• Journal of Drive and Control
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• v.19 no.2
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• pp.1-10
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• 2022

Field robots operate in various areas, including construction, agriculture, forestry and manufacturing. Typical tasks of field robots used in various areas include excavation, flattening, and demolition. Such tasks are often accomplished in narrow alleys or indoors. In the case of field robots, there is a limit to working in a small space. Thus, to compensate for these shortcomings, many field robots equipped with Tiltrotators have recently been observed. The advantages of Tiltrotator are improved task efficiency and reduced operating time by reducing unnecessary behavior. We need simulation models that can improve the ability of new people to work and simulate tasks in advance. Thus, in this paper, we developed a simscape-based simulation model and modeling of 6DOF systems for field robots equipped with Tiltrotator. Dynamic modeling of field robot 3D models using Simcape multibody and hydraulic systems of field robots using Simcape Hydraulics were modeled. We applied a PID controller to create a control system that operates along the input angle. Simulation results show that errors occur when comparing input and output angles, but overall, they move along input angles.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.9
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• pp.1065-1071
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• 2012

Wind energy is becoming one of the most preferable alternatives to conventional sources of electric power that rely on fossil fuels. For stable electric power generation, constant rotating speed control of a wind turbine is performed through pitch control and stall control of the turbine blades. Recently, variable pitch control has been implemented in modern wind turbines to harvest more energy at variable wind speeds that are even lower than the rated one. Although wind turbine pitch controllers are currently optimized using a step response via the Ziegler-Nichols auto-tuning process, this approach does not satisfy the requirements of variable pitch control. In this study, the variable pitch controller was optimized by a genetic algorithm using a neural network model that was constructed by the Latin Hypercube sampling method to improve the Ziegler-Nichols auto-tuning process. The optimized solution shows that the root mean square error, rise time, and settle time are respectively improved by more than 7.64%, 15.8%, and 15.3% compared with the corresponding initial solutions obtained by the Ziegler-Nichols auto-tuning process.