Unknown-input estimator-based controller design of electric power-assisted steering system

In this study, the control design problem of double-pinion-type electric power assist steering (EPAS) is carefully examined. Based on a Lagrangian-based dynamical model, an optimal control approach with unknown input is formulated thereby eliminating the need for torque sensor. A new controller is developed using combination of non-linear assist curve, unknown-input estimator (UIE) and linear quadratic integral theory. It has been established that the estimation of the state, and unknown input using UIE yields a good estimate with performance compared with the well-known Kalman estimator. It is further shown that the resulting closed-loop response is able to track non-linear assist curve for different velocity. This reveals a salient feature, that is, controlling an EPAS system can be done using only single constant gain.