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As dynamic system models become more complex, their computation times increase. Traditionally, the model, as a whole, would be evaluated at a single time step that would give the desired stability and accuracy for all states. It is hypothesized that the models be partitioned allowing different portions of the model be solved at different time steps, allowing each state to be evaluated at a time step that will give the desired stability and accuracy. Furthermore, with the model operating at several time steps, each time step could be solved on a separate processor of a multiple processor machine. Using a Simulink ® (Simulink) model of a multiple degree of freedom, spring, mass, damper system, multiple time steps were created through the use of rate transition blocks and discrete integrators. A multithreaded program was then created by modifying the rsim_main.C script.

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 deceleration slip regulation in an automobile. Most of the existing literature on the anti-lock brake control systems lacks the effectiveness of the wheel lockup prevention when the automobile is in a skid condition (in a low friction coefficient surface with panic braking situation). Simulation results show that the predictive feature of the proposed controller provides an effective way to prevent wheel lock-up in a braking event.

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.

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.

Ground speed control of a large wheel loader (LWL) is a very important part of a truck loading cycle. Since the engine is at full throttle for most part of a loading cycle, the ground speed is controlled by an impeller clutch/brake pedal. Essentially, this mechanical pedal, when engaged, disconnects the engine from the driveline and applies the service brakes. However, in order to properly control the ground speed of a large wheel loader, an appropriate powertrain control strategy is needed for the directional shifts (1R-1F, 2R-2F, etc.). These shifts are usually associated with unacceptable levels of jerk and acceleration. A reference trajectory for the vehicle speed based on the desired jerk and acceleration traces can be generated which, when properly tracked by appropriate control of the impeller clutch and the brakes, results in the desired levels of jerk and acceleration. A tracking controller is therefore appropriate.

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.