  Most of the advanced control systems use sensor-based feedback for robust
control. Tilt angle estimation is key feedback for many robotics and
mechatronics applications in order to stabilize a system. Tilt angle cannot be
directly measured when the system in consideration is not attached to a
stationary frame. it is usually estimated through indirect measurements in such
systems. The precision of this estimation depends on the measurements; hence it
can get expensive and complicated as the precision requirement increases. This
research is aimed at developing a novel and economic method to estimate tilt
angle with a relatively less sophisticated and complicated system, while
maintaining precision in estimating tilt angle. The method is developed to
explore a pendulum as an inertial measurement sensor and estimates tilt angle
based on dynamics of pendulum and parameter estimation models. Further,
algorithms are developed with varying order of complexity and accuracy to have
customization for different applications. Furthermore, this study will validate
the developed algorithms by experimental testing. This method focuses on
developing algorithms to reduce the input measurement error in the Kalman
filter.
