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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.

A Generalized Predictive Kinetic Energy Controller (GPKEC), which has been previously developed, is implemented to control the machine tool vibration in a lathe turning process. The control variable, tool feed, is computed using acceleration feedback through the GPKEC algorithm. The feed is controlled by attaching a high torque/low inertia permanent magnet DC servomotor to the main feed rod through a high performance timing belt. Experiments are carried out for a number of cases. The system identification of the overall machining process is done on-line and precedes the control action. Accelerometers have been used to sense the vibration signal in the feed direction. The experimental results show that GPKEC can effectively suppress the chatter vibration in a single point turning process, even in presence of an appreciable change in the dynamics of the process. GPKEC has also been observed to be robust against step disturbances.