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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.

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.