Parameter Identification for a Quadrotor Helicopter Using Multivariable Extremum Seeking Algorithm

Parameter identification for a quadrotor helicopter is challenging from a theoretical point of view. In this paper, a closed-loop multivariable extremum seeking algorithm (MESA) is proposed for a nonlinear quadrotor helicopter parameter identification with two groups of input data. The proposed scheme is universally applicable to the closed-loop identification for cross-coupling multivariable processes where the identification problem is formulated as a minimization of a cost function. As the gradients of the performance parameters are obtained by step response experiments, the whole system searches along the negative gradient of the cost function until the reference trajectory or point is derived. Since the cost function is treated as a mapping from the model parameters, then the parameters can be identified online and in a real-time manner. The procedure of the identification algorithm is presented, and its effectiveness is illustrated by numerical simulations.