• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.2 s.173
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• pp.290-302
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• 2000

In this paper, we study on the identification of discrete-time state-space model for robust control of tandem cold mill, using a MOESP(MIMO output-error state-space model identification) algorithm based on subspace method. It is shown that the identified model is well adapted to input-output data sets, which are obtained from nonlinear mathematical equations of tandem cold mill. Furthermore, deterministic H$\infty$ norm bounds on uncertainties including modeling errors and disturbances are quantitatively identified in the frequency domain. Finally, the results give a basic idea to determine weighting functions included in formulating some robust control problems of tandem cold mill.

• International Journal of Automotive Technology
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• v.8 no.3
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• pp.351-359
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• 2007

In this study, a hierarchical switching control scheme based on robust control theory is proposed for tracking control of vehicle longitudinal acceleration in the presence of large uncertainties. A model set consisting of four multiplicative-uncertainty models is set up, and its corresponding controller set is designed by the LMI approach, which can ensures the robust performance of the closed loop system under arbitray switching. Based on the model set and the controller set, a switching index function by estimating the system gain of the uncertainties between the plant and the nominal model is designed to determine when and which controller should be switched into the closed loop. After theoretical analyses, experiments have also been carried out to validate the proposed control algorithm. The results show that the control system has good performance of robust stability and tracking ability in the presence of large uncertainties. The response time is smaller than 1.5s and the max tracking error is about $0.05\;m/S^2$ with the step input.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.85-90
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• 2003

This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameters of PID controller are adapted using the error. The parameters of TSK fuzzy model also adapted to plant. The proposed algorithm allows designing adaptive PID controller which Is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

• Nuclear Engineering and Technology
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• v.30 no.3
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• pp.235-244
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• 1998

The parametric model method determines the accident source term which is Presented by a set of source term parameters. In this method, the cumulative distribution of each source term parameter should be derived for its uncertainty analysis. This paper introduces a method of generating the parameters in the form of cumulative distribution using MAAP version 4.0. In MAAP, there are model parameters which could incorporate uncertain physical and/or chemical phenomena. In general, the model parameters do not have a point value but a range. In this paper, considering that, the input values of model parameters influencing each parameter are sampled using LHS. Then, the computation results are shown in cumulative distribution form. For a case study, the CDFs of FCOR and WES of Kori Unit 1 are derived. The target scenarios for the computation are the ones whose initial events are large LOCA, small LOCA and transient, respectively. It is found that the computed CDF's in this study are consistent to those of NUREG-1150 and the use of MAAP is proven to be adequate in assessing the parameters of the severe accident source term.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.331-338
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• 2022

Face stability analyses provides the most probable failure mechanisms and the understanding about parameters that need to be considered for the evaluation of ground movements caused by tunneling. After the Upper Bound Method (UBM) solution which can consider the influence of seepage forces and depth-dependent effective cohesion is verified with the numerical experiments, the probabilistic model is proposed to calculate the unbiased limiting tunnel collapse pressure. A reliability analysis of a shallow circular tunnel driven by a pressurized shield in a frictional and cohesive soil is presented to consider the inherent uncertainty in the input parameters and the proposed model. The probability of failure that exceeding a specified applied pressure at the tunnel face is estimated. Sensitivity and importance measures are computed to identify the key parameters and random variables in the model.

• Magazine of the Korea Concrete Institute
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• v.10 no.6
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• pp.149-159
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• 1998

The reasonable prediction of time-dependent deformation of prestressed concrete(PSC) box girder bridges is very important for accurate construction as well as good serviceability. The long-term behavior is mostly influenced by the probabilistic characteristic of creep and shrinkage. This paper presents a method of statistical analysis and sensitivity analysis of creep and shrinkage effects in PSC box been taken into account - model uncertainty, parameter variation and environmental condition. The statistical and sensitivity analyses are performed by using the numerical simulation of Latin Hypercube sampling. For each sample, the time-dependent structural analysis is performed to produce response data, which are then statistically analyzed. The probabilistic prediction of the confidence limits on long-term effects of creep and shrinkage is then expressed. Three measure are examined to quantify the sensitivity of the outputs of each of the input variables. These are rank correlation coefficient(RCC), partical rank correlation coefficient(PRCC) and standardiozed rank regression coefficient(SRRC) computed on the ranks of the observations. Three creep and shrinkage models - i. e., ACI model. CEB-FIP model and the model in Korea Highway Bridge Specification - are studied. The creep model uncertainy factor and the relative humidity appear to be the most dominant factors with regard to the model output uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.502-502
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• 2000

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. For constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time Instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an of set error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1597-1600
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• 2010

In this paper, a direct controller for nonlinear plants using a neural network is presented. The composed of the control input by using RBF neural networks and auxiliary input to compensate for effects of the approximation errors and disturbances. In the results, using this scheme, the output tracking error between the plant and the reference model can asymptotically converge to zero in the presence of bounded disturbances and approximation errors. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.599-605
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• 2014

This paper presents the interactive behavior modeling method based on POMDP (Partially Observable Markov Decision Process) for HRI (Human-Robot Interaction). HRI seems similar to conversational interaction in point of interaction between human and a robot. The POMDP has been popularly used in conversational interaction system. The POMDP can efficiently handle uncertainty of observable variables in conversational interaction system. In this paper, the input variables of the proposed conversational HRI system in POMDP are the input information of sensors and the log of used service. The output variables of system are the name of robot behaviors. The robot behavior presents the motion occurred from LED, LCD, Motor, sound. The suggested conversational POMDP-based HRI system was applied to an emotional robot KIBOT. In the result of human-KIBOT interaction, this system shows the flexible robot behavior in real world.