Many-Objective Optimization of Nonlinear Dynamic Inversion Altitude Controller Using Metaheuristics
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Abstract
Effective control of nonlinear dynamics under varying conditions is challenging in aerospace systems, especially for altitude regulation. Traditional linear controllers like PID and LQR are simple but struggle with complex flight dynamics and changing conditions, such as airspeed and density variations. Nonlinear Dynamic Inversion (NDI) offers a solution by transforming nonlinear dynamics into a linearized form, allowing the use of linear control techniques. However, tuning NDI parameters is complex and time-consuming with traditional methods. Metaheuristic algorithms provide a robust alternative, efficiently exploring large solution spaces for near-optimal tuning. This study compares various metaheuristics for optimizing the parameters of NDI controllers in altitude control, assessing stability, responsiveness, and robustness. Results indicate that Success-History Based Adaptive Differential Evolution (SHADE) with Linear Population Size Reduction (L-SHADE) is the most effective algorithm for NDI controller optimization, delivering optimal control gains across varying conditions.
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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