손흥선 교수

This paper presents a control system design for a particular form of variable-reluctance spherical motors, referred to here as a spherical wheel motor (SWM). The method decoupling the spin from the inclination offers a means to control, in open loop (OL), the inclination of a continuously rotating shaft. Specifically, the OL controller presented in this paper combines a multispeed switching control law for controlling the spin motion and a dynamic model-based control law for regulating the rotor

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Design and control of multi-degree-of-freedom (DOF) electromagnetic actuators require a good understanding of the magnetic fields, and involve real-time calculation of magnetic forces. This paper presents a method to derive distributed multipole (DMP) models for characterizing the magnetic field and torque of permanent magnet (PM) based devices. The DMP method, which offers magnetic-field solutions i

An automatic target tracking algorithm must be capable of dealing with an unknown number of targets and their trajectory behaviour inside the surveillance region. However, due to target motion uncertainties, heavily populated clutter measurements and low detection probabilities of targets, the smoothing algorithms often fail to detect the true number of target trajectories. In this study, the authors discussed some deficiencies and insignificances of existing smoothing algorithms and proposed a

This paper presents a novel method to calculate magnetic inductance with a fast-computing magnetic field model referred to as the extended distributed multi-pole (eDMP) model. The concept of mutual inductance has been widely applied for position/orientation tracking systems and applications, yet it is still challenging due to the high demands in robust modeling and efficient computation in real-time applications. Recently, numerical methods have been utilized in design and analysis of magnetic f

In heavily cluttered environments, it is difficult to estimate the uncertain motion of an unknown number of targets with low detection probabilities. In particular, for tracking multiple targets, standard multi‐target data association algorithms such as joint integrated probabilistic data association (JIPDA), face complexity and severely limited applicability due to a combinatorially increasing number of possible measurement‐to‐track associations. Smoothers refine the target estimates based on f