• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.176-181
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• 1992

In this paper, an adaptive generalized predictive control(GPC) algorithm which minimizes a N-stage cost function is proposed. The resulting controller is based on GPC algorithm and can be used in unknown plant parameters as the parameters of one step ahead predictor are estimated by recursive least squares method. The estimated parameters are extended to G,P, and F amtrix which contain the parameters of N step ahead predictors. And the minimization of cost function assuming no constraints on future controls results in the projected control increment vector. Hence this adaptive GPC algorithm can be used for either unknown system or varing system parameters, and it is also shown through simulations that the algorithm is robust to the variation of system parameters. This adaptive GPC scheme is shown to have the same stability properties as the deterministic GPC, and requires small amount of calculation compared to other adaptive algorithms which minimize N-stage cost function. Especially, in case that the maximum output horizon is 1, the proposed algorithm can be applicable to direct adaptive GPC.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.463-467
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• 2008

Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.199-206
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• 2004

This paper proposes a hybrid independent component analysis(ICA) of fixed-point(FP) algorithm and robust algorithm. The FP algorithm is applied for improving the analysis speed and performance, and the robust algorithm is applied for preventing performance degradations by means of very small kurtosis and temporal correlations between components. And the adaptive adaptation of temporal correlations has been proposed for solving limits of the conventional robust algorithm dependent on the maximum time delay. The proposed ICA has been applied to the problems for separating the 4-mixed signals of 500 samples and 10-mixed images of $512\times512$pixels, respectively. The experimental results show that the proposed ICA has a characteristics of adaptively adapting the maximum time delay, and has a superior separation performances(speed, rate) to conventional FP-ICA and hybrid ICA of heuristic correlation. Especially, the proposed ICA gives the larger degree of improvement as the problem size increases.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.393-400
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• 2002

Teleoperation enables an operator to manipulate remote objects. One of the main goals in teleoperation researches is to provide the operator with the fueling of the telepresence, being present at the remote site. For these purposes, a master robot must be designed as a bilateral control system that can transmit position/force information to a slave robot and feedback the interaction force. A newly proposed impedance algorithm is applied for the control of a haptic interface that was developed as a master robot. With the movements of the haptic interface for position/force commands, impedance parameters are always varying. When the impedance parameters between an operator and the haptic interface and the dynamic model are known precisely, many model based control theories and methods can be used to control the device accurately. However, due to the parameters'variations and the uncertainty of the dynamic model, it is difficult to control haptic interfaces precisely. This paper presents a robust adaptive impedance control algorithm for haptic interfaces.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.256-267
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• 2017

An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.4
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• pp.55-66
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• 1995

This paper presents an explicit pole-assignment adaptive servocontrol shceme and its application to cutting force regulation for feedrate maximization. The controller structure of the suggested adaptive control scheme is based on robust control theory. This controller structure is then combined with an on-line model estimation algorithm. The whole scheme is applied to a milling process control. The results of real time cutting experimental studies show that the asymptotic regulation of milling peak cutting forces can be achieved with robust- ness against the time varying perturbations to the process model parameters, which are caused by nonlinear cutting dynamics.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.2
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• pp.80-88
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• 2005

In this paper, an robust adaptive backstepping controller is proposed for the speed control of separately excited DC motor with uncertainties and disturbances. Armature and field resistance, damping coefficient and load torque are considered as uncertainties and noise generated at applying load torque to motor is also considered. It shows that the backstepping algorithm can be used to solve the problems of nonlinear system very well and robust controller can be designed without the variation of adaptive law. Simulation and experiment results are provided to demonstrate the effectiveness of the proposed controller.

• Journal of Korea Multimedia Society
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• v.2 no.1
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• pp.99-108
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• 1999

In this paper, a new robust adaptive algorithm is proposed to improve the performance of AEC without computational burden. The proposed adaptive algorithm is based on NLMS algorithm, and its step-size is varied with the reference input signal power and the desired signal power. Its step-size is normalized by the sum of the powers of the reference input signal and the desired signal. When the near-end speaker's speech and noise are applied into the microphone, the step-size becomes small and the misalignment of coefficients are reduced. The convergence speed is comparable to NLMS algorithm at AEC application because the echo signals are attenuated about 10∼20 dBSPL. The characteristics of this algorithm is also analyzed and compared with conventional ones in this paper.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.117-123
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• 2001

To improve control performance of a non-linear system, many other reserches have used the sliding model control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However, this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network. The perturbation estimator based on the fuzzy adaptive network generates the control input of compensating unmodeled dynamics terms and disturbance. And the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluation control performance of the proposed approach, tracking control simulation is carried is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.173-176
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• 2002

This paper describes an implementation of fast face detection algorithm. This algorithm can robustly detect human faces with unknown sizes and positions in complex backgrounds. This paper provides a powerful face detection algorithm using skin color segmenting. Skin Color is modeled by a Gaussian distribution in the HSI color space among different persons within the same race, Oriental. The main feature of the Algorithm is achieved face detection robust to illumination changes and a simple adaptive thresholding technique for skin color segmentation is employed to achieve robust face detection.