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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.

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.