The aerodynamic parameters of each flying vehicle dynamically change along its flight profile, because of aerodynamic parameter relationship with flight conditions, and several flight conditions take place during each flight profile. Therefore, in this research, the concept of dynamic aerodynamic parameter estimation (DAPE) is introduced. A two-step strategy is used: In the first step, the aerodynamic forces and moments are estimated; then, after passing through a designed smoothing filter, in the second step, the DAPE is converted to a dynamic optimization problem and solved by a heuristic optimization algorithm that hybridizes the features of particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. Two new algorithms are developed: DAPE and SDAPE. In DAPE algorithm, all aerodynamic parameters are estimated at once by solving a single optimization problem. In SDAPE algorithm, four separate optimization problems are solved. A rolling airframe is the plant studied in this research. Simulation results indicate that SDAPE is better than DAPE in terms of accuracy. Comparing the performance of the newly proposed algorithms with that of three state-of-the-art static optimization algorithms and extended Kalman filter reveals their less run time and acceptable accuracy.

由于空气动力学参数与飞行条件的关系，每个飞行器的空气动力学参数沿其飞行轮廓动态变化，并且在每个飞行轮廓期间发生若干飞行条件。因此，在本研究中，引入了动态空气动力学参数估计（DAPE）的概念。采用两步策略：第一步，估算空气动力和力矩；第二步，估算气动力矩。然后，经过设计的平滑滤波器后，在第二步中，将DAPE转换为动态优化问题，并通过启发式优化算法解决该问题，该算法将粒子群优化的特征与新的进化过程相结合，以跟踪动态变化。开发了两种新算法：DAPE和SDAPE。在DAPE算法中，通过解决单个优化问题，可以立即估算所有空气动力学参数。在SDAPE算法中，解决了四个单独的优化问题。滚动机身是本研究中研究的设备。仿真结果表明，SDAPE在准确性方面优于DAPE。将新提出的算法的性能与三种最新的静态优化算法和扩展的卡尔曼滤波器的性能进行比较，可以发现它们的运行时间较短且精度可接受。