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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 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.

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.