Earticle

The linearization of an input/output controller has been designed for an input time delay nonlinear time discretized nonlinear system. The time discretized nonlinear model has been obtained based on Taylor-Lie series expansion method and zero order hold assumption. The resulting control algorithm enables the time delay nonlinear system control, while the continuous time controller cannot handle a time delay nonlinear system due to its infinite dimensionality. The performance of the proposed controller is evaluated by using two different case studies: a Van Der Pol equation and a Continuous Stirred Tank Reactor (CSTR) system that all exhibit nonlinear behavior and input time delay. For all the case studies, the results validate the proposed methods.

Filter Bank Multicarrier with Offset Quadrature Amplitude Modulation (FBMC/OQAM) is one of the best waveforms used in 5G communications. The FBMC is a multicarrier modulation system (MCM) which suffers from high peak to average power ratio (PAPR) causing a reduction in power efficiency. Nonlinear companding techniques are widely used to reduce the peak-to-average-power ratio (PAPR). In this paper, we have evaluated the performance of different non-linear companding techniques to reduce the PAPR in FBMC/OQAM system while achieving good Bit Error rate. We assessed the hyperbolic tangent, the error function, the logarithmic function, the A-law, and Mu-law companding techniques. It is found that the best companding function is the Mu-law.

In radio-over-fiber (RoF) systems, nonlinear compensation is very important to meet the error vector magnitude (EVM) requirement of the mobile network standards. In this study, a nonlinear compensation technique based on an artificial neural network (ANN) is proposed for RoF systems. This technique is based on a backpropagation neural network (BPNN) with one hidden layer and three neuron units in this study. The BPNN obtains the inverse response of the system to compensate for nonlinearities. The EVM of the signal is measured by changing the number of neurons and the hidden layers in a RoF system modeled by a measured data. Based on our simulation results, it is concluded that one hidden layer and three neuron units are adequate for the RoF system. Our results showed that the EVMs were improved from 4.027% to 2.605% by using the proposed ANN compensator.

본 연구는 GDP 증가율의 비선형 변환을 이용한 회귀분석을 1970년부터 2019년까지 116개 국 가의 자료에 적용하여 소비위험을 추정한다. 비선형 변환을 위하여 본 연구는 GDP 증가율에 루트를 취하고 이를 회귀분석의 독립변수로 이용한다. 본 연구의 실증분석 결과에 따르면 전 체 국가 중 30개 선진국의 경우 GDP 증가율의 루트 변환 값이 소비증가율에 유의한 영향을 미치기 때문에 GDP 증가율과 소비증가율 사이에 비선형 관계가 존재한다. 반면 86개 개도국 의 경우 GDP 증가율의 루트 변환 값은 소비증가율에 유의한 영향을 미치지 못하며, 따라서 GDP 증가율과 소비증가율 사이에 비선형 관계가 존재하지 않는다. 이러한 결과에 따르면 개 도국의 경우 기존의 선형 회귀분석에 바탕을 둔 소비위험 추정 방법이 타당하나, 선진국의 경 우 선형 회귀분석을 이용하여 소비위험을 측정하는 것은 타당하지 않다. 선진국의 경우 GDP 증가율과 소비증가율이 비선형 관계를 갖는다는 결과에 따라 소비위험의 크기는 GDP 증가율 의 수준에 따라 변한다. 이러한 비선형 관계에 근거하여 소비증가율을 GDP 증가율의 비선형 함수로 설정 후 그 함수를 GDP 증가율에 대해 편미분함으로써 본 연구는 소비위험의 크기를 도출한다. 이와 같이 도출한 소비위험의 크기는 GDP 증가율이 커질수록 감소하며, 소비위험 의 감소폭 역시 GDP 증가율이 커질수록 작아지는 것으로 나타났다. 이러한 결과는 선진국의 소비위험은 경기 변동에 영향을 받을 가능성을 제시한다. 본 연구의 결과는 선진국과 개도국 의 소비위험 사이의 차이가 선진국과 개도국 사이의 금융시스템의 차이에 기인할 수 있다는 시사점을 제공한다.

Purpose : This study purposes to evaluate consumption risk by applying regression analysis with nonlinearly transformed GDP growth rates. Research design, data, methodology : This paper considers data for 116 countries from 1970 to 2019 and includes square root of GDP growth rates as a regressor for the regression analysis. The estimate for the square root of GDP growth rate is to be used for evaluating the partial derivative of per capita consumption growth rate with respect to per capita GDP growth rate. In this study, those calculated derivatives are used as a measure of consumption risk. Results : The results show that the square root of GDP growth rate has significant effects on the consumption growth rate for 30 advanced countries while it has no significant impacts for 87 developing countries. For those advanced economies, this study calculates the partial derivatives using the estimates for the square root of GDP growth rates. According to the results for the partial derivative, consumption risk for advanced countries decrease with their GDP growth rates and, in addition, decrease at slower speed for the larger GDP growth rates. In contrast, consumption risk is found to be independent of GDP growth rates for the developing countries. Conclusions : The results suggest that business cycles affect consumption risk for the advanced countries while they have no relationship with consumption risk for the developing countries. A possible implication for such difference is that different county-level of financial system can lead to contrast in consumption risk between advanced and developing countries.

A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.

We propose the robust nonlinear controller design methodology for the multivariable system which has hard nonlinearities (Coulomb friction, dead-zone, etc) and the structured real parameter uncertainty. The hard nonlinearity can be linearized by the RIDF technique and structured real parameter uncertainty can be modelled as the sense of Peterson-Hollot's quadratic Lyapunov bound. For this system, we apply the robust QLQG/H$_{\infty}$ control and then can obtain four Riccati equations. Because of the system's nonlinearity, however, one Riccati equation contains the nonlinear correction term that is very difficult to solve numerically, In order to treat this problem, using some transformations to Riccati equations, the nonlinear correction term can be eliminated. Then, only two Riccati equations need to design a controller. Finally, the robust nonlinear controller is synthesized via IRIDF techniques. To test this proposed control method, we consider the direct-drive robot manipulator system that has Coulomb frictions and varying inertia.

In this paper the nodes of the multilayer hidden layers have been modified for modeling the nonlinear systems. The structure of nodes in the hidden layers is built with the feedforward, the cross talk and the recurrent connections. The feedforward links are mapping the nonlinear function and the cross talks and the recurent links memorize the dynamics of the system. The cross talks are connected between the modes in the same hidden layers and the recurrent connection has self feedback, and these two connections receive one time delayed input signals. The simplified steam boiler and the analytic multi input multi output nonlinear system which contains process noise have been modeled using this neural networks.

In this paper the comparison between the neural networks and traditional approaches as nonlinear system identification methods are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. Computer simulation for an analytic dynamic model of a single input single output nonlinear system has been done for all the chosen models. Model validation for the obtained models also has been done with testing inputs of the sinusoidal, ramp and the noise ramp.

주기함수의 외력을 갖는 버선형 시스템의 다양한 응답 특성을 구하기 위해 새로운 조화함수법(HBM)을 적용하였다. 새로운 조화함수법의 해는 비선형항을 선형항으로부터 따로 분리시킨 다음 같은 주파수 성분을 갖는 비선형 방정식들을 Newton-Raphosn법으로 풀어서 구하였다. 다양한 천이(Bifurcation) 특성을 해석적으로 판별하기 위하여 HBM의 해를 이용하여 구한 섭동 방정식의 Floquet 지수의 고유해를 사용하였다. 새로이 개발한 HBM과 천이 판별법을 1차원 비선형항을 갖는 구조물인 ALP(Articulated Loading Platform) 모델과 다차원인 비선 형 회전체 모델에 적용시켜 HBM의 해의 정확성과 이들 시스템의 천이 특성의 하나인 Chaos 존재를 확인 하였다.

Crane operation for transporting heavy loads causes swinging motion at the loads due to crane's acceleration and deceleration. This sway causes the suspension ropes to leave their grooves and leads to possibility of serious damages. So, this swing of the objects is a serious problem and the goal of crane system is transporting to a goal position as soon as possible without the oscillation of the rope. Generally crane is operated by expert's knowledge. Therefore, a satisfactory control method to supress object sway during transport is indispensible. The dynamic behavior of the crane shows nonlinear characteristics. when the length of the rope is changed the crane is time varying system and the design of anti-sway controller is very difficult. In this paper, the nonlinear dynamic model for the industrial overhead crane whose girder, trolley and hoister move simultaneously is derived. and the Fuzzy logic controller based on the expert experiments during acceleration, constant velocity, deceleration and stop position period is proposed to supress the swing motion and control the position of the crane. The performance of the fuzzy controller for the nonlinear crane model is simulated on the personal computer.

In this paper, the position tracking control problem of the servo system with nonlinear dynamic friction is issued. The nonlinear dynamic friction contains a directly immeasurable friction state variable and the uncertainty caused by incomplete parameter modeling and its variations. In order to provide the efficient solution to these control problems, we propose the composite control scheme, which consists of the robust friction state observer, the FNN approximator and the approximation error estimator with sliding mode control. In first, the sliding mode controller and the robust friction state observer is designed to estimate the unknown internal state of the LuGre friction model. Next, the FNN estimator is adopted to approximate the unknown lumped friction uncertainty. Finally, the adaptive approximation error estimator is designed to compensate the approximation error of the FNN estimator. Some simulations and experiments on the servo system assembled with ball-screw and DC servo motor are presented. Results show the remarkable performance of the proposed control scheme. The robust friction state observer can successfully identify immeasurable friction state and the FNN estimator and adaptive approximation error estimator give the robustness to the proposed control scheme against the uncertainty of the friction parameters.

A kinematic nonlinear multivariable launcher is modeled of which the azimuth and elevation axes are drived simultaneously and position control systems are designed for this system by the PD and LQG/LTR control methods. Also, the suitable command input fonction is suggested for the desired command following performance and the two control systems with disturbances and load variation are evaluated for the entire operating range by computer simulation. It is found that the two linear controllers can be used for the kinematic nonlinear multivariable launcher in the entire operating range and LQG/LTR controller is more effective for disturbance rejection.

A tracking control scheme on the XY ball-screw drive system in the presence of nonlinear dynamic friction is proposed. A nonlinear dynamic friction is regarded as the Lund-Grenoble friction model to compensate effects of friction. The conventional VSC method that often has been used as a non-model-based friction controller has poor tracking performance in high-precision position tracking application since it cannot compensate the friction effect below a certain precision level completely. Thus to improve the precise position tracking performance, we propose the integral type VSC method combined with the friction-model-based observer. Then this control scheme has the high precise tracking performance compared with the non-model-baked VSC method and the PID control method with a similar observer. This fact is shown through the experiment on the XY ball-screw drive system with the nonlinear dynamic friction.

An investigation into the response statistics of a spring-pendulum system whose base oscillates randomly along vertical and horizontal line is made. The spring-pendulum system with internal resonance examined is known to be a good model for a variety of engineering systems, including ship motions with nonlinear coupling between pitching and rolling motions. The Fokker-Planck equation is used to generate a general first-order differential equations for the random responses of the system are reduced to a system of autonomous ordinary differential equations. In view of equilibrium solutions of this system and their stability, the response statistics is examined. It is seen that increase in horizontal excitation level leads to a decreased width of the internal resonance region.

An axial electromagnetic levitation system using attractive force is a highly nonlinear system due to the nonlinearity of materials, variable air gap and flux density. To control the levitating system with large air gap, a conventional PID control based on the linear model is not satisfactory to obtain the desired performance and the position tracking control of the sinusoidal motion by simulation results. Thus, sliding mode control(SMC) based on the input-output linearization is suggested and evaluated by simulation and experimental approaches. Usefulness of the SMC to this system is conformed experimentally. If the expected variation of added mass can be included in the gain conditions and the model, the position control performance of the electromagnetic levitation system with large air gap will be improved with robustness.

우리나라 전력시장 발전부문에 경쟁을 도입함으로써 각발전사들이 얼마나 발전해야 할지는 중요한 문제가 되었고, 안정적인 계통한계가격(System Marginal Price, SMP)유지는 중요한 문제다. 본 연구에서는 인공신경망 중 MLP(Multi-Layer Perceptron), NARX(Nonlinear Autoregressive exogenous)를 이용하여 서로 비교 분석했고 외부변수로 전력수요를 설정하여 SMP를 예측했으며, ARIMA(Autoregressive Integrated Moving Average)와도 비교를 실시했다. 모두 30일, 40일, 50일을 예측해 봤으며 MLP의 경우 은닉층의 개수가 90개, 85개, 80개 였을 때 최적의 결과를 보여준 반면, NARX의 경우 95개, 80개, 70개 였을 때 최적의 결과를 보여주었다. 전반적으로 모든 예측에서 ARIMA, MLP, NARX 순으로 예측 오차가 작게 나타났다.

With the introduction of competition in the power generation sector in Korea, determining how much each electricity company should develop and maintain a stable system marginal price (SMP) has become an important issue. In this study, SMP was predicted by comparing the values for each company using a multilayer perceptron (MLP) and nonlinear autoregressive exogenous (NARX) model among artificial neural networks (ANN), and setting electricity demand as an external variable. The Autoregressive Integrated Moving Average (ARIMA) was also compared. Each method predicted 30, 40, and 50 days. In the case of MLP, optimal results were obtained when the number of hidden layer units were 90, 85, and 80, whereas in the case of NARX, optimal results were obtained when the number of hidden layer units were 95, 80, and 70. Overall, the forecast errors were small in the order of ARIMA, MLP, and NARX for all predictions.

The equations of motion for a basic industrial robotic system which has a rigid or a flexible arm are derived by Lagrange's equation, respectively. Especially, for the deflection of the flexible arm, the assumed mode method is employed. These equations are highly nonlinear equations with nonlinear coupling between the variables of motion. In order to design the control law for the rigid-arm robot, Hunt-Su's nonlinear transformation method and Marino's feedback equivalence condition are used with linear quadratic regulator(LQR) theory. The control law for the rigid-arm robot is employed to input the desired path and to provide the required nonlinear transformations for the flexible-arm robot to follow. By using the implicit Euler method to solve the nonlinear equations, the comparison of the motions between the flexible and the rigid robots and the effect of flexibility are examined.

In this study optimal analysis of pipe network was performed using linear and non linear analysis method for complex real pipe network system of Mungyeong water purification field system which consists of 70 nodes and 86 elements. From the examination result of total flow which is distributed to each pipe, it is found that KYPIPE2 Model supplies less amount than NLAM. It is known that dynamic water level and pressure head of KYPIPE2 Model and NLAM are nearly in accordance with each other from each method of the pipe network analyses, and appeared that both methods of analysis shows high reliable result since the distribution of dynamic water level for every node is the short range of EL. 205.0m~EL. 210.0m besides the pressed dynamic water level. The analysis results of pressure in the methods of pipe network analysis for KYPIPE2 Model and NLAM are similar and it is satisfactory result that the pressure distributions of the tab water design criterion of 5.0kgf/cm$^2$ besides the small part of highland.

In this study, we conduct linear and nonlinear modeling of the DC motor driving system of a wheeled mobile robot, which is a nonlinear system involving dead zone, friction, and saturation. The DC motor driving system consists of a DC motor, a wheel, and gears. A linear DC motor driving system is modeled using a steady-state response and parameter measurements. A nonlinear DC motor driving model is identified with the use of the Hammerstein-Wiener method. By using these models, PID controllers for the DC motor system are then established. Each PID controller is applied as a low-level controller in order to achieve posture stabilization control for the real mobile robot. We also compare the performance of the proposed PID controllers in posture stabilization experiments by using several different final robot postures.

In this paper, the effect of a dynamic vibration absorber to suppress the response of a base excitation vibration system composed of a cubic nonlinear spring and a friction damper is investigated. And the dynamic absorber consists of a linear spring and a viscose damper. The mathematical models of these systems are governed by second order nonlinear ordinary differential equations. The response characteristics of the system are analyzed using the slowly changing phase and amplitude(SCPA) method, which is one of the averaging methods. As a function of the friction force ratio, It was obtained the locking frequency at which the relative motion starts was obtained, and the regions where the locking occurred. The displacement transmissibility was investigated according to the change of the design parameter, and the optimal design parameters could be found to minimize the displacement transmissibility.