Mohamed Hamdaoui

Evolutionary Algorithms to Analyse and Design a Controller for a Flapping Wings Aircraft

Evolutionary Algorithms are now mature optimization tools, especially in a multi-objective context. This ability is used here to help explore, analyse and, on this basis, propose a controller for a complex robotics system: a flapping wings aircraft. A multi-objective optimization is performed to find the best parameters of sinusoidal wings kinematics. Multi-objective algorithms generate a set of trade-off solutions instead of a single solution. The feedback is then potentially more informative in a multi-objective context relative to the one of a single objective setup: the set of trade-off solutions can be analyzed to characterize the studied system. Such an approach is applied to study a simulated flapping wing aircraft. The speed-energy relation is empirically evaluated and the analysis of the relations between the parameters of the kinematics and speed has led, in a further step, to the synthesis of an open-loop controller allowing to change speed during flight.

Using Multiobjective Evolutionary Algorithms and Data-Mining Methods to Optimize Ornithopters' Kinematics

The aim of this work is to present a method to find and analyze maximum propulsive efficiency kinematics for a birdlike flapping-wing unmanned aerial vehicle using multiobjective evolutionary optimization and data-mining tools. For the sake of clarity and simplicity, simple geometry (rectangular wings with the same profile along the span) and simple kinematics (symmetrical harmonic dihedral motion) are used. In addition, it is assumed that the birdlike aerial vehicle (for which the span and surface area are, respectively, 1 m and 0.15 m 2 ) is in horizontal motion at low cruise speed (6 m/s). The aerodynamic performances of the flapping-wing vehicle are evaluated with a semiempirical flight physics model and the problem is solved using an efficient multiobjective evolutionary algorithm called ∈-MOEA. Groups of attractive solutions are defined on the Pareto surface, and the most efficient solutions within these groups are characterized. Given the high dimensionality of the Pareto surface in the kinematic parameters space, data-mining techniques are used to conduct the study. First, it is shown that these groups can be qualified versus the whole Pareto surface by accurate mathematical relations on the kinematic parameters. Second, the inner structure of each group is studied and highly accurate mathematical relations are found on the optimized parameters describing the most efficient solutions.