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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.

This paper discusses the development of a Distributed Intelligent Ground Speed Control System, similar to a cruise control system, based on Fuzzy Logic. Fuzzy sets have been developed to input speed error, acceleration and the absolute speed error in order to arrive at a defuzzified output for the impeller clutch control, brake control and the control law selection. A PI controller and a Sliding Mode controller are utilized based on the magnitude of the Absolute Speed Error. A road model is introduced with erratic set speed profiles, which is introduced to replicate a similar situation for a Stop & Go procedure. The system is simulated in a MATLAB/SIMULINK environment and the results indicate smooth and cooperative switching between the controllers stimulated by the Fuzzy Logic Controller.

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.