In this paper, the mechanism for the fault estimation (FE) problem for a steer-by-wire (SBW) vehicle with sensor and actuator faults is investigated. To deal with the design issues, we transformed the nonlinear model of SBW vehicle into a new coordinate system to jointly estimate the sensor and actuator faults. In the new coordinate system, the Lipschitz conditions and system uncertainties are also considered. The proposed schemes essentially transform the original system into two subsystems, where subsystem-1 includes the effects of actuator faults but is free from sensor faults and subsystem-2 only has sensor faults. Then two sliding mode observers (SMOs) are designed to estimate actuator and sensor faults, respectively. The sufficient conditions for the existence of the proposed observers with FI°o performance are derived and expressed as an LMI optimization problem such that the upper bounds of the state and fault estimation errors can be minimized. Finally, the numerical example with simulation results is provided to validate the practicability and efficacy of the developed estimation strategy

<abstract><p>In this paper, the mechanism for the fault estimation (FE) problem for a hydraulic servo actuator (HSA) with sensor faults is investigated. To deal with the design issues, we transformed the nonlinear model of HSA into a new coordinate system to estimate the sensor faults. In the new coordinate system, the Lipschitz conditions and system uncertainties are also considered. Then, we implement a sliding mode observer (SMO) approach to introduce the transformation scheme to make the system rational. The proposed fault estimation scheme essentially transforms the original system into two subsystems where the first one includes system uncertainties, but is free from sensor faults and the second one has sensor faults but without uncertainties. The effects of system uncertainties on the estimation errors of states and faults are minimized by integrating an $ H_\infty $ uncertainty attenuation level into the observer. The sufficient conditions for the state estimation error to be bounded and satisfy a prescribed $ H_\infty $ performance are derived and expressed as a linear matrix inequality (LMI) optimization problem. Finally, the numerical example with simulation results is provided to validate the practicability and efficacy of the developed control strategy.</p></abstract>