Aircraft Control Parameter Estimation Using Self-Adaptive Teaching-Learning-Based Optimization with an Acceptance...

Area of Science:

• Aerospace Engineering
• Computational Intelligence
• Optimization Algorithms

Background:

• Aircraft parameter estimation is crucial for flight dynamics modeling and control.
• Traditional methods like output error methods (OEM) can struggle with complex, inverse optimization problems.
• Metaheuristics (MH) offer robust search capabilities for challenging optimization tasks.

Purpose of the Study:

• To introduce a novel metaheuristic algorithm, self-adaptive teaching-learning-based optimization (saTLBO), for aircraft parameter estimation.
• To address the inverse problem of determining longitudinal aerodynamic parameters using real flight data.
• To evaluate the performance of saTLBO against other MHs and a conventional OEM.

Main Methods:

• Development of the self-adaptive teaching-learning-based optimization (saTLBO) algorithm incorporating an acceptance probability.
• Formulation of an inverse optimization problem for longitudinal aircraft parameter identification.
• Numerical validation using the HANSA-3 aircraft dataset.
• Comparative analysis of saTLBO, other MHs, and the output error method (OEM).

Main Results:

• The proposed saTLBO algorithm demonstrated superior performance in search convergence and consistency.
• saTLBO outperformed established metaheuristics and the conventional output error method.
• The algorithm effectively minimized errors between real flight data and dynamic equation calculations.

Conclusions:

• The self-adaptive teaching-learning-based optimization (saTLBO) is a highly effective method for aircraft parameter estimation.
• This work establishes a baseline for the application of metaheuristics in aircraft parameter identification.
• The findings suggest saTLBO's potential for advancing aerodynamic modeling and analysis.