• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.608-615
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• 1998

In this paper a second-order iterative learning control algorithm with feedback is proposed for the trajectory-tracking control of nonlinear dynamic systems with unidentified parameters. In contrast to other known methods, the proposed teaming control scheme utilize more than one past error history contained in the trajectories generated at prior iterations, and a feedback term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances or system parameter variations. The convergence proof of the proposed algorithm is given in detail, and the sufficient condition for the convergence of the algorithm is provided. We also discuss the convergence performance of the algorithm when the initial condition at the beginning of each iteration differs from the previous value of the initial condition. The effectiveness of the proposed algorithm is shown by computer simulation result. It is shown that, by adding a feedback term in teaming control algorithm, convergence speed, robustness to disturbances and robustness to unmatched initial conditions can be improved.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.201-208
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• 2006

This paper describes the control philosophy of ESP(Electronic Stability Program) which consists of the stability control the fault diagnosis and the fault tolerant control. Besides the functional performance of the stability control, robustness of control and fault diagnosis is focused to avoid the unnecessary activation of the controller. The look-up tables are mentioned to have the accurate target yaw rate of the vehicle and obtained from vehicle tests for the whole operation range of the steering wheel angle and the vehicle speed. The wheel slip control with a design goal of wheel slip invariance is implemented for the yaw compensation and the target wheel slip is determined by difference between the target yaw rate and actual yaw rate. Since the ESP has a high severity level and the robust control is required, the robustness margin for the stability control is determined according to several uncertainties and the robust fault diagnosis is performed. Both computer simulation and test results are shown in this paper.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.862-867
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• 2021

Although Deep Neural Networks (DNNs) have shown remarkable performance in various artificial intelligence fields, it is well known that DNNs are vulnerable to adversarial attacks. Since adversarial attacks are implemented by adding perturbations onto benign examples, increasing the sparsity of DNNs minimizes the propagation of errors to high-level layers. In this paper, unlike the traditional pruning scheme removing low magnitude weights, we eliminate high magnitude weights that are usually considered high absolute values, named 'reverse pruning' to ensure robustness. By conducting both theoretical and experimental analyses, we observe that reverse pruning ensures the robustness of DNNs. Experimental results show that our reverse pruning outperforms previous work with 29.01% in Top-1 accuracy on perturbed CIFAR-10. However, reverse pruning does not guarantee benign samples. To relax this problem, we further conducted experiments by adding a regularization term for the high magnitude weights. With adding the regularization term, we also applied conventional pruning to ensure the robustness of DNNs.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1254-1261
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• 2003

In this paper we present a simple and efficient robust optimal design formulation and its application to a resonant-type micro probe. The basic idea is to use the Gradient Index (GI) to improve robustness of the objective and constraint functions. In the robust optimal design procedure, a deterministic optimization for performance of MEMS structures is followed by design sensitivity analysis with respect to uncertainties such as fabrication errors and change of operating conditions. During the process of deterministic optimization and sensitivity analysis, dominant performance and uncertain variables are identified to define GI. The GI is incorporated as a term of objective and constraint functions in the robust optimal design formulation to make both performance and robustness improved. While most previous approaches for robust optimal design require statistical information on design variations, the proposed GI based method needs no such information and therefore is cost-efficient and easily applicable to early design stages. For the micro probe example, robust optimums are obtained to satisfy the targets for the measurement sensitivity and they are compared in terms of robustness and production yield with the deterministic optimums through the Monte Carlo simulation.

• Journal of Power Electronics
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• v.18 no.1
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• pp.92-102
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• 2018

A sliding mode control (SMC) for servo motors based on the differential evolution (DE) algorithm, called DE-SMC, is proposed in this study. The parameters of SMC should be designed exactly to improve the robustness, realize the precision positioning, and reduce the steady-state speed error of the servo drive. The main parameters of SMC are optimized using the DE algorithm according to the speed feedback information of the servo motor. The most significant influence factor of the DE algorithm is optimization iteration. A suitable iteration can be achieved by the tested optimization process profile of the main parameters of SMC. Once the parameters of SMC are optimized under a convergent iteration, the system realizes the given performance indices within the shortest time. The experiment indicates that the robustness of the system is improved, and the dynamic and steady performance achieves the given performance indices under a convergent iteration when motor parameters mismatch and load disturbance is added. Moreover, the suitable iteration effectively mitigates the low-speed crawling phenomenon in the system. The correctness and effectiveness of DE-SMC are verified through the experiment.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1249-1255
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• 2004

A new parameter adaptation scheme for RBF Neural Network (NN) has been developed in this paper. Even though the RBF Neural Network (NN) based controllers are robust against both un-modeled dynamics and external disturbances, the performance is not satisfactory for a fast and precise mobile robot. To improve the tracking performance as well as robustness, all the parameters of RBF NN are updated in real time. The stability of this control law is rigorously proved by following the Lyapunov stability theory and shown by the experimental simulations. The fact that all of the weighting factors, width and center of RBF NN have been updated implies that this scheme utilizes all the possibilities in RBF NN to make the controller robust and precise while the mobile robot is following un-known trajectories. The performance of this new algorithm has been compared to the conventional RBF NN controller where some of the parameters are adjusted for robustness.

• Journal of Communications and Networks
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• v.9 no.1
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• pp.34-42
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• 2007

Orthogonal frequency division multiplexing (OFDM) combined with time division multiplexing (TDM), in this paper called OFDM/TDM, can overcome the high peak-to-average-power ratio (PAPR) problem of the conventional OFDM and improve the robustness against long time delays. In this paper, the bit error rate (BER) performance of OFDM/FDM in a frequency-selective Rayleigh fading. channel is evaluated by computer simulation. It is shown that the use of frequency-domain equalization based on minimum mean square error criterion (MMSE-FDE) can significantly improve the BER performance, compared to the conventional OFDM, by exploiting the channel frequency-selectivity while reducing the PAPR or improving the robustness against long time delays. It is also shown that the performance of OFDM/FDM designed to reduce the PAPR can bridge the conventional OFDM and single-carrier (SC) transmission by changing the design parameter.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.59-66
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• 2005

In this paper, a robust stationary Kalman filter is designed by minimizing selected performance indices so that it is less sensitive to uncertainties. The uncertainties include not only stochastic factors such as process noise and measurement noise, but also deterministic factors such as unknown initial estimation error, modeling error and sensing bias. To reduce the effect on the uncertainties, three performance indices that should be minimized are selected based on the quantitative error analysis to both the deterministic and the stochastic uncertainties. The selected indices are the size of the observer gain, the condition number of the observer matrix, and the estimation error variance. The observer gain is obtained by optimally solving the multi-objectives optimization problem that minimizes the indices. The robustness of the proposed filter is demonstrated through the comparison with the standard Kalman filter.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.202-204
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• 2005

This paper is proposed genetic algorithm tuning(GAT) controller for high performance of induction motor drive. We employed GA to the classical PI controller. The approach having ability for global optimization and with good robustness, is expected to overcome some weakness of conventional approaches and to be more acceptable for industrial practices. The control performance of the GAT PI controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3445-3461
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• 2000

Although many different intrusion detectionsystems have been developed there have not been enough researches on the methodology for evaluating these intrusion delection systems. With this understanding,in this paper we present a methodology for evaluating infrusion detection systems from the view point of performance and robustness, both of which are considered the most important criteria Current research on evaluating the performance f intrusion detection systems mostly foduson the in issuse detection but not on the anormaly detection. Regarding evalieting robustness it is not easy to apply off -line methodologies and methods for testing robustness hae not been proposed in on -line methodolomes, In this paper we provide an systematic way of classifyin and generating anomalies and using this reult, present an methodology for evaluating the pertormance of intrusion detection systems in detecting anomaalies ans well as misuses . Moreover, ww study the factors that can damage the robustness of intrusion detection systems and suggest an methodology for assessing the robustness of intrusion detection systems.