• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.279-288
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• 2004

This paper presents a novel design for a tubular linear brushless permanent-magnet motor. In this design, the magnets in the moving part are oriented in an NS-NS―SN-SN fashion which leads to higher magnetic force near the like-pole region. An analytical methodology to calculate the motor force and to size the actuator was developed. The linear motor is operated in conjunction with a position sensor, three power amplifiers, and a controller to form a complete solution for controlled precision actuation. Real-time digital controllers enhanced the dynamic performance of the motor, and gain scheduling reduced the effects of a nonlinear dead band. In its current state, the motor has a rise time of 30 ms, a settling time of 60 ms, and 25% overshoot to a 5-mm step command. The motor has a maximum speed of 1.5 m/s and acceleration up to 10 g. It has a 10-cm travel range and 26-N maximum pull-out force. The compact size of the motor suggests it could be used in robotic applications requiring moderate force and precision, such as robotic-gripper positioning or actuation. The moving part of the motor can extend significantly beyond its fixed support base. This reaching ability makes it useful in applications requiring a small, direct-drive actuator, which is required to extend into a spatially constrained environment.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.35-40
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• 1998

A nonlinear attitude model of a satellite with thrusters, magnetic torquers and a reaction wheel cluster is developed. Then the linearized version of this satellite attitude model is derived far the attitude hold mode. For comparison purpose, various control methods are considered for attitude control of a satellite. We consider a proportional derivative controller which is actually used in the remote sensing satellite, KOMPSAT. Then a comparison is made with an H$_2$controller, an H$\sub$$\infty$/ controller, and a mixed H$_2$/ H$\sub$$\infty$/ controller. The analysis and numerical studies show that the proportional derivative controller's performance is limited in the sense that the pitch angle cannot approach zero. The simulations also show that among three control methods (H$_2$control, H$\sub$$\infty$/ control, and mixed H$_2$/ H$\sub$$\infty$/ control) H$_2$control has the fastest response time, H$\sub$$\infty$/ control has the slowest and mixed H$_2$/ H$\sub$$\infty$/ control comes in between the first two control methods. On the other hand, H$\sub$$\infty$/ control used least amount of control effort while H$_2$control required the most.

• Journal of Korean Neurosurgical Society
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• v.66 no.2
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• pp.113-120
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• 2023

Artificial intelligence (AI) is a field of computer science that equips machines with human-like intelligence and enables them to learn, reason, and solve problems when presented with data in various formats. Neurosurgery is often at the forefront of innovative and disruptive technologies, which have similarly altered the course of acute and chronic diseases. In diagnostic imaging, such as X-rays, computed tomography, and magnetic resonance imaging, AI is used to analyze images. The use of robots in the field of neurosurgery is also increasing. In neurointensive care units, AI is used to analyze data and provide care to critically ill patients. Moreover, AI can be used to predict a patient's prognosis. Several AI applications have already been introduced in the field of neurosurgery, and many more are expected in the near future. Ultimately, it is our responsibility to keep pace with this evolution to provide meaningful outcomes and personalize each patient's care. Rather than blindly relying on AI in the future, neurosurgeons should gain a thorough understanding of it and use it to enhance their patient care.

• Journal of Positioning, Navigation, and Timing
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• v.1 no.1
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• pp.35-43
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• 2012

The UGV is one of the special field robot developed for mine detection, surveillance and transportation. To achieve successfully the missions of the UGV, the accurate and reliable navigation data should be provided. This paper presents design and implementation of multi-sensor-based open architecture integrated navigation for localization of UGV. The presented architecture hierarchically classifies the integrated system into four layers and data communications between layers are based on the distributed object oriented middleware. The navigation manager determines the navigation mode with the QoS information of each navigation sensor and the integrated filter performs the navigation mode-based data fusion in the filtering process. Also, all navigation variables including the filter parameters and QoS of navigation data can be modified in GUI and consequently, the user can operate the integrated navigation system more usefully. The conventional GPS/INS integrated system does not guarantee the long-term reliability of localization when GPS solution is not available by signal blockage and intentional jamming in outdoor environment. The presented integration algorithm, however, based on the adaptive federated filter structure with FDI algorithm can integrate effectively the output of multi-sensor such as 3D LADAR, vision, odometer, magnetic compass and zero velocity to enhance the accuracy of localization result in the case that GPS is unavailable. The field test was carried out with the UGV and the test results show that the presented integrated navigation system can provide more robust and accurate localization performance than the conventional GPS/INS integrated system in outdoor environments.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.480-480
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• 2000

In this paper, we proposed a fuzzy-neuro controller to control the speed of wound rotor induction motor with slip energy recovery. The speed is limited at some range of sub-synchronous speed of the rotating magnetic field. Control speed by adjusting resistance value in the rotor circuit that occurs the efficiency of power are reduced, because of the slip energy is lost when it passes through the rotor resistance. The control system is designed to maintain efficiency of motor. Recently, the emergence of artificial neural networks has made it conductive to integrate fuzzy controllers and neural models for the development of fuzzy control systems, Fuzzy-neuro controller has been designed by integrating two neural network models with a basic fuzzy logic controller. Using the back propagation algorithm, the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the control speed of a wound rotor induction motor process. The control system is designed to maintain efficiency of motor and compensate power factor of system. That is: the proposed controller gives the controlled system by keeping the speed constant and the good transient response without overshoot can be obtained.