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This paper presents a novel design of a control law optimizing the performance of an on-demand all wheel drive (ODAWD) vehicle with hybrid powertrain for traction enhancement via slip regulation in a driving event. Based on a reasonably simplified vehicle model (bicycle model) and optimization of a performance index based on wheel slip, a closed loop actuator control law is derived. The proposed optimal controller tries to minimize the wheel slip error by activating and dynamically controlling the electric motor drive torque to the non-driven wheel pair (e.g. rear wheels), in order to enhance vehicle longitudinal traction. Simulation of the proposed controller was performed on a validated 14 degree-of-freedom detailed vehicle model in SIMULINK.

This paper presents a nonlinear observer and long range prediction based analytical redundancy for a Steer-By-Wire (SBW) system. A Sliding Mode Observer was designed to estimate the vehicle steering angle by using the combined linear vehicle model, SBW system, and the yaw rate. The estimated steering angle along with the current input was used to predict the steering angle at various prediction horizons via a long range prediction method. This analytical redundancy methodology was utilized to reduce the total number of redundant road-wheel angle (RWA) sensors, while maintaining a high level of reliability. The Fault Detection, Isolation and Accommodation (FDIA) algorithm was developed using a majority voting scheme, which was then used to detect faulty sensor(s) in order to maintain safe drivability. The proposed observer-prediction based FDIA algorithms as well as the linearized vehicle model were modeled in MATLAB-SIMULINK.

Generalized predictive control (GPC) is a discrete time control strategy proposed by Clark et al [1]. The controller tries to predict the future output of a system or plant and then takes control action at present time based on future output error. Such a predictive control algorithm is presented in this paper for yaw stability management of an automobile. Most of the existing literature on the yaw stability management systems lacks the insight into the yaw rate error growth when the automobile is in a understeer or oversteer condition on a low friction coefficient surface in a handling maneuver. Simulation results show that the predictive feature of the proposed controller provides an effective way to control the yaw stability of a vehicle.

Generalized predictive control (GPC) is a discrete time control strategy proposed by Clark et al [1]. The controller tries to predict the future output of a system or plant and then takes control action at present time based on future output error. Such a predictive control algorithm is presented in this paper for acceleration slip regulation in an automobile. Most of the existing literature on the brake based traction control systems (BTCS) lacks the insight into the wheel slip growth when the automobile is on a low friction coefficient surface and the driver has the throttle wide open. Simulation results show that the predictive feature of the proposed controller provides an effective way to control the wheel slip in a vehicle acceleration event.