• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1516-1520
• /
• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control a nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.2
• /
• pp.145-150
• /
• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.76.2-76
• /
• 2002

Because of the wheeled mobile robot is modeled by nonlinear system framework and controlled by nonlinear algorithms or fuzzy algorithms, the treatment of wheeled mobile robot is very complecate and conservative. In this paper, a new model of two wheeled mobile robot, which is a type of linear system and treated easily, is presented. And we will show that the control algorithms based on the linear system theory is well work to the wheeled mobile robot by simulation and experiment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.05a
• /
• pp.191-194
• /
• 2001

The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Also, the results of prediction are not a good performance so far, especially in the high-level ozone concentration. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system.

• Proceedings of the KSME Conference
• /
• 2000.11a
• /
• pp.510-515
• /
• 2000

Dynamic stability of a flying structure undertaking constant and pulsating axial forces is investigated in this paper. The equations of motion of the structure, which is idealized as a free-free beam, are derived by using the hybrid variable method and the assumed mode method. The structural system includes a directional control unit to obtain the directional stability. The analysis model presented in this paper considers the nonlinear effect due to rigid body motion of the beam. Dynamic stability of the system is influenced by the nonlinear effect. In order to examine the nonlinear effect, first the unstable regions of the linear system are obtained by using the method based upon Floquet's theory, and dynamic responses of the nonlinear system in the unstable region are obtained by using direct time integration method. Dynamic stability of the nonlinear system is determined by the obtained dynamic responses.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.10
• /
• pp.927-932
• /
• 2007

The sophisticated mathematical model is required for the design and the database construction of the advanced flight control system of UAV. In this paper, flight test of KARI's research UAV, often called DURUMI-II, is implemented for the data acquisition from the maneuver flight. The flight path reconstruction is implemented to ensure that the measured data is consistent and error free. The nonlinear system identification for the refined mathematical modeling is implemented with the verified measurements from the flight path reconstruction. The simulation with the identified results have a good validation when the simulated responses were compared to the flight tested data.

• Proceedings of the KIEE Conference
• /
• 2002.11c
• /
• pp.233-236
• /
• 2002

We consider a single-input-single-output nonlinear system which can be represented in a normal form. The nonlinear system has a modeling uncertainties including the input coefficient uncertainties. A high-gain observer is used to estimate the states variables to reject a modeling uncertainty. A globally bounded output feedback integral sliding mode control is proposed to stabilize the closed loop system. The proposed integral sliding mode control can asymptotically stabilize the closed loop system in the it presence of input coefficient uncertainty.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.11
• /
• pp.916-921
• /
• 2002

We presents a new method of constructing an equivalent T-S fuzzy model by using the sum of products of linearly independent scalar functions from nonlinear dynamics. This method exactly expresses nonlinear systems and automatically determines the number of rules. We design a stabilizing controller f3r ul inverted pendulum system by using the concep of parallel distributed compensation (PDC) and linear matrix inequalities (LMIs) based on the proposed T-S fuzzy modeling method. We show effectiveness of a systematically designed fuzzy controller based on the proposed T-S fuzzy modeling method through the simulation and experiment of an inverted pendulum system.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2010.05a
• /
• pp.1439-1442
• /
• 2010

Time-lag recurrent neural networks model (Time-Lag RNNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$) and mean relative humidity ($RH_{mean}$). And, for the performances of Time-Lag RNNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of Time-Lag RNNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE using Time-Lag RNNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using Time-Lag RNNM.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.04a
• /
• pp.346-350
• /
• 1996

This paper presents a Neural Network Identification (NNI) method for modeling of highly complicated nonlinear and time varing human system with a pneumatically driven mock circulation system of Left Ventricular Assist Device(LVD). This system consists of electronic circuits and pneumatic driving circuits. The initation of systole and the pumping duration can be determined by the computer program. The line pressure from a pressure transducer inserted in the pneumatic line was recorded. System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, Heart Rate(HR), Systole-Diastole Rate(SDR), which can vary state of system, and preload, afterload, which indicate the systemic dynamic characteristics and output parameters are preload, afterload.