• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.51-55
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• 1995

An adaptive unified predictive control (UPC) algorithm is applied to a batch polymerization reactor for poly(methyl methancrylate) (PMMA) and the effects of controller parameters are investigated. Computational studies are performed for a batch polymerization system model developed in this study. A transfer function in parametric form is estimated by recursive least squares (RLS) method, and the UPC algorithm is implemented to control the reactor temperature on the basis of this transfer function. The adaptive unified predictive controller shows a better performance than the PID controller for tracking set point changes, especially in the latter part of reaction course when gel effect becomes significant. Various performance can be acquired by selecting adequate values for parameters of the adaptive unified predictive controller; in other words, the optimal set of parameters exists for a given set of reaction conditions and control objective.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.263-272
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• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.3
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• pp.145-155
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• 1990

In this paper, an adaptive Infinite Impulse Response (IIR) filter algorithm using output error method, which prevents poles of a system transfer function from being out of unit circle, is proposed, and it is proved that the proposed algorithm always satisfies hyperstability. The proposed algorithm is applied to an Adaptive Noise Canceller (ANC), and compared with a Least Square (LS) method adaptive IIR filter algorithm and an adaptive Finite Inpulse Response (FIR) filter algorithm. As a result, the validity of the proposed algorithm is proved.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.157-162
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• 2003

Estimates from an EM algorithm are somewhat sensitive to the initial values for the estimates, and it is more likely when the model becomes larger and more complicated. In this article, we examined how the estimates fluctuate during an EM procedure for a recursive model of categorical variables. It is found that the fluctuation takes place mostly during the first half of the procedure and that it can be subdued by applying the Bayesian method of estimation. Both simulation data and real data are used for illustration.

• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.320-327
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• 2005

A new mechanism for hyper redundant manipulator (HRM) is presented, which comprises of serially assembled compound three-bar linkages (CTL). The CTL mechanism has some unique properties. This paper presents the forward and inverse kinematics of this mechanism and shows the simulation of the HRM havig 9 CTL units. The recursive algorithm of the inverse kinematics that the author originally developed is employed. It is fast and stable ; moreover, it enables us to obtain a solution in which the end-point of the HRM is controlled by a portion of joints. It also presents the method of the dynamical analysis. There exist kinematical constraints in the proposed closed linkage mechanism. In the dynamic analysis constraints are sufficiently sustained by the constraint stabilization method that the author developed. The mechanical structure of the HRM having some CTL units that is under construction is shown.

• Korean Journal of Computational Design and Engineering
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• v.10 no.2
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• pp.107-113
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• 2005

Octree-based algorithm is developed for machining simulation. Most of commercial machining simulators are based on Z map model, which have several limitations to get a high precision in 5 axis machining simulation. Octree representation is three dimensional decomposition method. So it is expected that these limitations be overcome by using octree based algorithm. By using the octree model, storage requirement is reduced. And also recursive subdivision was processed in the boundaries, which reduces useless computation. The supersampling method is the most common form of the anti-aliasing and usually used with polygon mesh rendering in computer graphics. Supersampling technique is applied for advancing its efficiency of the octree algorithm.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1724-1729
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• 2003

Two different subsystem synthesis methods with independent generalized coordinates have been developed and compared. In each formulation, the subsystem equations of motion are generated in terms of independent generalized coordinates. The first formulation is based on the relative Cartesian coordinates with respect to moving subsystem base (virtual) body. The second formulation is based on the relative joint coordinates using recursive formulation. Computational efficiency of the formulations has been compared theoretically by the operational counting method.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.285-288
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• 2002

This paper presents external load disturbance compensation that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a deadbeat observer To reduce of the noise effect, the post-filter, which is implemented by MA process, is adopted. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller The proposed estimator is combined with a high performance load torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.240-240
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• 2000

This paper presents two algorithms based on the concept of the frequency domain adaptive filter(FDAF). First the frequency domain recursive least squares(FRLS) algorithm with the overlap-save filtering technique is introduced. This minimizes the sum of exponentially weighted square errors in the frequency domain. To eliminate discrepancies between the linear convolution and the circular convolution, the overlap-save method is utilized. Second, the sliding method of data blocks is studied Co overcome processing delays and complexity roads of the FRLS algorithm. The size of the extended data block is twice as long as the filter tap length. It is possible to slide the data block variously by the adjustable hopping index. By selecting the hopping index appropriately, we can take a trade-off between the convergence rate and the computational complexity. When the input signal is highly correlated and the length of the target FIR filter is huge, the FRLS algorithm based on the block processing technique has good performances in the convergence rate and the computational complexity.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.578-580
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• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.