• adaptive
• control
• following
• helicopter
• hover
• model
• PAV
• PID
• uncertainties
• unmatched

In the last decades commuting traffic in large metropolitan areas has posed a serious problem to the international community. Due to delays and pollution caused by traffic jams, every year bilions of euros are spent only in Europe.

The european project myCopter had the goal of revolutioning the current transportation system by investigating all the social, logistic and technical implications of personal aerial transportation.

During myCopter project the ideal dynamic of a Personal Aerial Vehicle (PAV) was developed through esperiment with naive pilots to be the easiest and most intuitive to fly.

The Max Plank Institute for Biological Cybernetic has researched the possibility of augmenting the dynamic of an identified light civil helicopter in hover to resemble the behavior of a PAV.

The solution proposed in this thesis is a two step procedure to overcome the limitations of previous attemps and provide a control architecture that works when uncertainties in the identified parameters of the helicopter model are taken into account.

First, no uncertainty in the identified parameters are considered. A PID-based model following controller is implemented with the goal of tracking the PAV model. The solution provides adequate model following in the nominal case but is not robust to parametric uncertainties.
Then, an adaptive controller is added to the system to reduce the effect of uncertainties and restore the nominal behaviour of the augmented helicopter.
Finally, the design is validated with Montecarlo simulation.