• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.652-656
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• 2011

In this paper, a transmitter-based multipath processing and inter-channel interference (ICI) cancellation scheme for a ultra-wideband (UWB) spatial multiplexing (SM) multiple input multiple output (MIMO) system is presented. It consists of taps delayed lines and zero-forcing (ZF) filters in the transmitter and correlators in the receiver. For a UWB SM MIMO system with N transmit antennas, M receive antennas, and Q resolvable multipath components, the BER performance of a linear transmit ZF scheme is analyzed in a log-normal fading channel and also compared with that of a receiver-based ICI rejection approach. It is found that when M ${\leq}$ N, the transmit ZF processing approach outperforms the ZF receiver while making the mobile units low-cost and low-power.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2807-2810
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• 2003

In this paper, we propose a fuzzy control method for improving the control performance by automatically tuning the scaling factor. The proposed method is that automatically tune the input scaling factor and the output scaling factor of fuzzy logic system through neural network. Used neural network is ADALINE (ADAptive Linear NEron) neural network with delayed input. ADALINE neural network has simple construct, superior learning capacity and small computation time. In order to verify the effectiveness of the proposed control method, we performed simulation. The results showed that the proposed control method improves considerably on the environment of the disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.356-361
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• 1986

This paper presents robustness properties of LQ regulators for input-delayed systems. Using frequency-domain representations, the Kalman inequality concerning the return-difference matrix is derived. The stability margins of LQ regulators are investigated using the Kalman inequality when the open-loop system is stable. In order to obtain stability margins a upper bound of the solution of LQ Riccati equations is derived. Finally, the LQG/LTR method to improve the robustness of LQG regulators is obtained and illustrated with an example.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.98-103
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• 2000

This paper is concerned with a delay-dependent sliding mode scheme for the robust stabilization of input-delay systems with bounded unknown uncertainties. A sliding surface based ona predictor is proposed to minimize the effect of the input delay. Then, a robust control law is derived to ensure the existence of a sliding mode on the surface. In input-delay systems, uncertainties given during te delayed time are not directly controlled by the switching control because of causality prolem of them. They can influence the stability of the system in the sliding mode. Hence, a delay-dependent stability analysis for reduced order dynamics is employed to estimate maximum delay bound such that the system is globally asymptotically stable in the sliding mode. A numerical example is given to illustrate the design procedure.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.2004-2007
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• 2002

In the linear combination type stereophonic echo canceller, it is known not to converge the coefficient vector of the adaptive filter to a correct echo path. In this report, we analyze the convergence value of the filter coefficient vector of the stereo echo canceling algorithm using input signals of all channels in relation to this problem. In this analysis, one of the two inputs to the un-known system and adaptive one are assumed to be a delayed and attenuated version of the other signal as a model of the input signal with a strong cross-correlation. As a result, it is shown for the coefficient vectors not to converge to echo paths, and nor to converge to the value which depends on the time delay and the attenuation of the input signal. We show that the computer simulation result are corresponding to our analytical results.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.2
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• pp.102-109
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• 2000

This paper deals with an observer-based preview control for semi-active suspensions. An Observer has been designed such that all the state variables and road inputs can be estimated from accelerations. Since the road input to the rear wheels is a delayed version of that to the front wheels, it can be obtained by estimating the road input to the front wheels without preview the estimated values of state and the estimated road input has been investigated. The results show that the observer-based control can provide good performance. The observer-based preview control improves the dynamic behavior of the rear axle and that of pitch motion compared to the LQ optimal control.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.11a
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• pp.573-577
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• 2002

A new estimated neural networks are proposed in order to measure nonlinear road environments in realtime. This new neural networks is Error Estimated Neural Networks. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we control 7 degree simulation, this controller and driver were proved to be effective to drive a car in the environments of nonlinear road systems.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2949-2952
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• 2000

A new neural networks and learning algorithm are proposed in order to measure nonlinear heights of complexed road environments in realtime without pre-information. This new neural networks is Error Self Recurrent Neural Networks(ESRN), The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by back-propagation and each weights are updated by RLS(Recursive Least Square). Consequently. this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by ESRN and learning algorithm and control nonlinear models. To show the performance of this one. we control 7 degree of freedom full car model with several control method. From this simulation. this estimation and controller were proved to be effective to the measurements of nonlinear road environment systems.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.239-242
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• 1998

Most of time delay existing in industrial processes can be modeled as an input time-delay which causes a lot of difficulties. To implement a time-delay free control, loop transfer function is recovered through the direct state feedback. For the proper state feedback, and observer is designed based upon the factorization approach which is known as one of the most powerful tool for the design of various control systems. The design scheme is parctically applied for the control of a DC motro which suffers from input time-delay. Simulational results demonstrate that this new algorithm guarantess the stability of the time-delayed system, as well as performance improvement compared to the conventional PI control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.140-143
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• 2001

In this paper, a control problem of the Takagi-Sugeno(TS) fuzzy system with time-varying input delay is considered. It is well known that the delay is one of the major sources responsible for the instability of the controlled system. A systematic design technique is developed based on the Lyapunov-Razumikhin stability theorem. A sufficient condition for the global asymptotic stability of the TS fuzzy systems is formulated in terms of linear matrix inequalities (LMIs). The derived condition can deal with any time-varying input delay within the admissible bound. The effectiveness of the proposed controller design technique is demonstrated by a numerical simulation.