Swarm Intelligence & System Identification: A Hybrid Discrete Jumping Frogs Algorithm For Optimal Input Design

Abstract

The representativeness of the flight dynamics model has become a major concern in aviation industry since numerical simulation is now inevitable in aeronautics and widely used for every new aircraft program. Consequently, the requirements on its accuracy and reliability increase constantly. To build a flight dynamics model of acceptable quality, flight tests are designed and flown to adjust some relevant coefficients of the preflight aerodynamic model to the real A/C. But, the constraint of building more accurate models from shorter flight tests campaigns leads to revisit this identification process by designing optimal inputs since the aerodynamic parameter estimation is dependent on the quality of the inputs sent to the aircraft. Optimal Input Design (OID) for dynamical systems corresponds to complex and difficult optimization problems characterized by an infinite-dimensional search space combined with the presence of multiple local extrema. Even if signals can be parametrized using function basis, so that problem becomes of finite dimension, OID dedicated to flight dynamics identification still remains hard to solve. Indeed, flight tests protocol optimization must be tackled in its entirety (single/multiple input/s for one or several flight tests) which means that problem dimension can be high. Moreover, decision variables correspond pratically to real-valued discrete parameters due to the tabulated aerodynamic data structure of the model. This article describes some recent advances in the field of OID for flight dynamics identification through an original and innovative bio-inspired evolutionary algorithm based on the theoretical principles of the Jumping Frogs Optimization (JFO) technique.