Abstract:
A tensegrity is a special mechanical structure which has a special design consisting of
bars and tensile elements. Tensegrity systems became popular due to their ability to
withstand heavy loads in spite of having a very light weight. Efficiently controlling the
dynamics of tensegrity structures is very challenging due to their complex dynamics.
This thesis will provide the formulation and implementation of two closed-loop control
methods: Model Predictive Control (MPC) and Neural Networks (NNs). Specifically,
the method based on NNs consists of imitating the system trajectories generated by
MPC. The computation time of MPC is too high for real-time implementation, but
this is not the case for NN, which has a much lower computation time.