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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.

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.

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.

In this paper, the design, implementation, and testing of an autonomous agricultural robot with GPS guidance is presented. This robot is also responsible for weed detection and killing by spraying appropriate herbicide as well as fertilizing. This rover is powered by 5 12 V electric bike batteries and two electric motors. Machine learning algorithms such as Haar feature-based cascade classifiers has been utilized to detect three kinds of common weeds found in a corn field. The robot control system consists of GPS guided control of propulsion system and steering actuators, an image processing and detection system, and a spray control system for herbicide and fertilizer applications. Multiple microprocessors such as Raspberry Pi 3, Arduino, as well as an on-board computer have used to provide all control functions in an integrated fashion. Open sources software such as Mission Planner and ReachView have been used to provide autonomous guidance of the vehicle.

In this paper, feasibility study was completed to prove the ability of detecting soot load amount in diesel particulate filter using electromagnetic finite element model. It is critical to accurately measure the soot load in a Diesel Particulate Filter (DPF) in order to optimize the active regeneration process that clears the filter clogged with soot. Tetrahedral mesh was created with electrostatic solid element for this analysis. Because of the geometry of the DPF and the narrowness of some sections between holes, a very fine mesh was generated. A model consistency study was created illustrating the effect of clogging on the internal electric field of the filter. Multiple scenarios representing various clogging configurations were simulated. These configurations included a fully clogged filter, the top-right quadrant of holes clogged, and the lower half of holes clogged.