• Journal of Magnetics
• /
• v.16 no.1
• /
• pp.51-57
• /
• 2011

A power supply for magnetic-stimulation devices was designed via a control algorithm that involved a start current application based on a resonant converter. In this study, a new power supply for magnetic-stimulation devices was designed by controlling the pulse repetition frequency and pulse width. The power density could be controlled using the start-current-compensation and ZCS (zero-current switching) resonant converter. The results revealed a high-repetition-frequency, high-power magnetic-stimulation device. It was found that the stimulation coil current pulse width and that pulse repetition frequency could be controlled within the range of 200-450 ${\mu}S$ and 200-900 pps, respectively. The magnetic-stimulation device in this study consisted of a stimulation coil device and a power supply system. The maximum power of the stimulation coil from one discharge was 130 W, which was increased to 260 W using an additional reciprocating discharge. The output voltage was kept stable in a sinusoidal waveform regardless of the load fluctuations by forming voltage and current control using a deadbeat controller without increasing the current rating at the starting time. This paper describes this magnetic-stimulation device to which the start current was applied.

• Journal of Korean Society of Steel Construction
• /
• v.21 no.2
• /
• pp.175-181
• /
• 2009

An intelligent control technique using a neural network is proposed for offshore structures exposed to sea-bed earthquakes. Fluid-structure interaction effect was considered in developing controller and a training algorithm for the neural network is presented. In the numerical example, the performance of the proposed neural network controller was compared with that of a passive controller and uncontrolled structures. Based on the example, it can be concluded that the proposed neuro-control scheme can be used for offshore structures with nonlinear characteristics due to its interaction with fluid.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2261-2263
• /
• 2001

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. These rules can then be applied to the system. It is very important to decide parameters of IF-THEN rules. Because fuzzy controller can make more adequate force to the plant by means of parameter optimization, which is accomplished by learning procedure. In this paper, we apply fuzzy controller designed to the Swing-UP Inverted pendulum.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.6 s.171
• /
• pp.207-212
• /
• 2005

The purpose of this work is to develop the FES controller that can cope with the muscle fatigue which is one of the most important problems of current FES (Functional Electrical Stimulation). The feasibility of the proposed FES controller was evaluated by simulation. We used a fitness function to describe the effect of muscle fatigue and recovery process. The FES control system was developed based on the biological neuronal system. Specifically, we used PD (Proportional and Derivative) and GC (Gravity Compensation) control, which was described by the neuronal feedback structure. It was possible to control of multiple joints and muscles by using the phase-based PD and GC control method and the static optimization. As a result, the proposed FES control system could maintain the cycling motion in spite of the muscle fatigue. It is expected that the proposed FES controller will play an important role in the rehabilitation of SCI patient.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.6
• /
• pp.494-499
• /
• 2013

Based on the state-feedback adaptive neuro-control algorithm for a SISO nonaffine pure-feedback nonlinear system proposed in [15], an output-feedback controller is proposed in this paper. The output-feedback adaptive neural-net controller for the considered nonlinear system has not been previously proposed in any other literatures yet. The proposed output-feedback controller inherits all the advantages of [15] such that it does not adopt backstepping and this results in relatively simple control and adapting laws. Only one neural network is required for the proposed adaptive controller. The proposed neural-net control scheme expands the applicable class of nonlinear systems.

• Smart Structures and Systems
• /
• v.20 no.6
• /
• pp.657-668
• /
• 2017

In this research, an active vibration suppression of a smart beam having piezoelectric sensor and actuators is investigated by designing separate controllers comprising a linear quadratic regulator and a neural network. Firstly, design of a smart beam which consists of a cantilever aluminum beam with surface bonded piezoelectric patches and a designed mechanism having a micro servomotor with a mass attached arm for obtaining variations in the frequency response function are presented. Secondly, the frequency response functions of the smart beam are investigated experimentally by using different piezoelectric patch combinations and the analytical models of the smart beam around its first resonance frequency region for various servomotor arm angle configurations are obtained. Then, a linear quadratic regulator controller is designed and used to simulate the suppression of free and forced vibrations which are performed both in time and frequency domain. In parallel to simulations, experiments are conducted to observe the closed loop behavior of the smart beam and the results are compared as well. Finally, active vibration suppression of the smart beam is investigated by using a linear controller with a neural network based adaptive element which is designed for the purpose of overcoming the undesired consequences due to variations in the real system.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.1251-1253
• /
• 2004

This paper is proposed a fuzzy neural network controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neuro-fuzzy control(NFC) and estimation of speed using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• International journal of advanced smart convergence
• /
• v.3 no.2
• /
• pp.18-21
• /
• 2014

A control application requirements currently used is very low, such as packet loss rate, minimum delay on sensor networks with quality of service (QoS) requirements some packet delivery guarantee. This paper is the sampling period at the end of the actuator and sensor data transfer related to the Miss ratio for each source sensor node, use the controller and the internal ANFIS. The proposed scheme has the advantages of simplicity, scalability, and General. Simulation results of the proposed scheme can provide QoS support in WSANs.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1993.10a
• /
• pp.402-405
• /
• 1993

This paper presents design.dynamic modeling and control issues of a novel type of an ER valve-cylinder system incorporating with an electro-rheological(ER) fluid. The yield stress of the ER fluid to be employed to the proposed system is evaluated as a function of applied electric fields. The design and manufacturing process of the ER valve which features fast system response and simple mechanism are undertaken on the basis of model parameters. The governing equation for the hydraulic and pneumatic model is constructed by incorporation with the field-dependent Bingham behavior of the ER fluid. An effective neuro controller is proposed to realize an accurate position control.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.14-19
• /
• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.