• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.307-316
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• 2007

In real system application, the interacting multiple model (IMM) based algorithm operates with the following problems: it requires less computing resources as well as a good performance with respect to the various target maneuvering, it requires a robust performance with respect to the time-varying measurement noise, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an adaptive fuzzy interacting multiple model (AFIMM) algorithm, which is based on the basis sub-models defined by considering the maneuvering property and the time-varying mode transition probabilities designed by using the mode probabilities as the inputs of the fuzzy decision maker whose widths are adjusted, is proposed. To verify the performance of the proposed algorithm, a radar target tracking is performed. Simulation results show that the proposed AFIMM algorithm solves all problems in the real system application of the IMM based algorithm.

• The Journal of the Acoustical Society of Korea
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• v.23 no.2E
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• pp.40-43
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• 2004

In moving jammer environments, the performance of conventional adaptive beamformer is severely degraded and the robust adaptive beamformer requires additional sensors to obtain desired performances. Therefore, it is necessary to develop efficient algorithm without any additional requirement of the number of sensors, etc. In this paper, we introduce a fast adaptive algorithm with variable forgetting factor, which does not have any additional requirements. From the computer simulations, we obtain the better performances than those of other techniques for the arrays with various aperture lengths.

• Smart Structures and Systems
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• v.15 no.3
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4C
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• pp.384-391
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• 2003

Blind channel identification and equalization attempt to identify the communication channel and to remove the inter-symbol interference caused by a communication channel without using any known trainning sequences. In this paper, we propose a blind adaptive channel identification and equalization algorithm with phase offset compensation for single-input multiple-output (SIMO) channel. It is based on the one-step forward multichannel linear prediction error method and can be implemented by an RLS algorithm. Phase offset problem, we use a blind adaptive algorithm called the constant modulus derotator (CMD) algorithm based on condtant modulus algorithm (CMA). Moreover, unlike many known subspace (SS) methods or cross relation (CR) methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch.

• Journal of the Korean Society of Industry Convergence
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• v.2 no.2
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• pp.91-102
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• 1999

The paper describes a robust adaptive control algorithm for induction motor drive without speed sensor at low speed range. The control algorithm use only current sensors in a space vector pulse width modulation within loop control with rotor speed estimation and voltage source inverter. On-line rotor speed estimation is based on utilizing parallel model reference adaptive control system. MRAC of the modified flux model for flux and rotor speed estimator uses dual-adaptation mechanism, ${\omega}_r$ and ${\omega}_e$ scheme. The estimated flux components in the model can be compensated from the effects of offset errors on pure integrals. It can be compensated to the parameter variations and torque fluctuation with speed estimation in less then 10 rad/sec. In a simulation, the proposed induction motor control algorithm without speed sensor at very low speed range are shown to operate very well in spite of variable rotor time constant and fluctuating load without change the controller parameters. The suggested control strategy and estimation method have been validated by simulation study, and it proposed the designed system for the implementation using TI320C31 DSP/ASIC controller.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.187-190
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• 2003

In this paper, we address the robust adaptive backstepping controller using fuzzy neural network (FHIN) for a class of uncertain output feedback nonlinear systems with disturbance. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The state estimation is solved using K-fillers. All unknown nonlinear functions are approximated by FNN. The FNN weight adaptation rule is derived from Lyapunov stability analysis and guarantees that the adapted weight error and tracking error are bounded. The compensated controller is designed to compensate the FNN approximation error and external disturbance. Finally, simulation results show that the proposed controller can achieve favorable tracking performance and robustness with regard to unknown function and external disturbance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.7
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• pp.1414-1420
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• 1994

In this paper the existence of chaotic signal is probed in adaptive dead beat control law for nonlinear dynamic system. These chaotic signal makes the system unstable and difficult to control, but it broaden the range of application, confirms the robustness of system and gives a lot of information. Considering of low correlation between chaotic signals, robust adaptive control method which uses for parameter estimation is proposed. With this algorithm the parameters converges stable rapidly. Finally the superiority of it is proved by computer simulation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.3
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• pp.291-300
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• 1990

This paper presents a robust discrete-time direct adaptive pole-placement with new discrete parameter adaptation algorithm (PAA), the standard RLS is suitably modified by adding a term which is exponentially proportional to the filtered tracking error and using a signal normalization. It is shown that it makes the overall adaptive system more robust in the presence of disturbances or unmodeled dynamics. In order to discuss the robustness improvement by using the input-output stability theory, the overall adaptive control system is reformulated and the sector theory is applied. In addition, computer simulation results are presented to complement the theoretical development.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.6
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• pp.143-157
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• 1994

This paper is concerned with the position control of electrohydraulic servo system under parameter variation. An adaptive sliding mode control which uses the direct parameter estimation scheme, is proposed to design a robust controller for fast and accurate control of the system. It is shown that the adaptive sliding mode control algorithm is robust and effective in attaining fast and accurate position control of system under time-dependent parameter variation. It is also shown experimentally that chattering phenomena in a sliding mode control can significantly be reduced by using boundary layer technique, and that new approach in sliding mode control introducing a term proportional to the distance between the current state and the sliding surface in the control law is effective to obtain fast response and to increase stability of the system. Computer simulation on the dynamic performance of the control system is also presented.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.179-182
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• 2002

The development of noise robust speech processing systems is becoming increasingly important as speech technology is currently widely applied in real world applications. Recently, to resolve such a noise problem, adaptive noise canceller(ANC) is frequently used, which is based upon adaptive filters. The adaptive recursive filters perform better than adaptive non-recursive filters due to the added poles, but the stability may be severely threatened. But these problems of adaptive recursive filters was solved by ACHARF algorithm. This paper presents a method which combines speaker verification system with ANC(Adaptive Noise Canceller) using the ACHARF algorithm. In the front-end stage, ANC is adopted to suppress the additive noise imposed on the speech signal. The results show that the performance of speaker verification system becomes better than before.