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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 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.

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.