• The Journal of Korean Institute of Communications and Information Sciences
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• v.10 no.5
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• pp.258-265
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• 1985

A design method for a single-input single-output discrete time model reference adaptive system is described in this paper. By using the state-variable filters into inputs and outputs in reference model and unknown system, a simple adaptive structure which use all accessible signals can be constructed. Some papers for the adaptive shstem is which thw relative degree of unknown system have one or two have been reported, but the resulting adaptive system are intricate in structures and the design theories for the model reference adaptive system are not generalized. In this paper, for having two or more relative degrees, it has been verified that an adaptive scheme can be obtained by introducing a simple linear filter.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1283-1286
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• 2003

Human can update the memory with new information not forgetting acquired information in the memory. ART(Adaptive Resonance Theory) does not need to change all information. The methodology of ART is followed. The ART updates the memory with the new information that is unknown if it is similar with the memorized information. On the other hand, if it is unknown information the ART adds it to the memory not updating the memory with the new one. This paper shows that ART is able to classify sensory information of a certain object. When ART receives new information of the object as an input, it searches for the nearest thing among the acquired information in the memory. If it is revealed that new information of the object has similarity with the acquired object, the model is updated to reflect new information to the memory. When new object does not have similarity with the acquired object, the model register the object into new memory

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.496-499
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• 2003

Support Vector Machines (SVMs) is applied to a practical problem as one of standard tools for machine learning. The application of Reinforcement Learning (RL) and SVMs in action of mobile robot is investigated. A technique to decide the action of autonomous mobile robot in practice is explained in the paper, The proposed method is to find n basis for good action of the system under unknown environment. In multi-dimensional sensor input, the most reasonable action can be automatically decided in each state by RL. Using SVMs, not only optimal decision policy but also generalized state in unknown environment is obtained.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.12-19
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• 2015

In order to guide the robots move along a collision-free path efficiently and reach the goal position quickly in the unknown multi-obstacle environment, this paper presented the navigation problem of a wheel mobile robot based on proximity sensors by fuzzy logic controller. Then a genetic algorithm was applied to optimize the membership function of input and output variables and the rule base of the fuzzy controller. Here the environment is unknown for the robot and contains various types of obstacles. The robot should detect the surrounding information by its own sensors only. For the special condition of path deadlock problem, a wall following method named angle compensation method was also developed here. The simulation results showed a good performance for navigation problem of mobile robots.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.416-422
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• 2010

Estimating condition of geotechnical structures are difficult because of nonlinear time dependency and seasonal effects. Measuring data of structure failure is highly variable in time and space, and a unique approach cannot be defined to model structure movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, this method is advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured.

• Journal of Power Electronics
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• v.9 no.5
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• pp.791-799
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• 2009

This paper presents a time-varying sinusoidal disturbance compensation method (based on an adaptive estimation scheme) for induction motor drives fed by a matrix converter. In previous disturbance accommodation methods, sinusoidal disturbances with unknown time-invariant frequencies have been considered. However, in the new method proposed here, disturbances with unknown time-varying frequencies are considered. The disturbances can be estimated by using a disturbance accommodating observer, and an additional control input is added to the induction machine drive. The stability analysis is carried out considering the disturbance estimation error and simulation results are shown to illustrate the performance of the proposed solution.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.285-287
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• 2006

The paper treats the nonlinear robust control of Chua's circuit having Chuar's diode as an element based on the internal model principle. The Chua's diode has unknown nonlinear parameters and the circuits parameters are alos assumend unknown. Nonlinear regulator equations are established to obtain 3-fold equilibrium equations on which the output error is zero. Also an internal model of the 3-fold exosystem is constructed for obtaining the control law. Pole Placement method is used for obtaining the feeback control law. Simulation results are presented for tracking the sinusoidal and constant reference input signal. Asymptotic trajectory control and the suppression of chaotic motion in spite of uncertainties in the system are accomplished.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.359-363
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• 1994

In this paper the system called the inverse model compensation system is proposed as a system whose input-output transfer function can be regarded as that of a model with uncertainty in spite of including an unknown plant. And their to construct the robust model following system, which is of low sensitivity and robust stability, in order to control the inverse model compensation system is proposed. The simulation experiments show that the robust model following system including the inverse model compensation system is practical and useful as a system which controls unknown plants.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.347-350
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• 1996

With only the classical PID controller applied to control of a DC motor, a good (target) performance characteristic of the controller can be obtained, if all the model parameters of DC motor and operating conditions such as external load torque, disturbance, etc. are exactly known. However, in case when some of system parameters or operating conditions are uncertain or unknown, the fixed PID controller does not guarantee the good performance which is assumed with precisely known system parameters and operating conditions. In view of this and robustness enhancement of DC motor control system, we propose a PID learning controller which consists of a set of learning rules for PID gain tuning and learning of an auxiliary input. The proposed PID learning controller is shown to drive the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one globally asymptotically. Computer simulation results are given to demonstrate the effectiveness of the proposed PID learning controller, thereby showing whose superiority to the conventional fixed PID controller.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.357-362
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• 2002

The underlying principle of fault detection via unknown input observer is to make the state estimation error independent of disturbances(or unknown inputs). In this paper, we present a systematic method that can exactly assign the eigenstructure with disturbance suppression and fault isolation capability. A desired eigenstructure for both fault isolation and disturbance suppression is obtained by an optimization method. For the dual purposes, terms for fault isolation and far disturbance suppression are included in the employed objective function for the optimization. The proposed scheme is applied to a simple example to confirm the usefulness of the method.