• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.454-456
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• 1998

Traditional adaptive control algorithms are not robust to dynamic uncertainties. The adaptive control algorithms developed previously to deal with dynamic uncertainties do not facilitate quantitative design. We proposed a new robust adaptive control algorithms consists of an $H^{\infty}$ suboptimal control law and a robust parameter estimator. Numerical examples showing the effectiveness of the $H^{\infty}$ adaptive scheme are provided.

• Transactions on Control, Automation and Systems Engineering
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• 제3권1호
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• pp.15-20
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• 2001

In this paper, adaptive algorithms on the sliding model control for robust tracking control of robust manipulators are presented. The presented algorithms use adaption laws for tuning both the sliding mode gain and the thickness of the boundary layer to reject a disconitnuous control input, and to improve the tracking performance. It is shown that the robustness of the developed adaptive algorithms are guaranteed by the sliding mode control law and that the algorithms are globally convergent in the presence of disturbances and modeling uncertainties. Computer simulations are performed for a two-link manipulator, and the results show good properties of the proposed adaptive algorithms under large mainpulator parameter uncertainties and disturbances.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(4)
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• pp.191-194
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• 2000

Frequency domain adaptive filter is effective to communication fields of many computational requirements. In this paper we propose a new variable step size algorithms which improves the convergence speed and reduces computational complexity for frequency domain adaptive filter. we compared MSE of the proposed algorithms with one of normalized FLMS using computer simulation of adaptive noise canceler based on synthesis speech.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 춘계학술대회 논문집
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• pp.419-423
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• 1996

These days, industrial robots are required to have high speed and high precision in doing various tasks. Recently, the adaptive control algorithms for those nonlinear robots have been developed. With spatial vector space, these adaptive algorithms including recursive implementation are simply described. Without sensing joint acceleration and computing the inversion of inertia matrix, these algorithms which include P.D. terms and feedforward terms have global tracking convergence. In this paper, the feasibility of the proposed control method is illustrated by applying to 2 DOF SCARA robot in DSP(Digital Signal Processing).

• 한국경영과학회지
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• 제32권4호
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• pp.1-18
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• 2007

In this paper, we develop an adaptive genetic algorithm (aGA). The aGA has an adaptive scheme which can automatically determine the use of local search technique and adaptively regulate the rates of crossover and mutation operations during its search process. For the adaptive scheme, the ratio of degree of dispersion resulting from the various fitness values of the populations at continuous two generations is considered. For the local search technique, an improved iterative hill climbing method is used and incorporated into genetic algorithm (GA) loop. In order to demonstrate the efficiency of the aGA, i) a canonical GA without any adaptive scheme and ii) several conventional aGAs with various adaptive schemes are also presented. These algorithms, including the aGA, are tested and analyzed each other using various test problems. Numerical results by various measures of performance show that the proposed aGA outperforms the conventional algorithms.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권1호
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• pp.29-35
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• 2012

This paper introduces an adaptive control method of strong mutation rate and probability for queen-bee genetic algorithms. Although the queen-bee genetic algorithms have shown good performances, it had a critical problem that the strong mutation rate and probability should be selected by a trial and error method empirically. In order to solve this problem, we employed the measure of convergence and used it as a control parameter of those. Experimental results with four function optimization problems showed that our method was similar to or sometimes superior to the best result of empirical selections. This indicates that our method is very useful to practical optimization problems because it does not need time consuming trials.

• 한국통신학회논문지
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• 제35권6C호
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• pp.506-515
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• 2010

본 논문에서는 실내 환경에서 대표적인 적응 MIMO 알고리즘들의 스펙트럼 효율을 비교, 분석한다. 먼저, MIMO 알고리즘들의 변복조 기법을 간략히 고찰하고 BPSK 와 256QAM 까지의 정방형 QAM 변조방식을 이용하여 적응 알고리즘을 구성한다. 끝으로, 실내 전파환경에서 (2$\times$2) 혹은 (4$\times$4) 안테나 어레이로 구성되는 적응 MIMO 알고리즘들의 스펙트럼 효율을 시뮬레이션을 통해 비교, 분석한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.681-684
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• 2000

Gradient-type algorithms for adaptive IIR notch filters are very attractive in terms of both performances and computational requirements. Generally, it is quite difficult to assess their performances analytically. There have been several trials to analyze such adaptive algorithms as the sign and the plain gradient algorithms for some types of adaptive IIR notch filters, but many of them still remain unexplored. Furthermore, analysis techniques used in those trials can not be directly applied to different types of adaptive IIR notch filters. This paper presents a detailed performance analysis of the sign algorithm for a well-known adaptive IIR notch filter with constrained poles and zeros, which can not be done by just applying the related existing analysis techniques, and therefore has not been attempted yet. The steady-state estimation error and mean square error (MSE) of the algorithm are derived in closed forms. Stability bounds of the algorithm are also assessed. extensive simulations are conducted to support the analytical findings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3095-3111
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• 2018

Spectrum sensing (SS) is one of the fundamental tasks for cognitive radio. In SS, decisions can be made via comparing the test statistics with a threshold. Conventional adaptive algorithms for SS usually adjust their thresholds according to the radio environment. This paper concentrates on the issue of adaptive SS whose threshold is adjusted based on the Markovian behavior of primary user (PU). Moreover, Bayesian cost is adopted as the performance metric to achieve a trade-off between false alarm and missed detection probabilities. Two novel adaptive algorithms, including Markov Bayesian energy detection (MBED) algorithm and IMBED (improved MBED) algorithm, are proposed. Both algorithms model the behavior of PU as a two-state Markov process, with which their thresholds are adaptively adjusted according to the detection results at previous slots. Compared with the existing Bayesian energy detection (BED) algorithm, MBED algorithm can achieve lower Bayesian cost, especially in high signal-to-noise ratio (SNR) regime. Furthermore, it has the advantage of low computational complexity. IMBED algorithm is proposed to alleviate the side effects of detection errors at previous slots. It can reduce Bayesian cost more significantly and in a wider SNR region. Simulation results are provided to illustrate the effectiveness and efficiencies of both algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.264-267
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• 1988

One of the major reasons of Adaptive Control is to control time varying systems. In this paper new adaptive algorithms are suggested for a class of linear time varying systems that satisfy certain assumptions. These algorithms consist of three modules, modeling, parameter estimation and control. The key feature of this paper is that power series of time varying parameters are used for estimation.