• Journal of the Korean Society for Precision Engineering
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• v.13 no.12
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• pp.106-113
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• 1996

This paper presents a modeling of undersea robot manipulators and a control scheme appropriate for manipulating the manipulators working under the unstrcuctured sea water environment. Under the sea, the added mass and added moment of inertia, buoyancy, and drag forces should be considered in modeling the dynamics of the robot manipulators. Due to the complexity of them, the desired dynamics of manipulators can not be accomplished by the conventional control schemes. Hence, a sliding mode control is applied to control the modeling error.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1070-1078
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• 2000

A nonlinear adaptive flight control system is proposed using a backstepping controller with neural network controller. The backstepping controller is used to stabilize all state variables simultaneously without the two-timescale assumption that separates the fast dynamics, involving the angular rates of the aircraft, from the slow dynamics which includes angle of attack, sideslip angle, and bank angle. It is assumed that the aerodynamic coefficients include uncertainty, and an adaptive controller based on neural networks is used to compensate for the effect of the aerodynamic modeling error. It is shown by the Lyapunov stability theorem that the tracking errors and the weights of neural networks exponentially converge to a compact set. Finally, nonlinear six-degree-of-freedom simulation results for an F-16 aircraft model are presented to demonstrate the effectiveness of the proposed control law.

• Journal of Power Electronics
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• v.9 no.5
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• pp.800-808
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• 2009

This paper presents a practical method of pressure control for a hydraulic oil-pump system using an SR (Switched Reluctance) drive. For a 6Mpa grade hydraulic oil-pump, a 2.6kW SR drive is developed. In order to get high performance pressure dynamics in actual applications, a data based PID control scheme is proposed. The look-up table from a pre-measured data base produces an approximate current reference based on motor speed and oil-pressure. A PID controller can compensate for the pressure error. With the combination of the two references, the proposed control scheme can achieve fast dynamics and stable operation. Furthermore, a suitable current controller considering the nonlinear characteristics of an SRM (Switched Reluctance Motor) and practical test methods for data measuring are presented. The proposed control scheme is verified by experimental tests.

• Journal of applied mathematics & informatics
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• v.26 no.3_4
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• pp.739-750
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• 2008

A phenomenon which is seen in some of the manufacturing systems and production planning is chaos and the butterfly effect. The butterfly effect points out that in case of the presence of nonlinear relations in system and incorrect estimation of initial values of variables, the error in the estimates of system state will be intensified, and after a while there will be a large distance between available state of system and reality. Using mathematical means and computer simulation, we have tried to demonstrate that in a production system the numerical combination of Cycle Time (CT), Adjustment Time between existing and desired Work In Progress (WIP), and Adjustment Time between current and desired inventory can lead to chaos and butterfly effect in the behavior of the inventory state variable. Our paper concludes with a discussion of a hypothesis that emerged from this research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.1
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• pp.91-95
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• 2009

We investigated the characteristics of a capacitive nano-accelerometer based on carbon nanotube by means of classical molecular dynamics simulations. The position of the telescoping nanotube was controlled by the externally applied force and the feedback sensing was achieved from the capacitance change. Considering energy dissipation, the oscillation features of the nano-accelerometers were similar, regardless of their initial displacements. The capacitance variations, which were almost linearly proportional to the applied acceleration, were monitored within an error tolerance.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2702-2704
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• 2005

This paper proposes the genetic algorithm (GA)-based fuzzy modeling method for intelligent tracking of a maneuvering target. When the maneuvering to turn or taking evasive action, the performance of the standard Kalman filter has been degraded because residual between the modeled target dynamics and the actual target dynamics. To solve this problem, the state prediction error is minimized by the intelligent estimation method. Then, this filter is corrected by measurement corrections which is the fuzzy system. The performance of the proposed method is compared with those of the input estimation(IE) technique through computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.440-445
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• 1994

An application of H$_{\infty}$ synthesis to contact control of a manipulator is suggested. Based on computed torque linearization of a manipulator, a target dynamics for contact motion control is defined and used as a reference model. The target dynamics relates position and force errors through free motion impedance and force error compensators. The H$_{\infty}$ control synthesis is adopted to find an optimum the compensator for position tied force control in various directions of the end-effector. The optimization is performed on the augmented criteria, which trades off the sensitivity function of the errors and the input load at the joints. A design example of the compensator is provided that meets the design specifications.s.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.324-327
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• 1994

In this paper, a new design method of variable structure model following control system(VSMFCS) for robot manipulators is proposed. The proposed controller overcomed reaching phase problem by using function augmenting scheme to the sliding surface. Therefore, it can be guaranteed that the overall system always has a robust property against parameter variations and external disturbances. Furthermore, the proposed controller does not use the model state, .chi.$_{m}$, different from other previous works. Regardless of not using the model state, the model following error dynamics, virtual dynamics, is shown to be globally exponentially stable. The efficiency of the proposed method has been demonstrated by an example.e.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.898-906
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• 1999

A control structure is introduced for the purpose of rejecting periodic (or repetitive) disturbances on a tracking system. The objective of the proposed structure is to drive the output of the system to the reference input that will result in perfect following without any changing the inner configuration of the system. The structure includes an adaptation block which learns the dynamics of the periodic disturbance and forces the interferences, caused by disturbances, on the output of the system to be reduced. Since the control structure acquires the dynamics of the disturbance by on-line adaptation, it is possible to generate control signals that reject any slowly varying time-periodic disturbance provided that its amplitude is bounded. The artificial neural network is adopted as the adaptation block. The adaptation is done at an on-line process. For this , the real-time recurrent learning (RTRL) algoritnm is applied to the training of the artificial neural network.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1617-1620
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• 2005

The current control for interior permanent magnet machines is more complicate than surface permanent magnet machine because of its torque characteristic depending on the reluctance. For high performance torque control, it requires state decoupling between the d-current and q-current dynamics. However the variation of the inductances, which couples the state dynamics of the currents, makes the state decoupling difficult. So some decoupling methods have developed to cope this variations and each current can be regulated independently. This paper presents a novel approach for fully decoupling the states cross-coupling using parameter adaptation. The adaptation method is based on the error between reference currents and the currents with state decoupling which have to follow the references. This method is more object-oriented than the other online parameter estimation methods in IPM machine and other electrical machines