• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.1
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• pp.35-49
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• 1994

It is well known that, for nominimum phase systems, a conventional linear controller of PID type or an adaptive controller of this structure shows limitation in achieving a satisfactory performance under tight specifications. In this paper, we combine a neuro-controller with a PI-controller with off-line learning capability provided by the Genetic Algorithm to propose a novel neuro-controller to control nonminimum phase systems effectively. The simulation results show that our proposed model is more efficient with faster rising time and less undershoot effect when the performances of the proposed controller and a conventional form are compared.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.271-276
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• 2006

To implement a generalization of value functions in Adaptive Search Element (ASE)-reinforcement learning, CMAC (Cerebellar Model Articulation Controller) is integrated into ASE controller. ASE-reinforcement learning scheme is briefly studied to discuss how CMAC is integrated into ASE controller. Neighbourhood Sequential Training for CMAC is utilized to establish the look-up table and to produce discrete control outputs. In computer simulation, an ASE controller and a couple of ASE-CMAC neural network are trained to balance the inverted pendulum on a cart. The number of trials until the controllers are established and the learning performance of the controllers are evaluated to find that generalization ability of the CMAC improves the speed of the ASE-reinforcement learning enough to realize the cartpole control system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.348-353
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• 1994

Magnetic Servo Levitation (MSL) has been proposed as a method to drive a fast-tool servo system. This paper discusses some fundamental control and modeling issues in the development of a long-range high-bandwidth fast-tool servo based on MSL. A resursive linear model is developed to describe the system's dynamics linear model is developed to describe the system's dynamics, and further used to discuss controller design. For a given controller architecture, the performance of two controllers is then compared, one based on an approximation to the inverse plant dynamics, the second based on a adaptive neural network.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.7
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• pp.15-25
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• 2011

This proposes an online loss minimization algorithm for series PI(SPI) based interior permanent magnet synchronous motor(IPMSM) drive to yield high efficiency and high dynamic performance over wide speed range. The loss minimization algorithm is developed based on the motor model. In order to minimize the controllable electrical losses of the motor and thereby maximize the operating efficiency, the d-axis armature current is controlled optimally according to the operating speed and load conditions. For vector control purpose, a SPI is used as a speed controller which enables the utilization of the reluctance torque to achieve high dynamic performance as well as to operate the motor over a wide speed range. Also, this paper proposes current control of model reference adaptive fuzzy controller(MFC), and estimation of speed using artificial neural network(ANN) controller. The proposed efficiency optimization control, SPI, MFC, ANN in this paper is applied to IPMSM drive system, the validity of this paper is proved by analyzing response characteristics in variety operating conditions.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1539-1544
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• 2005

The gauge control of the fishing mill is very important because more and more accurately sized hot rolled coils are demanded by customers recently. Because the mill constant and the plasticity coefficient vary with the specifications of the mill, the classification of steel, the strip width, the strip thickness and the slab temperature, the variation of these parameters should be considered in the automatic gauge control system(AGC). Generally, the AGC gain is used to minimize the effect of the uncertain parameters. In a practical field, operators set the AGC gain as a constant value calculated by FSU (Finishing-mill Set-Up model) and it is not changed during the operating time. In this paper, the thickness data signals that occupy different frequency bands are respectively extracted by adaptive filters and then the main cause of the thickness variation is analyzed. Additionally, the AGC gain is adaptively tuned to reduce this variation using the online tuning model. Especially ANFIS(Adaptive-Neuro-based Fuzzy Interface System) which unifies both fuzzy logics and neural networks, is used for this gain adjustment system because fuzzy logics use the professionals' experiences about the uncertainty and the nonlinearity of the system. Simulation is performed by using POSCO's data and the results show that proposed on-line gain adjustment algorithm has a good performance.

• Smart Structures and Systems
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• v.19 no.4
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• pp.341-350
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• 2017

Based on the super elastic properties of the shape memory alloy (SMA) and the inverse piezoelectric effect of piezoelectric (PZT) ceramics, a kind of hybrid semi-active control device was designed and made, its mechanical properties test was done under different frequency and different voltage. The local search ability of genetic algorithm is poor, which would fall into the defect of prematurity easily. A kind of adaptive immune memory cloning algorithm(AIMCA) was proposed based on the simulation of clone selection and immune memory process. It can adjust the mutation probability and clone scale adaptively through the way of introducing memory cell and antibody incentive degrees. And performance indicator based on the modal controllable degree was taken as antigen-antibody affinity function, the optimization analysis of damper layout in a space truss structure was done. The structural seismic response was analyzed by applying the neural network prediction model and T-S fuzzy logic. Results show that SMA and PZT friction composite damper has a good energy dissipation capacity and stable performance, the bigger voltage, the better energy dissipation ability. Compared with genetic algorithm, the adaptive immune memory clone algorithm overcomes the problem of prematurity effectively. Besides, it has stronger global searching ability, better population diversity and faster convergence speed, makes the damper has a better arrangement position in structural dampers optimization leading to the better damping effect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4084-4104
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• 2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.57-66
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper proposes hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme, The validity of the proposed controller is verified by results at different dynamic operating conditions.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.27-35
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• 2021

Artificial life is used in various fields of applied science by evaluating natural life-related systems, their processes, and evolution. Research has been actively conducted to evolve physical body design and behavioral control strategies for the dynamic activities of these artificial life forms. However, since co-evolution of shapes and neural networks is difficult, artificial life with optimized movements has only one movement in one form and most do not consider the environmental conditions around it. In this paper, artificial life that co-evolve bodies and neural networks using predator-prey models have environmental adaptive movements. The predator-prey hierarchy is then extended to the top-level predator, medium predator, prey three stages to determine the stability of the simulation according to initial population density and correlate between body evolution and population dynamics.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.61-66
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• 2002

A position control algorithm for a flexible manipulator is studied. The proposed algorithm is based on a fuzzy theory with a Steady State Genetic Algorithm(SSGA) and an Adaptive Genetic Algorithms(AGA). The proposed controller for a flexible manipulator have decreased 90.8%, 31.8%, 31.3% in error when compared with a conventional fuzzy controller, fuzzy controller using neural network, fuzzy controller using evolution strategies, respectively when the weight and the velocity of end-point are 0.8k9 and 1m/s, respectively.