• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.309-314
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• 2007

This paper presents an adaptive neural computation algorithm for multi-layered neural networks which are applied to identify the characteristic function of dynamic systems. The main feature of the proposed algorithm is that the initial learning rate for the employed neural network is assigned systematically, and also the assigned learning rate can be adjusted empirically for effective neural leaning. By employing the approach, enhanced modeling of dynamic systems is possible. The effectiveness of this approach is veri tied by simulations.

• 대한전기학회논문지
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• 제40권7호
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• pp.706-717
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• 1991

In order to remove the assumption of full state availability which is one of the major difficulties with the practical realization of variable structure control systems,a new switching function with a dynamic structure is proposed. And the control performances of the output feedback variable structure control systems with the dynamic switching function are evaluated through simulation studies. The proposed dynamic switching function is driven by small number of measured output and input variables while conventional static switching function requires full state information. Therefore, the proposition of the dynamic swiching function makes practical implementation of output feedback variable structure control scheme possible for the systems with unmeasurable state variables, high order systems and large scale systems that the conventional variable structure control schemes with static switching function cannot be applied. In the variable structure control systems with the dynamic switching function, desired control performance can be guaranteed by proper choice of design parameters such as poles of switching function dynamic equation and switching control gains even though small number of measured output and input variables are provided as shown in simulation resuls.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2719-2724
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• 2003

In this paper, we deal with the kinematic and dynamic modeling of hybrid robotic systems that are constructed by combination of parallel and serial modules or series of parallel modules. Previously, open-tree structure has been employed for dynamic modeling of hybrid robotic systems. Though this method is generally used, however, it requires expensive computation as the size of the system increases. Therefore, we propose an efficient dynamic modeling methodology for hybrid robotic systems. Initially, the dynamic model for the proximal module is obtained with respect to the independent joint coordinates. Then, in order to represent the operational dynamics of the proximal module, we model virtual joints attached at the top platform of the proximal module. The dynamic motion of the next module exerts dynamic forces to the virtual joints, which in fact is equivalent to the reaction forces exerted on the platform of the lower module by the dynamics of the upper module. Then, the dynamic forces at the virtual joints are distributed to the independent joints of the proximal module. For multiple modules, this scheme can be constructed as a recursive dynamic formulation, which results in reduction of the complexness of the open-tree structure method for modeling of hybrid robotic systems. Simulation for inverse dynamics is performed to validate the proposed modeling algorithm.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권6호
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• pp.325-336
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• 2006

This paper deals with the dynamic analysis of variable speed wind power systems with doubly-fed induction generators (DFIG). First, the mathematical modeling of wind farm which consists of turbine rotor, DFIG, rotor side and grid side converter and control systems is presented. In particular, the equation for dynamic modeling of the DFIG and the AC/DC/AC converter is expressed as dq reference frame. And then, on the basis of mathematical modeling for each component of wind farm, dynamic simulation algorithms for speed and pitch angle control of wind turbine and generated active and reactive power control of the DFIG and the AC/DC/AC converter are established. Finally, Using the MATLAB/SIMULINK, this paper presents dynamic simulation model for 6MW wind power generation systems with the DFIG considering distribution systems and performs the dynamic analysis of wind power systems in steady state. Moreover, this paper also presents the dynamic performance for the case when the voltage sag in grid source and phase fault in bus are occurred.

• 한국산업정보학회논문지
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• 제17권2호
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• pp.11-18
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• 2012

Smart Dynamic Pricing has been introduced to address the under-utilised network resources problem in mobile telecommunications systems. In this paper, we investigate the applicability of Smart Dynamic Pricing and its signalling models into Cognitive Radio Systems. Cognitive Radio System is defined as one in which cognitive radios are employed to access shared spectrum and/or dynamically allocated spectrum. Network elements, protocols, traffic and control channels, and system architecture are proposed for the implementation of Smart Dynamic Pricing in Cognitive Radio System. It is found that Smart Dynamic Pricing and its signalling models can be applied to Cognitive Radio Systems.

• International Journal of Control, Automation, and Systems
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• 제3권1호
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• pp.56-69
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• 2005

In this paper, it is clearly shown that the two well-known necessary and sufficient conditions mp n as generic static output feedback pole-assignment and mp + d(m+p) n+d as generic minimum d-th order dynamic output feedback pole-assignment on complex field, unbelievably, do not match up each other in strictly proper linear systems. For the analysis, a diagram analysis is newly created (which is defined by the analysis of 'convoluted rectangular/dot diagrams' constructed via node-branch conversion of the signal flow graphs of output feedback gain loops). Under this diagram analysis, it is proved that the minimum d-th order dynamic output feedback compensator for pole-assignment in m-input, p-output, n-th order systems is quantitatively decomposed into static output feedback compensator and its associated d number of arbitrary 1st order dynamic elements in augmented (m+d)-input, (p+d)-output, (n+d)-th order systems. Total configuration of the mismatched data is presented in a Table.

• 농업과학연구
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• 제51권3호
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• pp.295-305
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• 2024

Due to the environment of irregular soil characteristic for agricultural fields, dynamic characteristic occurs in the tractor cabin during agricultural operations. Operator's fatigue is increased, and operation performance is decreased by these irregular environment conditions. This study was conducted to measure and analyze the dynamic characteristic of a tractor cabin, a major agriculture machinery, during agricultural operations. The specification of tractor used in the study was a 95 kW class tractor. To analyze the dynamic characteristics of the tractor cabin, the main agricultural operations, plow tillage and rotary tillage, were selected. To measure data of dynamic characteristic of the cabin, which continuously changes during operations, an Ellipse Series INS (inertia navigation system) with a built-in IMU (inertia measurement unit) was attached to the center of gravity of the cabin. During field test, the gear stages of plow tillage were B4 (4.3 km·h^-1) and B5 (5.6 km·h^-1), and the gear stages of rotary tillage were A3 (3.3 km·h^-1) and A4 (4.2 km·h^-1), which are the most commonly used. To analyze dynamic characteristic such as roll and pitch during operations according to the gear stages. As a result, the dynamic characteristics of pitch increased more than the dynamic characteristics of roll as the travel speed increased, and the dynamic characteristics of both agricultural operations were in the range of 2 to 5°.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.413-416
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• 1997

Recently, dynamic recurrent neural networks(DRNN) for identification of nonlinear dynamic systems have been researched extensively. In general, dynamic backpropagation was used to adjust the weights of neural networks. But, this method requires many complex calculations and has the possibility of falling into a local minimum. So, we propose a new approach to identify nonlinear dynamic systems using DRNN. In order to adjust the weights of neurons, we use evolution strategies, which is a method used to solve an optimal problem having many local minimums. DRNN trained by evolution strategies with mutation as the main operator can act as a plant emulator. And the fitness function of evolution strategies is based on the difference of the plant's outputs and DRNN's outputs. Thus, this new approach at identifying nonlinear dynamic system, when applied to the simulation of a two-link robot manipulator, demonstrates the performance and efficiency of this proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.119.5-119
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• 2002

$\textbullet$ Lack of exact dynamic modeling of omni-directional mobile robots $\textbullet$ The exact dynamic model of the mobile robots including the wheel dynamics $\textbullet$ The joint-space and operational-space dynamic model of the mobile are dervied as anaytical forms $\textbullet$ Comparison between the discrepancy of the incomplete dynamic and the exact dynamic $\textbullet$ Useful aspect of redundant actuation

• 제어로봇시스템학회논문지
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• 제5권2호
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.