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Composite compensation control method for airborne opto-electronic platform mounted on multi-rotor UAV
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摘要:
为了提高多旋翼无人飞行器机载光电平台的扰动补偿能力，实现机载光电平台的稳定跟踪控制，提出一种基于改进扰动观测器和径向基函数(RBF)神经网络逼近的复合补偿控制方法。首先，对现有扰动观测器结构进行改进，构建基于速度信号的改进型扰动观测器，并分析了干扰补偿能力和稳健性；然后，利用RBF神经网络的函数逼近性质解决非线性未知扰动的补偿问题；最后，基于Lyapunov稳定性原理设计出复合补偿控制结构。实验结果表明，机载光电平台的扰动得到有效补偿。该补偿控制方法具有较高的稳定精度和跟踪控制性能，满足多旋翼无人飞行器机载光电平台的稳定控制要求。
Abstract:In order to compensate disturbance and accomplish the stabilized tracking control for airborne platform mounted on multi-rotor unmanned aerial vehicle (MUAV), a self-adjusting tracking control method based on an improved disturbance observer (DOB) and radial basis function (RBF) neural network approximation is proposed. First, a compensated control is introduced into feedback loop in the structure of original disturbance observer, an improved disturbance observer is established based on velocity signals, and the ability of disturbance compensation and robustness are analyzed. Second, aiming at the compensation problem of nonlinear unknown disturbance, a method based on the RBF neural network (RBFNN) approximation properties is utilized. Finally, a composite compensation control structure is designed based on Lyapunov stability theory. The experimental results show that after applying the proposed method, the disturbance of airborne opto-electronic platform is compensated effectively. The proposed method has high precision and stable tracking control performance, and it can fully meet the requirement of airborne opto-electronic platform stability control.
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Recently, multi-rotor unmanned aerial vehicle (MUAV) has been widely used in military and civilian fields. Airborne opto-electronic platform (AOEP) is the key to the application of MUAV, such as target reconnaissance, identification and tracking. The imaging quality, recognition accuracy and tracking accuracy of airborne opto-electronic devices, to a large extent, depend on the stable control performance of the AOEP. Unfortunately, the AOEP is vulnerably affected by air disturbance, vibration and other unknown disturbance factors during the flight operation process, which seriously influences the stability and accuracy, and even leads to reconnaissance and tracking tasks failure. Therefore, how to improve the anti-disturbance ability of the AOEP has become the key problem, which restricts the development and applications of the MUAV severely. It has been one of the hot research directions in recent years.
For the problem of disturbance compensation of airborne stabilized platform, the control method based on disturbance observer (DOB) has been widely used. To a certain extent, the stability control performance of the airborne stabilized platform is improved. However, the compensation effect of DOB on high frequency noise is not ideal. Simultaneously, disturbance usually has strong nonlinearity. It is difficult to obtain ideal tracking control performance by using DOB method only. Fortunately, neural networks and fuzzy systems are real-time, robust, and can approximate any function. They have been widely used in the tracking control system of stabilized platform.
Aiming at the disturbance compensation and stability control of AOEP, a composite compensation control method for AOEP mounted on MUAV is proposed. First, to eliminate the effects of high frequency noise, by introducing a compensation control into the original DOB structure, an improved disturbance observer (IDOB) based on the velocity signal is proposed. Second, considering the nonlinearity of the disturbance, the radial basis function neural network (RBFNN) is used to estimate and compensate the nonlinear disturbance. In order to realize the stable control of AOEP, a composite compensation control system based on IDOB and RBFNN is designed by using Lyapunov stability principle. It is proved that the proposed control system is asymptotically stable and the tracking error is bounded. It has good stability and robustness. Finally, the effectiveness of the method is verified by experiments. The experimental results show that the IDOB structure has better disturbance rejection ability and has higher stability accuracy. The proposed method can restrain the effect of disturbance to the AOEP system. The AOEP has higher stability and tracking precision. The composite compensation control system completely satisfies the requirements of tracking control of AOEP.
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图 4 实验用搭载于多旋翼无人飞行器的机载光电平台.
Figure 4. Airborne opto-electronic platform mounted on mUAV for experiment.
图 5 视轴稳定控制对比结果. (a)扰动观测器. (b)改进型扰动观测器.
Figure 5. Comparison results of line of sight error. (a) DOB. (b) IDOB.
图 6 未引入RBFNN的位置跟踪曲线. (a)位置跟踪曲线. (b)跟踪误差.
Figure 6. Position tracking curve without RBFNN. (a) Position tracking curve. (a) Position tracking error.
图 7 引入RBFNN跟踪曲线. (a)位置跟踪曲线. (b)跟踪误差.
Figure 7. Position tracking curve with RBFNN. (a) Position tracking curve. (b) Position tracking error.
图 8 机载环境下的跟踪曲线和跟踪误差. (a)位置跟踪曲线. (b)跟踪误差.
Figure 8. Tracking curve and tracking error of proposed method in airborne environment. (a) Position tracking curve. (b) Position tracking error.
表 1 机载光电平台模型参数
Table 1. Parameters of airborne opto-electronic platform.
Parameter Value Pitch channel Me/(kgm2) 0.0314 Pitch channel Fv /(N·m·s/rad) 0.0023 Yaw channel Me/(kg·m2) 0.4286 Yaw channel Fv/(N·m·s/rad) 0.0055 Parameters ${\hat a_1} $ 0.5 Parameters ${\hat a_2} $ 2 
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