面向戰機大迎角機動(dòng)過(guò)程的智能學(xué)習控制
doi: 10.16383/j.aas.c230642
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1.
西北工業(yè)大學(xué)自動(dòng)化學(xué)院 西安 710072
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2.
山東大學(xué)控制科學(xué)與工程學(xué)院 濟南 250061
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3.
成都飛機設計研究所 成都 610041
Intelligent Learning Control for Fighter Maneuvers at High Angle of Attack
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School of Automation, Northwestern Polytechnical University, Xi＇an 710072
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School of Control Science and Engineering, Shandong University, Jinan 250061
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3.
Chengdu Aircraft Design & Research Institute, Chengdu 610041
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摘要: 針對戰機大迎角動(dòng)力學(xué)呈現的強非線(xiàn)性、氣動(dòng)不確定和通道耦合特性, 提出了一種基于智能學(xué)習的自適應機動(dòng)跟蹤控制方法. 通過(guò)將通道耦合視為集總擾動(dòng)的一部分, 把模型分解為迎角子系統、側滑角子系統和滾轉角速率子系統. 采用神經(jīng)網(wǎng)絡(luò )估計不確定, 設計跟蹤誤差反饋與集總干擾估計前饋相結合的控制器獲取期望操縱力矩, 并基于串接鏈分配方法求解氣動(dòng)舵偏角和推力矢量偏角. 對于神經(jīng)網(wǎng)絡(luò )權重更新, 構建預測誤差表征集總干擾的估計性能, 結合跟蹤誤差設計復合學(xué)習更新律. 基于李雅普諾夫方法證明了閉環(huán)系統的一致最終有界穩定性. 針對眼鏡蛇機動(dòng)和赫伯斯特機動(dòng)指令進(jìn)行了仿真驗證和抗干擾參數拉偏測試, 結果表明所提方法具有較高的機動(dòng)指令跟蹤精度和魯棒性能.
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關(guān)鍵詞:
- 戰機 /
- 大迎角機動(dòng) /
- 復合學(xué)習 /
- 自適應控制 /
- 控制分配
Abstract: Considering the strong nonlinearity, aerodynamic uncertainty and channel coupling characteristics of fighter dynamics at high angle of attack, an adaptive maneuver tracking control is proposed based on intelligent learning. By taking the channel coupling into a part of the total disturbance, the model is decomposed into the angle of attack subsystem, the sideslip angle subsystem and the roll angle rate subsystem. Neural networks are used to estimate aerodynamic uncertainties, and the controllers using tracking error feedback and total disturbance estimation feed-forward are designed to obtain the desired control torque. Then the aerodynamic surface deflection and thrust vector deflection are calculated based on daisy chain method. For the neural network weight update, the prediction error is constructed to reflect the estimation performance of the total disturbance, and the composite learning update law is designed combining with the tracking error. The uniformly ultimate boundedness of the closed-loop system is proved based on the Lyapunov method. Simulation and anti-disturbance parameter deviation tests are carried out for the Cobra and Herbst maneuvers, and the results show that the proposed method presents high tracking accuracy and more robust performance.-
Key words:
- Fighter /
- high angle of attack maneuver /
- composite learning /
- adaptive control /
- control allocation
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圖 2 眼鏡蛇機動(dòng)迎角跟蹤((a) 指令跟蹤; (b) 跟蹤誤差)
Fig. 2 Angle of attack tracking under Cobra maneuver ((a) Command tracking; (b) Tracking error)
圖 3 眼鏡蛇機動(dòng)$f_\alpha$的估計值((a) 基于NN-CL的$\hat f_\alpha$;(b) 基于NN的$\hat f_\alpha$; (c) 估計誤差)
Fig. 3 Estimation of $f_\alpha$ under Cobra maneuver ((a) $\hat f_\alpha$ under NN-CL; (b) $\hat f_\alpha$ under NN; (c) Estimation error)
圖 4 眼鏡蛇機動(dòng)的操縱偏轉量((a) 升降舵; (b) 俯仰推矢偏角)
Fig. 4 Control surface deflection under Cobra maneuver ((a) Elevator; (b) Pitch thrust vector deflection angle)
圖 5 赫伯斯特機動(dòng)迎角跟蹤((a) 指令跟蹤; (b) 跟蹤誤差)
Fig. 5 Angle of attack tracking under Herbst maneuver ((a) Command tracking; (b) Tracking error)
圖 6 赫伯斯特機動(dòng)滾轉角速率跟蹤((a) 指令跟蹤; (b) 跟蹤誤差)
Fig. 6 Roll angle rate tracking under Herbst maneuver ((a) Command tracking; (b) Tracking error)
圖 7 赫伯斯特機動(dòng)飛行狀態(tài)((a) 側滑角;(b) 速度; (c) 航跡方位角)
Fig. 7 Flight states under Herbst maneuver ((a) Sideslip angle; (b) Speed; (c) Flight path azimuth angle)
圖 9 赫伯斯特機動(dòng)氣動(dòng)操縱舵面偏轉((a) 升降舵; (b) 副翼; (c) 方向舵)
Fig. 9 Aerodynamic control surfaces deflection under Herbst maneuver ((a) Elevator; (b) Aileron; (c) Rudder)
圖 10 赫伯斯特機動(dòng)推力矢量偏轉((a)滾轉推矢偏角; (b)偏航推矢偏角; (c)俯仰推矢偏角)
Fig. 10 Thrust vector nozzles deflection under Herbst maneuver ((a) Roll thrust vector deflection angle; (b) Yaw thrust vector deflection angle; (c) Pitch thrust vector deflection angle)
圖 11 赫伯斯特機動(dòng)$f_\alpha$的估計值((a) 基于NN-CL的$\hat f_\alpha$; (b) 基于NN的$\hat f_\alpha$; (c) 估計誤差)
Fig. 11 Estimation of $f_\alpha$ under Herbst maneuver ((a) $\hat f_\alpha$ under NN-CL; (b) $\hat f_\alpha$ under NN; (c) Estimation error)
圖 14 赫伯斯特機動(dòng)$f_p$的估計值((a) 基于NN-CL的$\hat f_p$; (b) 基于NN的$\hat f_p$; (c) 估計誤差)
Fig. 14 Estimation of $f_p$ under Herbst maneuver ((a) $\hat f_p$ under NN-CL; (b) $\hat f_p$ under NN; (c) Estimation error)
圖 15 神經(jīng)網(wǎng)絡(luò )權重估計值 ((a) $\|\hat{{\boldsymbol{\omega}}}_{f_\alpha}\|$; (b) $\|\hat{{\boldsymbol{\omega}}}_{f_q}\|$; (c) $\|\hat{{\boldsymbol{\omega}}}_{f_r}\|$; (d) $\|\hat{{\boldsymbol{\omega}}}_{f_p}\|$)
Fig. 15 Estimation of NN weights ((a) $\|\hat{{\boldsymbol{\omega}}}_{f_\alpha}\|$; (b) $\|\hat{{\boldsymbol{\omega}}}_{f_q}\|$; (c) $\|\hat{{\boldsymbol{\omega}}}_{f_r}\|$; (d) $\|\hat{{\boldsymbol{\omega}}}_{f_p}\|$)
圖 16 魯棒測試((a) 迎角; (b) 側滑角; (c) 滾轉角速率)
Fig. 16 Robustness verification ((a) Angle of attack; (b) Sideslip angle; (c) Roll angle rate)
圖 12 赫伯斯特機動(dòng)$f_q$的估計值((a) 基于NN-CL的$\hat f_q$; (b) 基于NN的$\hat f_q$; (c) 估計誤差)
Fig. 12 Estimation of $f_q$ under Herbst maneuver ((a) $\hat f_q$ under NN-CL; (b) $\hat f_q$ under NN; (c) Estimation error)
圖 13 赫伯斯特機動(dòng)$f_r$的估計值((a) 基于NN-CL的$\hat f_r$; (b) 基于NN的$\hat f_r$; (c) 估計誤差)
Fig. 13 Estimation of $f_r$ under Herbst maneuver ((a) $\hat f_r$ under NN-CL; (b) $\hat f_r$ under NN; (c) Estimation error)
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