• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.534-536
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• 1999

Fuzzy Systems which are based on membership functions and rules, can control nonlinear, uncertain, complex systems well. However, Fuzzy logic controller(FLC) has problems; It is difficult to design the stable FLC and FLC depends mainly on individual experience. Although FLC can be designed using the error back-propagation algorithm, it takes long time to converge into global, optimal parameters. Well-developed linear system theory should not be replaced by FLC, but instead, it should be suitably used with FLC. A new methodology is introduced for designing THEN-PART membership functions of FLC based on its well-tuned state feedback controller. A example of inverted pendulum is given for demonstration of the robustness of proposed methodology.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.10
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• pp.60-67
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• 1990

This paper proposes a model-based fault detection method for linear/nonlinear system having modelling errors, nonlinearities and measurement noise. The system model is represented by the unified operator [5] in order to apply to both the continuous-time and discrete-time problems. The fault detection method suggested here accounts for the effects of noise, model mismatch and nonlinearities. Modelling errors are depicted by additive forms and the nominal model denominator is fixed via prior experiments in order to quantify the nucertainty bound on the parameter estima-tion. The least square method is used to estimate the numerator parameters of the nominal model. performance than traditional methods.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.3
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• pp.73-82
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• 1992

An industrial robot, installed real manufacturing processes an element of the system autmation, can be considered as an uncertain system due to dynamic uncertainties in inertial parameters and varying payloads. Most difficuties in controlling a robot manipulator are caused by the fact that the dynamic equations describing the motions of the manipulator are inherently nonlinear and heavily coupled effects between joints and associated links. Existing robot conrol systems have constant predefined gains and do not cover the complex dynamic interactions between manipulator joints. As a result, the manipulator is severly limited in range of application, speed of operation and variation of payload. The proposed controller is operated by adjusting its gains based on the response of the manipulator in such a way that the manipulator closely matches the reference model trajectories defined by the desinger. The proposed manipulator studied has two loops, an inner loop of model reference adaptive controller and an outer loop of state feedback controller with integral action to guarantee the stability of the adaptive scheme. This adaptation algorithm is based on the hyperstailiy approach with an improved Lyapunov function. The coupling among joints and the nonlinearity in the dynamic equation are explicitly considered. The designed manipulator controller shows good tracking performance in practical working environment, various load variations and parameter uncertainties.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.6
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• pp.77-82
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• 2008

This paper presents a Reliability-Based Topology Optimization(RBTO) using the Evolutionary Structural Optimization(ESO). An actual design involves some uncertain conditions such as material property, operational load and dimensional variation. The Deterministic Topology Optimization(DTO) is obtained without considering the uncertainties related to the uncertainty parameters. However, the RBTO can consider the uncertainty variables because it has the probabilistic constraints. In order to determine whether the probabilistic constraints are satisfied or not, simulation techniques and approximation methods are developed. In this paper, the reliability index approach(RIA) is adopted to evaluate the probabilistic constraints. In order to apply the ESO method to the RBTO, sensitivity number is defined as the change in the reliability index due to the removal of the ith element. Numerical examples are presented to compare the DTO with the RBTO.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.275-282
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• 2006

System algorithms estimated by deterministic input may occur the error between predicted and actual output. Especially, actual system can't predict the exact outputs due to uncertainty and tolernce of input parameters. A single output to a set of inputs has a limited value without the variation. Hence, we should consider various scatters caused by the load assessment, material characteristics, stress analysis and manufacturing methods in order to perform the robust design or etimate the reliability of structure. The system design with uncertainty should perform the probabilistic structural optimization with the statistical response and the reliability. This method calculated the probability distributions of the characteristics such as stress by combining stress analysis, response surface methodology and Monte Carlo simulation and got the probabilistic sensitivity. The sensitivity of structural response with respect to in constant design variables was estimated by fracture probability. Therefore, this paper proposed the probabilistic reliability design method for fracture of uncorved freight end beam and the design criteria by fracture probability.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1992.10a
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• pp.117-123
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• 1992

In recent years, the finite element method has become one of the most popular numerical technique for obtaining solutions of engineering science problems. However, there exist various uncertainties in modeling the problems, such as the dimensions(geometry shape), the material properties, boundary conditions, etc. The consideration for the uncertainties inherent in the problems can be made by understanding the influences of uncertain parameters[1]. Determining the influences of uncertainties as statistical quantities using the standard finite element method requires enormous computing time, while the probabilistic finite element method is realized as an efficient scheme[2,3] yielding statistical solution with just a few direct computations. In this paper, a formulation of the probabilistic fluid-structure interaction problem accounting for the first order perturbation of geometric shape is derived, and especially probabilistical acoustic pressure scattering from the structure with surrounding fluid is focused on. In Section 2, governing equations for the fluid-structure problems are given. In Section 3, a finite element formulation, based on the functional, is presented. First order perturbation of geometric shape with randomness is incorporated into the finite element formulation in conjunction with discretization of the random fields in Section 4 and 5. Finally, the proposed formulation is applied to a acoustic pressure scattering problem from an infinitely long cylindrical shell structure with randomness of radial perturbation.

• Journal of the Korean Society for Precision Engineering
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• v.7 no.4
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• pp.85-102
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• 1990

Up to now, most robot control systems are very naive. They consist of a number of independent position-servo loops to control each joint angle separately. Those control systems have constant predefined gains and do not cover the complex dynamic interactions between manipulator joints. As a result, the manipulator is severely limited in range of application, speed of operation and variation of payload. This study proposed a new method to design a robot manipulator controller capable of tracking the reference trajectories of joint angles in a reasonable accuracy to cope with actual situations of varying payload, uncertain parameters. The adaptive model following control method has been used to improve existing robot manipulator controllers. The proposed controller is operated by adjusting its gains based on the response of the manipulator in such a way that the manipulator closely matches the reference model trajectories defined by the designer. The stability of adaptive controller is based on the Second Method of Lyapunov. The coupling among joints and the nonlinearity in the dynamic equation are explicitly considered. The designed manipulator controller shows good tracking performance under various load varia- tion and parameter uncertainties.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.34-39
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• 2015

In this paper, a finite impulse response (FIR) filter is proposed for discrete-time nonlinear systems. The proposed filter is designed by combining the estimate of the perturbation state and nominal state. The perturbation state is estimated by adapting the optimal time-varying FIR filter for the linearized perturbation model and the nominal state is directly obtained from the nonlinear nominal trajectory model. Since the FIR structured estimators use the finite horizon information on the most recent time interval, the proposed extended FIR filter satisfies the bounded input/bounded output (BIBO) stability, which can't be obtained from infinite impulse response (IIR) estimators. Thus, it can be expected that the proposed extended FIR filter is more robust than IIR structured estimators such as an extended Kalman filter for the round-of errors and the uncertainties from unknown initial states and uncertain system model parameters. The simulation results show that the proposed filter has better performance than the extended Kalman filter (EKF) in both robustness and fast convergency.

• Journal of the military operations research society of Korea
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• v.32 no.1
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• pp.36-50
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• 2006

It is required to transport a considerable amount of wartime strategic materials from US to Korea via airlift operation. This study attempts to formulate the wartime airlift operation model and evaluate the airlift capability of mobilized resources, including civil aircrafts. Although an airlift plan has been annually updated by the Korea Transportation Command, it is necessary to evaluate the feasibility through simulation due to uncertainties in the process of airlift operation. Uncertain parameters are as follows; the inter-arrival time of materials in the US airfields, loading and unloading times, the distribution of aircraft's initial location at the time of mobilization order. The simulation is executed under two scenarios and the results are analyzed through a sensitivity analysis. Simulation result shows that the irregularity of inter-arrival time and the number of mobilized civil aircrafts are the most critical factors in influencing airlift capability.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.423-431
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• 2000

This paper presents a systematic approach to the input-output data-based fuzzy modeling for the complex and uncertain nonlinear systems, in which the conventional mathematical models may fail to give the satisfying results. To do this, we propose a new method that can yield a successful fuzzy model using a mGA hybrid schemes with a fine-tuning method. We also propose a new coding method fo chromosome for applying the mGA to the structure and parameter identifications of fuzzy model simultaneously. During mGA search, multi-purpose fitness function with a penalty process is proposed and adapted to guarantee the accurate and valid fuzzy modes. This coding scheme can effectively represent the zero-order Takagi-Sugeno fuzzy model. The proposed mGA hybrid schemes can coarsely optimize the structure and the parameters of the fuzzy inference system, and then fine tune the identified fuzzy model by using the gradient descent method. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its applications to two nonlinear systems.