• Journal of the Korean Society for Precision Engineering
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• v.14 no.6
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• pp.90-98
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• 1997

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilzation of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the systems with constrained inputs and/or states. A neural identifier or a genetic algorithm identifier could be utilized for modeling the partially known systems and the unknown systems. The nature of neural networks as a parallel processor would circumvent the problem of "curwe of dimensionality". The control law has been demonstrated for both a torque input motor and a velocity input motor identified by a genetic algorithm called GENOCOPed GENOCOP.

• Journal of Drive and Control
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• v.20 no.4
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• pp.1-8
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• 2023

In recent years, robots have been utilized in various industries to reduce workload and enhance work efficiency. The following mobility offers users convenience by autonomously tracking specific locations and targets without the need for additional equipment such as forklifts or carts. In this paper, deep learning techniques were employed to recognize individuals and assign each of them a unique identifier to enable the recognition of a specific person even among multiple individuals. To achieve this, the distance and angle between the robot and the targeted individual are transmitted to respective controllers. Furthermore, this study explored the control methodology for mobility that tracks a specific person, utilizing Simultaneous Localization and Mapping (SLAM) and Proportional-Integral-Derivative (PID) control techniques. In the PID control method, a genetic algorithm is employed to extract the optimal gain value, subsequently evaluating PID performance through simulation. The SLAM method involves generating a map by synchronizing data from a 2D LiDAR and a depth camera using Real-Time Appearance-Based Mapping (RTAB-MAP). Experiments are conducted to compare and analyze the performance of the two control methods, visualizing the paths of both the human and the following mobility.