Aircraft parameter estimation using Hybrid Neuro Fuzzy and Artificial Bee Colony optimization (HNFABC) algorithm

In this paper, method of aerodynamic parameter estimation of aircraft, in the presence of process and measurement noise, have been introduced with the help of relevant experiments carried out on simulated as well as real aircraft data. A merger between two recently proposed artificial intelligence based techniques one of which simulates the intelligent foraging behavior of honey bee swarm viz. Artificial Bee Colony (ABC) optimization along with well-known Adaptive Neuro-Fuzzy System (ANFIS) that simulates the working of a unit of brain viz. neuron combined with the knowledge-based decision making capability of fuzzy system have been shown to be a promisingly new approach to the problem of aerodynamic modeling and parameter estimation for both aerodynamically stable aircraft in the presence of measurement error (sensor noise). Obtained results have been compared with benchmark estimation methods viz. Least Square, Filter Error Method. Corroborating results and comparisons have been furnished highlighting the efficacy of proposed algorithm. Furthermore, it has been shown that the proposed hybrid estimation algorithm can have realizable application in extracting stability and control variables utilizing kinematics of stable aircraft even in absence of adequate information content in the data history.