• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.311-314
• /
• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF. To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKP uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• 제어로봇시스템학회논문지
• /
• 제7권11호
• /
• pp.958-961
• /
• 2001

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC is their robustness with respect to a model mismatch and disturbances. But it is difficult to apply for nonlinear systems. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in ANFIS can be effectively utilized to control a nonlinear systems. In this paper, we propose new ANFIS-based IMC controller for nonlinear systems. Numerical simulation results show that the proposed control scheme has good performances.

• Smart Structures and Systems
• /
• 제24권6호
• /
• pp.683-692
• /
• 2019

Traditional damage detection methods for nonlinear structures are often based on simplified models, such as the mass-spring-damper and shear-building models, which are insufficient for predicting the vibration responses of a real structure. Conventional global nonlinear finite element model updating methods are computationally intensive and time consuming. Thus, they cannot be applied to practical structures. A decentralized approach for identifying the nonlinear material parameters is proposed in this study. With this technique, a structure is divided into several small zones on the basis of its structural configuration. The unknown material parameters and measured vibration responses are then divided into several subsets accordingly. The structural parameters of each subset are then updated using the vibration responses of the subset with the Newton-successive-over-relaxation (SOR) method. A reinforced concrete and steel frame structure subjected to earthquake loading is used to verify the effectiveness and accuracy of the proposed method. The parameters in the material constitutive model, such as compressive strength, initial tangent stiffness and yielding stress, are identified accurately and efficiently compared with the global nonlinear model updating approach.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
• /
• pp.899-902
• /
• 1993

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming combined with neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (MNPC) shows good performances and robustness. To whom all correspondence should be addressed.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1996년도 추계학술대회 논문집
• /
• pp.799-805
• /
• 1996

In many cases, the magnetic farce model is linearized at the origin in designing the controller of a magnetic bearing system. However. this linear assumption is violated by the unmodeled nonlinear effect such as sensor dislocation and backup bearing dislocation. Therefore, a direct probe into the nonlinear magnetic force model in an active magnetic bearing system is necessary. To analyze the nonlinear magnetic force model of a magnetic bearing system, phase plot analysis which is to plot the numerical solution of the nonlinear equation in several initial points in the interested region is applied. Phase plot analysis is used to observe a nonlinear dynamic system qualitatively (not quantitatively). With this method, we can get much useful information of the nonlinear system. Among this information, a bifurcation graph that represents stability and locations of fixed points is essential. From the bifurcation graph, a stability criterion of magnetic bearing system is derived.

• 전기학회논문지
• /
• 제60권6호
• /
• pp.1229-1238
• /
• 2011

This paper deals with the lateral dynamic model of an all-wheel steered articulated vehicle to design a guidance controller. Nonlinear dynamic model of articulated vehicle is developed by complementing the model about the BRT system of California PATH in U. S. A. and the Phileas system of the APTS in Netherlands. Linear lateral dynamic model has been derived from the nonlinear dynamic model under some assumptions associated with the driving conditions. To design a guidance controller, we derive a transfer function that is steering angle as input and lateral acceleration as output from the linear lateral dynamic model by applying the parameter of vehicle that is developed by Korea Railroad Research Institute. To validate the dynamic model, nonlinear dynamic model has been compared with a vehicle model that has been programmed in ADAMS, and linear dynamic model has been compared with a nonlinear dynamic model under sime assumptions.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.160-163
• /
• 1996

This paper deals with MCMBPC(Multivariable Constrained Model Based Predictive Controller) for nonlinear boiler system with noise and disturbance. MCMBPC is designed by linear state space model obtained from some operating point of nonlinear boiler system and Kalman filter is used to estimate the state with noise and disturbance. The solution of optimization of the cost function constrained on input and/or output variables is achieved using quadratic programming, viz. singular value decomposition (SVD). The controller designed is shown to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• 한국도로학회논문집
• /
• 제18권4호
• /
• pp.63-68
• /
• 2016

PURPOSES : The nonlinear model of fatigue cracking is typically used for determining the maintenance period. However, this requires that the model parameters be known. In this study, the particle filter (PF) method was used to determine various statistical parameters such as the mean and standard deviation values for the nonlinear model of fatigue cracking. METHODS : The PF method was used to determine various statistical parameters for the nonlinear model of fatigue cracking, such as the mean and standard deviation. RESULTS : On comparing the values obtained using the PF method and the least square (LS) method, it was found that PF method was suitable for determining the statistical parameters to be used in the nonlinear model of fatigue cracking. CONCLUSIONS : The values obtained using the PF method were as accurate as those obtained using the LS method. Furthermore, reliability design can be applied because the statistical parameters of mean and standard deviation can be obtained through the PF method.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.1500-1503
• /
• 1996

This paper combines the disturbance accommodating control(DAC) and nonlinear model inversion control for a skid-to-turn(STT) missile. The missile autopilot may be designed to be robust with respect to a variety of uncertainties. We proposes the two step control design method. Nonlinear model inversion control is used as the main design method. Due to the model uncertainties and external disturbances, the exact nonlinear model inversion can not be achieved. DAC is designed to detect, to identify, and to compensate these uncertainties. DAC's disturbance observer is linear. Thus it is easy to implement. It does not cause the convergence problem due to coexistence between the modeling uncertainties and external disturbances. 6 DOF simulation results show that the proposed method may improve the missile tracking performance.

• 한국농공학회지
• /
• 제35권2호
• /
• pp.57-64
• /
• 1993

A modified kinematic wave model of the overland flow in vegetated filter strips was developed. The model can predict both flow depth and hydraulic radius of the flow. Existing models can predict only mean flow depth. By using the hydraulic radius, erosion, deposition and flow's transport capacity can be more rationally computed. Spacing hydraulic radius was used to compute flow's hydraulic radius. Numerical solution of the model was accomplished by using both a second-order nonlinear scheme and a linear solution scheme. The nonlinear portion of the model ensures convergence and the linear portion of the model provides rapid computations. This second-order nonlinear scheme minimizes numerical computation errors that may be caused by linearization of a nonlinear model. This model can also be applied to golf courses, parks, no-till fields to route runoff and production and attenuation of many nonpoint source pollutants.