• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.67-72
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• 2007

An efficient method for identification of MIMO state space model has been developed by combining partial least squares (PLS) regression, balanced realization, and balanced truncation. In the developed method, a MIMO system is decomposed into multiple MISO systems each of which is represented by a high-order ARX model and the parameters of the ARX models are estimated by PLS. Then, MISO state space models for respective MISO ARX transfer function are found through realization and combined to a MIMO state space model. Finally, a minimal balanced MIMO state space model is obtained through balanced realization and truncation. The proposed method was applied to the design of model predictive control for temperature control of a high pressure $CO_2$ solubility measurement system.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.180-185
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• 2008

A new method for relative error continuous and discrete time model order reduction is proposed. The reduction technique is based on two recently developed methods, namely frequency domain balanced truncation within a frequency bound and inner-outer factorization techniques. The proposed method is of interest for practical model order reduction because in this context it shows to keep the accuracy of the approximation as high as possible without sacrificing the computational efficiency. Numerical results show the accuracy and efficiency enhancement of the method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.11a
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• pp.424-427
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• 1998

입체음향 시스템의 방향감 제어에서 필수적인 머리전달함수(Head-Related Transfer Function)는 일반적으로 FIR 또는 IIR 필터로 구현되며, IIR 필터의 경우 FIR 보다 비교적 저차 모델링이 가능한 장점을 갖는다. 본 논문에서는 Balanced Model Truncation(BMT)를 이용하여 비교적 높은 차수를 가지는 FIR 필터를 IIR 필터로 설계하여 입체음향 시스템의 실시간 구현시 필수적인 계산의 효율을 높이는 방법을 제시하고, 기존의 방법과의 성능의 비교 평가를 하고자 한다.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.366-370
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• 2003

This paper is on weighted model reduction using structurally balanced truncation. For a given weighted(single or double-sided) transfer function, a state space realization with the linear fractional transformation form is obtained. Then we prove that two block diagonal LMI(linear matrix inequality) solutions always exist, and it is possible to get a reduced order model with guaranteed stability and a priori error bound. Finally, two examples are used to show the validity of proposed weighted reduction method, and the method is compared with other existing methods.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.1018-1023
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• 1992

In this paper we proposed the systematic method of reducing the order of controller with robustness. State space formulae for all controllers is found by solving two coupled J-lossless coprime factorizations and model reduction problem. To reduce the order of controller, balanced truncation and Hankel approximation are used.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.57-65
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• 2017

This paper presented a method of designing low-power feedback active noise control filter optimized for headphones/earphones. Using constrained optimization, we obtained a high order FIR noise control filter to ensure reasonable noise attenuation performance at high sampling frequency environment. Then using infinite impulse response (IIR) approximation method called Balanced Model Truncation (BMT), we obtained a low order IIR noise control filter suitable for low-power digital signal processing system like headphones/earphones. For further performance improvement, we utilized frequency warping method so that we could obtain more accurately approximated IIR filter and we ensured system stability by reconstructing the low order IIR filter in form of cascaded second order IIR filters. ANC simulation with white noise and stability test verified that the proposed algorithm had superior attenuation performance and better robustness compared to the conventional algorithm.

• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.305-317
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• 2021

An AMD control system is usually built based on the original model of a target building. As a result, the fact leads a large calculation workload exists. Therefore, the orders of a structural model should be reduced appropriately. Among various model-reduction methods, a suitable reduced-order model is important to high-rise buildings. Meanwhile, a partial structural information is discarded directly in the model-reduction process, which leads to the accuracy reduction of its controller design. In this paper, an optimal technique is selected through comparing several common model-reduction methods. Then, considering the dynamic characteristics of a high-rise building, an improved balanced truncation (BT) method is proposed for establishing its reduced-order model. The abandoned structural information, including natural frequencies, damping ratios and modal information of the original model, is reconsidered. Based on the improved reduced-order model, a new reduced-order controller is designed by a regional pole-placement method. A high-rise building with an AMD system is regarded as an example, in which the energy distribution, the control effects and the control parameters are used as the indexes to analyze the performance of the improved reduced-order controller. To verify its effectiveness, the proposed methodology is also applied to a four-storey experimental frame. The results demonstrate that the new controller has a stable control performance and a relatively short calculation time, which provides good potential for structural vibration control of high-rise buildings.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.486-491
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• 2008

This paper describes a model identification method that has been applied to a commercial 12-inch RTP (rapid thermal processing) equipment with an ultimate aim to develop a high-performance advanced controller. Seven thermocouples are attached on the wafer surface and twelve tungsten-halogen lamp groups are used to heat up the wafer. To obtain a MIMO balanced state space model, multiple SIMO (single-input multiple-output) identification with highorder ARX models have been conducted and the resulting models have been combined, transformed and reduced to a MIMO balanced state space model through a balanced truncation technique. The identification experiments were designed to minimize the wafer warpage and an output linearization block has been proposed for compensation of the nonlinearity from the radiation-dominant heat transfer. As a result from the identification at around 600, 700, and $800^{\circ}C$, respectively, it was found that $y=T(K)^2$ and the state dimension of 80-100 are most desirable. With this choice the root-mean-square value of the one-step-ahead temperature prediction error was found to be in the range of 0.125-0.135 K.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.20-27
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• 1996

In this paper, we propose a coprime factor model reduction method to get a reduced order controller in $H^{\infty}$ mixed sensitivity problem with frequency weighting functions. for this purpose, the given $H^{\infty}$ mixed sensitivity problem is transformed into robust stabilization problem with coprime factor uncertainty of given plant. This method is to define frequency weighted coprime factors of plant in CSD (chain scattering description) form and reduce the coprime factors using weighted balanced truncation. then a controller is designed to the reduced order coprime factors using J-lossless coprime factorization method. Using this approach, the robust stability condition is derived and good performance is preserved in closed loop system with the given plant and the reduced order controller. Also the order of reduced controller for guaranteeing the robust stability can be determined before designing the reduced controller. The proposed method behaves well in both stable and unstable plant.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.340-343
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• 1995

In this paper, we get a reduced order controller in $H^{\infty}$ mixed sensitivity problem with weighting functions. For this purpose, we define frequency weighted coprime factor of plant in $H^{\infty}$ mixed sensitivity problem and reduce the coprime factor using the frequency weighted balanced truncation technique. The we design the controller for plant with reduced order coprime factor using J-lossless coprime factorization technique. Using this approach, we can derive the robust stability condition and achieve good performance preservation in the closed loop system with reduced order controller. And it behaves well in both stable plant and unstable plant.t.