• MALSORI
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• v.55
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• pp.131-142
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• 2005

A mismatch between the training and the test conditions often causes a drastic decrease in the performance of the speech recognition systems. In this paper, non-linear transformation techniques based on histogram equalization in the acoustic feature space are studied for reducing the mismatched condition. The purpose of histogram equalization(HEQ) is to convert the probability distribution of test speech into the probability distribution of training speech. While conventional histogram equalization methods consider only the probability distribution of a test speech, for noise-corrupted test speech, its probability distribution is also distorted. The transformation function obtained by this distorted probability distribution maybe bring about miss-transformation of feature vectors, and this causes the performance of histogram equalization to decrease. Therefore, this paper proposes a new method of calculating noise-removed probability distribution by using assumption that the CDF of noisy speech feature vectors consists of component of speech feature vectors and component of noise feature vectors, and this compensated probability distribution is used in HEQ process. In the AURORA-2 framework, the proposed method reduced the error rate by over $44\%$ in clean training condition compared to the baseline system. For multi training condition, the proposed methods are also better than the baseline system.

• Journal of Ocean Engineering and Technology
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• v.11 no.4
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• pp.169-179
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• 1997

This paper presents a discrete-time sliding mode control of an autonomous underwater vehicle with parameter uncertainties and long sample interval based on discrete variable structure system. Although conventional sliding mode montrol techniques are robust to system uncertainties, in the case of the system with long sample interval, the sliding control system reveals chattering phenomenon and even makes the system unstable. This paper considers the AUV which acquires position informations from a surface ship through an acoustic telemetry system with a certain discrete interval. The control system is designed on the basis of a Lyapunov function and a sufficient condition of the switching gain to make the system stable is give. Each component of the switching gain can be determined separately one another. The controller is robust to the uncertainties, and reaching condition of the control system is satisfied for any initial condition. This control law is a generalized form of the discrete sliding mode control and reduce the chattering problem considerably. Motion control of the AUV in the vertical plane shows the effectiveness of the proposed technique.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.269-277
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• 2008

This paper proposes a necessary and sufficient condition of convergence in the $L_2$-norm sense for a feedback-based iterative learning control (ILC) system including a multi-input multi-output (MIMO) linear time-invariant (LTI) plant. It is shown that the convergence conditions for a nominal plant and an uncertain plant are equal to the nominal performance condition and the robust performance condition in the feedback control theory, respectively. Moreover, no additional effort is required to design an iterative learning controller because the performance weighting matrix is used as an iterative learning controller. By proving that the least upper bound of the $L_2$-norm of the remaining tracking error is less than that of the initial tracking error, this paper shows that the iterative learning controller combined with the feedback controller is more effective to reduce the tracking error than only the feedback controller. The validity of the proposed method is verified through computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.293-296
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• 1997

Robust stability conditions for discrete-time variable structure control is proposed. Conventionally the discrete-time variable structure control method with a variable structure uncertainty compensator approach requires a bounded changing rate of the uncertainties to ensure robust stability. However, when uncertainties vary as a function of state variables, which occur with parametric uncertainties, it is not reasonable to assume a bounded variation on the uncertainties. In this paper, uncertainties are assumed to consist of exogenous disturbances and parametric uncertainties. An uncertainty compensator is used to deal with the former, and a robust stability condition is derived using Small Gain Theorem for the latter.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.19-27
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• 2003

It is well known that one can easily design a high-gain adaptive output feedback control for a class of nonlinear systems which satisfy a certain condition called output feedback exponential passivity (OFEP). The designed high-gain adaptive controller has simple structure and high robustness with regard to bounded disturbances and unknown order of the controlled system. However, from the viewpoint of practical application, it is important to consider a robust control scheme for controlled systems for which some of the assumptions of output feedback stabilization are not valid. In this paper, we design a robust high-gain adaptive output feedback control for the OFEP nonlinear systems with uncertain nonlinearities and/or disturbances. The effectiveness of the proposed method is shown by numerical simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.107-112
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• 2003

In general, the dynamics of autonomous underwater vehicles(AUVs) are highly nonlinear and time-varying, and the hydrodynamic coefficients of vehicles are hard to estimate accurately because of the variations of these coefficients with different navigation conditions. For this reason, in this paper, the control gain function is assumed to be unknown and the exogenous input term is assumed to be unbounded, although it still satisfies certain restrict condition. And these two kinds of wild assumptions have been seldom handled simultaneously in one system because of the difficulty of stability analysis. Under the above two relaxed assumptions, a robust neural network control scheme is presented for autonomous diving control of an AUV, and can guarantee that all the signals in the closed-loop system are UUB (uniformly ultimately bounded). Some practical features of the proposed control law are also discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.255-257
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• 2009

The robust stabilization problem of a multivariable uncertain system with a polytopic model is considered. A PI-type $H_{\infty}$ controller with a low pass filter is used for robust stabilization and noise rejection. The problem is reduced to an LMI optimization problem. A sufficient condition for the existence of the PI controller is derived in terms of LMIs. The PI gain matrices are parameterized by using the solution matrices to the existence conditions. Finally, a numerical design example is given.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.785-791
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• 2008

This paper considers the problem of robust $H_{\infty}$ control for uncertain 2-D discrete systems in the General Model via output feedback controllers. The parameter uncertainty is assumed to be norm-bounded. The purpose is the design of output feedback controllers such that the closed-loop system is stable while satisfying a prescribed $H_{\infty}$ performance level. In terms of a linear matrix inequality, a sufficient condition for the solvability of the problem is obtained, and an explicit expression of desired output feedback controllers is given. An example is provided to demonstrate the application of the proposed method.

• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.1-6
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• 2017

The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appropriate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the management and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we consider the estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposed for an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.391-395
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• 1990

A new control algorithm is developed to achieve the robust performance of the system during the overall control process. Time-varying sliding manifolds are proposed to remove the reaching phase which is one of common shortcomings of variable structure control scheme. A necessary and sufficient condition for the existence of a sliding mode on the newly proposed time-varying sliding mode on the newly proposed time-varying sliding manifolds is derived by Lyapunov's second method. The digital simulation results show that the newly proposed control algorithm is superior to the typical variable structure control algorithm with respect to the robust performance of the system. The simplicity of the proposed control algorithm encourages control engineers to implement the proposed control algorithm in many control problems.