弹性高超声速飞行器智能控制系统设计  

Design of intelligent control system for flexible hypersonic vehicle

作　　者：王冠 茹海忠 张大力[1] 马广程[1] 夏红伟[1] WANG Guan;RU Haizhong;ZHANG Dali;MA Guangcheng;XIA Hongwei(School of Astronautics, Harbin Institute of Technology, Harbin 150001, China;Shanghai Institute of Satellite Engineering, Shanghai 201109, China)

机构地区：[1]哈尔滨工业大学航天学院,黑龙江哈尔滨150001 [2]上海卫星工程研究所,上海201109

出　　处：《系统工程与电子技术》2022年第7期2276-2285,共10页Systems Engineering and Electronics

基　　金：国家自然科学基金(61304108)资助课题。

摘　　要：针对气动舵受限下的弹性高超声速飞行器控制问题,提出一种基于神经自适应的智能控制方案。在速度子系统的设计过程中,为了降低对模型参数的依赖程度,应用强化学习算法在线调整比例积分微分(proportional integral derivative,PID)控制参数,给出智能PID控制策略。对于高度子系统,考虑气动舵的动态特性,利用神经自适应方法对模型未知函数及不确定项进行逼近。为了处理气动舵的约束问题,以非线性模型预测控制为优化分配模板生成大量样本数据集,经离线训练得到深度神经网络代替求解复杂优化问题和控制分配的过程。此外,通过引入自适应超螺旋微分器处理外部扰动,增强了系统的鲁棒性。利用Lyapunov方法证明了所设计控制器的稳定性,并通过仿真验证了所设计控制方案能够快速计算控制指令,实现高精度跟踪控制。Aiming at the control problem of the flexible hypersonic vehicle with constrained aerodynamic surfaces,an intelligent control scheme based on neural adaptation is proposed.In the design process of the velocity subsystem,the deep reinforcement learning algorithm is used to adjust the proportional integral derivative(PID)parameters online,and the intelligent PID control strategy is given to reduce the dependence on the model parameters.For the altitude subsystem,the neural adaptive method is used to approximate the unknown functions and uncertain terms of the model considering the dynamic characteristics of the aerodynamic surfaces.To deal with the constraint problem of aerodynamic surfaces,a large number of sample data sets are generated using nonlinear model predictive control as an optimal allocation template,and a deep neural network obtained through offline training is employed to replace the process of solving complex optimization problems and control allocation.In addition,by introducing an adaptive super-twist differentiator to handle external disturbances,the robustness of the system is enhanced.The stability of the controller is proved by using the Lyapunov method.The simulated results show that the proposed method can quickly calculate the control commands and realize high-precision tracking control.

关 键 词：高超声速飞行器 神经自适应 智能控制 深度强化学习 深度神经网络 

分 类 号：V448.2[航空宇航科学与技术—飞行器设计]