Dynamic aerodynamic parameter estimation using a dynamic particle swarm optimization algorithm for rolling airframes

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.