Abstract
Herein, the design optimization of multi-objective controllers for the lateral–directional motion using proportional–integral–derivative controllers for a twin-engine, propeller-driven airplane is presented. The design optimization has been accomplished using the genetic algorithm and the main goal was to enhance the handling quality of the aircraft. The proportional–integral–derivative controllers have been designed such that not only the stability of the lateral–directional motion was satisfied but also the optimum result in longitudinal trim condition was achieved through genetic algorithm. Using genetic algorithm optimization, the handling quality was improved and placed in level 1 from level 2 for the proposed aircraft. A comprehensive sensitivity analysis to different velocities, altitudes and centre of mass positions is presented. Also, the performance of the genetic algorithm has been compared to the case where the particle swarm optimization tool is implemented. In this work, the aerodynamic coefficients as well as the stability and control derivatives were predicted using analytical and semi-empirical methods validated for this type of aircraft.
Highlights
Propeller effects and power contribution to the trim condition and stability of a propeller-driven airplane are significant
Initial analytical behaviour of this phenomenon was investigated by Harris[4] in 1918 where he showed the resulting pitching moment due to the side wind effect
Most of the methods were based on the linear classic momentum theory.[7]
Summary
Propeller effects and power contribution to the trim condition and stability of a propeller-driven airplane are significant. The main advantage of the coded parameters is that they are able to change the continuous variables to the discrete ones.[39,40,41] GAs are stochastic search algorithms that act on a population of possible solutions and leading the solutions towards the most optimum one.[42] It should be noted that the GAs are not usually able to solve very complex problems where one is facing complex fitness functions or high scale of iterations In such cases, the GAs are facing a significant reduction in performance due to the complexity of time and lower accuracy.[43,44]. At the end of phase 4, all stability and performance terms compared to the first results placed in a better condition resulting in a higher handling quality (e.g. level 1) for all lateral–directional modes. It should be noted that in all cases, the population size of the GA was considered equal to 100 and the average relative change in the best fitness function value over max stall generations was equal to 1e–6
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