• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3118-3120
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• 2000

In this paper. a stable IIR adaptive filter for active noise control is proposed. The IIR filter structure is more effective when acoustic feedback exists, in which case an order of a FIR filter must be very large if some of the poles of the ideal control filter are near the unit circle. But the IIR filter may have stability problems especially when the adaptive algorithm is not converged. A stabilizing procedure for IIR adaptive filter is presented in this paper, and computer simulation is performed to show the effectiveness of proposed schemes.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.296-299
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• 2003

This paper shows a novel and simple IIR/FIR QMF(quadrature mirror filter) filter banks, mixed IIR and FIR structure. Here, FIR filters used for phase compensation. In this paper, we introduced analysis and synthesis filter banks, which used FIR linear phase filters and all pass filters. In result, phase response of analysis and synthesis filter banks become approximately linear characteristic. Simultaneously, a liasing distortion can be completely canceled.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1230-1242
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• 2007

This paper describes an IIR/FIR fusion filter for a nonlinear system, and analyzes the stability of the fusion filter. The fusion filter is applied to INS/GPS integrated system, and the performance is verified by simulation and experiment. In the fusion filter, an IIR-type filter (SPKF) and FIR-type filter (MRHKF filter) are processed independently, then the two filters are merged using the mixing probability calculated using the residuals and residual covariance information of the two filters. The merits of the SPKF and the MRHKF filter are embossed and the demerits of the filters are diminished via the filter fusion. Consequently, the proposed fusion filter has robustness against to model uncertainty, temporary disturbing noise, large initial estimation error, etc. The stability of the fusion filter is verified by showing the closeness of the states of the two sub filters in the mixing/redistribution process and the upper bound of the error covariance matrices. This fusion filter is applied into INS/GPS integrated system, and important factors for filter processing are presented. The performance of the INS/GPS integrated system designed using the fusion filter is verified by simulation under various error environments and is confirmed by experiment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3647-3653
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• 2012

In active noise control filter, IIR filter structure which used for control filter assures the stability property. The stability characteristics of IIR filter structure is mainly determined by pole location of control filter within unit disc, so stable selection of the value of control filter coefficient is very important. In this paper, we proposed novel adaptive stabilized Filtered_U LMS algorithms with IIR filter structure which has better convergence speed and less computational burden than conventional FIR structures, for multi-channel active noise control with vehicle enclosure signal case. For better convergence speed in adaptive algorithms, fuzzy LMS algorithms where convergence coefficient computed by a fuzzy PI type controller was proposed.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1220-1225
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• 2010

This paper proposes a novel active noise control (ANC) approach that uses an IIR filter and neural network techniques to effectively reduce interior noise. We construct a multiple-channel IIR filter module which is a linearly augmented framework with a generic IIR model to generate a primary control signal. A three-layer perceptron neural network is employed for establishing a secondary-path model to represent air channels among noise fields. Since the IIR module and neural network are connected in series, the output of an IIR filter is transferred forward to the neural model to generate a final ANC signal. A gradient descent optimization based learning algorithm is analytically derived for the optimal selection of the ANC parameter vectors. Moreover, re-estimation of partial parameter vectors in the ANC system is proposed for online learning. Lastly, we present the results of a numerical study to test our ANC methodology with realistic interior noise measurement obtained from Korean railway trains.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.955-959
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• 2003

A technique for realizing linear phase IIR filters has been proposed by Powell-Chau which gives a real-time implementation of H(z-1).H(z), where H(z) is a causal nonlinear phase IIR filter. Powell-Chau system is linear but not timeinvariant system. Therefore, that system has group delay response that exhibits a minor sinusoidal variation superimposed on a constant value. In the signal processing, this oscillation seriously degrade the signal quality. Unfortunately, that system has a large sample delay of 4L and also more computational complexity. Proposed system is present a reduced computational complexity technique by moved the numerator polynomial of H(1/z) out to cascade with causal filter H(z) and remain only all-pole of H(1/z), then applied truncated infinite impulse response to finite with truncated IIR filtel $H_L$(z) and L sample delay to subtract the output sequence from the top and bottom filter. Proposed system is linear time invariance and group delay response and total harmonic distortion are also improved.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1700-1705
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• 2012

This study is focusing on ripple elimination in the band pass filter. There are generally two design methods in IIR filter design, which are a direct method and an indirect one. The indirect design method that designs the digital IIR LPF using the prototype analog LPF is applied to this study. A Butterworth filter and a Chebyshev filter are the typical prototype analog LPFs. This study shows characteristics of the digital IIR LPFs that are transformed from the prototype analog LPFs. The designed Butterworth and Chebyshev IIR LPFs are also designed as the band pass filters by frequency transformation in order to compare with the proposed cascading Chebyshev BPFs. This study shows frequency characteristics between the transformed IIR BPFs and the proposed cascading Chebyshev BPFs as well. The proposed cascading Chebyshev BPF is designed by cascading the different orders of Chebyshev BPFs. The aspect of the cascading filter is offsetting the ripples to descend them while the pass band ripples of the Chebyshev filter are ascending and vice versa. The designed cascading Chebyshev filter shows the flatness and the sharpness, which represent the advantages of Butterworth filter in the pass band and of Chebyshev filter in the transition band respectively. This result verifies the validity of the designed filter.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.370-378
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• 2002

Fuzzy inference which combines numerical data and linguistic data has been used to design adaptive filter algorithms. In adaptive IIR filter design, the fuzzy prefilter is taken account, and applied to both direct and lattice structure. As for the fuzzy inference of the fuzzy filter, the Sugeno's method is employed. As membership functions and inference rules are recursively generated through neural network, the accuracy can be improved. The proposed adaptive algorithm, adaptive IIR filter with fuzzy prefilter, has been applied to adaptive system identification for the purposed of performance test. The evaluations have been carried out with viewpoints of convergence property and tracking properties of the parameter estimation. As a result, the faster convergence and the better coefficients tracking performance than those of the conventional algorithm are shown in case of direct structures.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.481-487
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• 2013

This paper presents an FIR (Finite Impulse Response) fixed-interval smoothing filter for fast and exact estimating state variables of a discrete nonlinear system with modeling uncertainty. Conventional IIR (Infinite Impulse Response) filter and smoothing filter can estimate state variables of a system with an exact model when the system is observable. When there is an uncertainty in the system model, however, conventional IIR filter and smoothing filter may cause large errors because the filters cannot estimate the state variables corresponding to the uncertain model exactly. To solve this problem, FIR filters that have fast estimation properties and have robustness to the modeling uncertainty have been developed. However, there is time-delay estimation phenomenon in the FIR filter. The FIR smoothing filter proposed in this paper makes up for the drawbacks of the IIR filter, IIR smoothing filter, and FIR filter. Therefore, the FIR smoothing filter has good estimation performance irrespective of modeling uncertainty. The proposed FIR smoothing filter is applied to the integrated navigation system composed of a magnetic compass based DR (Dead Reckoning) and a GPS (Global Positioning System) receiver. Even when the magnetic compass error that changes largely as the surrounding magnetic field is modeled as a random constant, it is shown that the FIR smoothing filter can estimate the varying magnetic compass error fast and exactly with simulation results.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.15-17
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of nonlinear IIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate nonlinear IIR filter parameter using the genetic algorithm.