This research involved the modeling, control and state estimation of a Roll Simulator. The focus of this study was on the Roll Simulator's application in emulating rollovers for vehicles such as ROVs. The Roll Simulator was designed to study occupant kinematics during a vehicle rollover in a laboratory setting. Little research has been performed where the focus has been on the vehicle rolling over to 90 degrees and the interaction of the occupant with the road plane at this instance has been closely examined. The Roll Simulator allows for these types of analyses to occur.
In this dissertation, a two (2) degree-of-freedom model, describing the dynamics of the Roll Simulator, is developed. Equations of motion, derived using Lagrange's energy methods, describe the dynamics of the sled-platform assembly. Additional sub-system modeling is also performed to capture the dynamics of a hydraulic system, electro-magnetic particle brake and electric roll motor. The validity of the full simulation is corroborated by comparisons with experimental data from the Roll Simulator.
Control strategies for the Roll Simulator are also discussed. The strategies are derived utilizing simple physics of the system. This allows for desired trajectories to be met using feed-forward terms. Application of feedback is limited due to the configurations of the actuators and the short duration maneuever.
A variety of linear observers are introduced to estimate states within the Roll Simulator. A Kalman Filter is developed to estimate sled speed. To tune the filter, the Kalman Filter is applied to a higher fidelity model which has four (4) degrees-of-freedom. To capture the non-linear behavior of the sled-platform assembly, an Extended Kalman Filter (EKF) is used. When applied to experimental data, the observed sled speed exhibits gross over-estimation of the true speed. This is due to a disturbance in the system. A disturbance observer is used to estimate rolling resistance between the sled and floor and account for any uncertainties in system parameters. When using the disturbance observer, the linear Kalman Filter is able to more accurately estimate sled speed. For low-load low-speed applications, the output of a Kalman Filter using an accelerometer and measured drum speed, closely agrees with sled speed, when appropriate gain scheduling is introduced.
Lastly, a feedback linearization technique is investigated. This studies the versatility of the Roll Simulator when the limitations of its actuators are increased.