Abstract:
As the new paradigm shift happened in the industrialization, introducing "Industry
4.0", many new applications in robotics are emerged. One of them safe human robot
interaction field. Such an approach can be executed by utilizing variable impedance
actuators, which imitates human and animal bodies, guaranteeing safety during the
interaction. But on the other hand, these actuators have some limitations due to the
damping elements in their construction. These elements behave as filters and may
decrease the accuracy and positioning of the end effector, and may cause vibrations.
Therefore, to avoid such unfavorable conditions, some stabilization elements should
be integrated with the actuators. The thesis work will introduce a two-axis reaction
wheel based inverted pendulum, which can deal with the nuisance by liquidating
vibrations and adjust accuracy. In this work the utilization of Neural Networks is
implemented to stabilize the pendulum. Moreover, it will consider the robust control
Neural Network for the case of uncertainties and external disturbances. By generating
the simulations and conducting real world experiments, the work will demonstrate the
advantages of Neural Network employment.