optimization image signal delay disturbance rejection |
| url | https://www.mdpi.com/2226-4310/10/9/806 |
| work_keys_str_mv | AT yizhang parameteridentificationofpilotmodelandstabilityanalysisofhumaninloopimageseeker AT taoli parameteridentificationofpilotmodelandstabilityanalysisofhumaninloopimageseeker AT yanningli parameteridentificationofpilotmodelandstabilityanalysisofhumaninloopimageseeker AT genwang parameteridentificationofpilotmodelandstabilityanalysisofhumaninloopimageseeker |