• Journal of Institute of Control, Robotics and Systems
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• 제11권10호
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• pp.888-894
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• 2005

In this paper, experimental studies of balancing a pendulum mounted on a wheeled drive mobile robot and its position control are presented. Main PID controllers are compensated by a neural network. Neural network learning algorithm is embedded on a DSP board and neural network controls the angle of the pendulum and the position of the mobile robot along with PID controllers. Uncertainties in system dynamics are compensated by a neural network in on-line fashion. Experimental results show that the performance of balancing of the pendulum and position tracking of the mobile robot is good.

• Journal of the Korean Institute of Telematics and Electronics A
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• 제31A권10호
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• pp.161-172
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• 1994

This paper describes the design of a neurochip with on-chip learning capability in hardware with multiple processing elements. A digital architecture is adopted because its flexiblity and accuracy is advantageous for simulating the various application systems. The proposed chip consists of several processing elements to fit the large computation of neural networks, and has on-chip learning capability based on error back-propagation algorithm. It also minimizes the number of blobal control signals for processing elements. The modularity of the system makes it possible to buil various kinds of boards to match the expected range of applications.

• Journal of the Korean Institute of Intelligent Systems
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• 제6권3호
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• pp.63-72
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• 1996

In this paper, we propose a design technique for a neural network controller and apply it to power system stabilization. Using a learning algorithm of error back propagation that accepts error and change of error as inputs, the momentum learning technique is used by which reduction of learning time is possible for real time control. The related simulation results show that the proposed control techinque is more powerful than the conventional ones for dynamic responses.

• Journal of the Korean Society for Precision Engineering
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• 제22권8호
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• pp.34-41
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• 2005

Generally, it is not easy to get a suitable controller for multi variable systems. If the modeling equation of the system can be found, it is possible to get LQR control as an optimal solution. This paper suggests an LQR learning method to design LQR controller without the modeling equation. The proposed algorithm uses the same cost function with error and input energy as LQR is used, and the LQR controller is trained to reduce the function. In this training process, the Jacobian matrix that informs the converging direction of the controller Is used. Jacobian means the relationship of output variations for input variations and can be approximately found by the simple experiments. In the simulations of a hydrofoil catamaran with multi variables, it can be confirmed that the training of LQR controller is possible by using the approximate Jacobian matrix instead of the modeling equation and this controller is not worse than the traditional LQR controller.

• Journal of Korea Multimedia Society
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• 제22권5호
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• pp.558-572
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• 2019

Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. Anthropomorphic animal face masking is the process by which human characteristics are plotted on the animal kind. In this research, we are proposing a compact system which finds the resemblance between a human face and animal face using Deep Convolutional Neural Network (DCNN) and later applies morphism between them. The whole process is done by firstly finding which animal most resembles the particular human face through a DCNN based animal face classification. And secondly, doing triangulation based morphing between the particular human face and the most resembled animal face. Compared to the conventional manual Control Point Selection system using an animator, we are proposing a Viola-Jones algorithm based Control Point selection process which detects facial features for the human face and takes the Control Points automatically. To initiate our approach, we built our own dataset containing ten thousand animal faces and a fourteen layer DCNN. The simulation results firstly demonstrate that the accuracy of our proposed DCNN architecture outperforms the related methods for the animal face classification. Secondly, the proposed morphing method manages to complete the morphing process with less deformation and without any human assistance.

• The Transactions of the Korean Institute of Electrical Engineers P
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• 제53권4호
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• pp.195-200
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• 2004

Many kinds of speed sensorless control system of induction motor had been developed. But it is difficult to implement at the real system because of complex algorithm and equations. This paper investigates a novel speed sensorless control of induction motor using neural networks. The proposed control strategy is based on neural networks using stator current and output of neural model based on state observer. The errors between the stator current and the output of neural model are back-propagated to adjust the rotor speed, so that adaptive state variable will coincide with the desired state variable. This algorithm may overcome several shortages of conventional model, such as integrator problems, small EMF at low speed and relatively large sensitivity of stator resistance variation. Also, this paper presents a newly developed optimal equation about the momentum constant and the learning rate. The proposed algorithms are verified through simulation.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 한국조명전기설비학회 2007년도 춘계학술대회 논문집
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• pp.322-327
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• 2007

This paper is proposed an efficiency optimization control algorithm for IPMSM which minimizes the copper and iron losses. The design of the speed controller based on adaptive fuzzy learning control-fuzzy neural networks(ABLC-FNN) controller that is implemented using adaptive, fuzzy control and neural networks. The control performance of the AFLC-FNN controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.103.1-103
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• 2002

In variable structure control algorithms, The control law used to realized the desired sliding mode dynamics is discontinuous on the switching manifold. However, due to imperfections in switching, such as time delays, the system trajectory chatters instead of sliding along the switching manifold. This chattering is undesirable because it may excite unmodeled high frequency dynamics in the physical system. In this paper, to overcome this drawback a self-organizing fuzzy sliding mode control algorithm using gradient descent method is proposed. The proposed method has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of disturbance, parameter variations and uncertainties ill the sliding mode. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum system. The results show that both alleviation of chattering and performance are achieved.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1012-1017
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• 2003

The development of a fast, accurate, and inexpensive position-controlled pneumatic actuator that may be applied to a variety of practical positioning applications with various external loads is described in this paper. A novel modified pulso width modulation (MPWM) valve pulsing algorithm allows on/off solenoid valves to be used in place of costly servo valves. A comparison between the system response of standard PWM technique and that of the novel modified PWM technique shows that the control performance is significantly increased. A state feedback controller with position, velocity and acceleration feedback is successfully implemented as the continuous controller. Switching algorithm of control parameter using learning vector quantization neural network (LVQNN) is newly proposed. which estimates the external loads of the pneumatic actuator. The effectiveness of the proposed control algorithms are demonstrated through experiments with various loads.

• Journal of the Korean Institute of Telematics and Electronics B
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• 제32B권8호
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• pp.1119-1126
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• 1995

The construction of rule-base for a nonlinear time-varying system, becomes much more complicated because of model uncertainty and parameter variations. Furthemore, FLC does not have an ability of adjusting rule- base in responding to some sudden changes of control environments. To cope with these problems, an auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), which is known to be very effective in the optimization problem, will be proposed. The tuning of the proposed system is performed by two tuning processes(the course tuning process and the fine tuning/adaptive learning process). The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.