• Journal of the Korea Safety Management & Science
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• v.15 no.4
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• pp.123-129
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• 2013

Among various manual materials handling tasks, pushing/pulling was known to be one of the risk factors for the low back and shoulder musculoskeletal disorders (MSDs). This study was conducted to find out an optimal solution set of the handle height and distance for 4-wheel cart with two vertical handles. Ten male college students participated in the pushing force measurement experiment. The face-centered cube design, one of the central composite designs, was applied for the experiment, and the isometric voluntary pushing force was measured in 9 treatment conditions. The second order response surface model was predicted by using the pushing strength as a response variable, and the handle height and distance as independent factors. According to the 2nd order response model, the handle height and distance showed nonlinear relationship with the isometric pushing strength. To maximize the 2nd order response model (pushing force), the handle height and distance were optimized. The optimal handle height was 'xyphoid process height - stature', and the optimal handle distance was '$1.25{\times}shoulder$ width'. When calculated using the anthropometric data of the subjects of this study, the optimal handle height was $115.4{\pm}3.4$ cm, slightly higher than the elbow height, and the handle distance was $52.9{\pm}2.3$ cm.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.25 no.3
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• pp.218-223
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• 2012

A rigorous electro-optical simulation and ray tracing for an electric field driven liquid crystal Fresnel lens was proposed to obtain design parameters of the electrode pattern of the Fresnel lens. The optimal design was carried out using Taguchi's experimental method for 17.1"($368{\times}229.5$ mm) wide LCD panels with 9 views. For the calculation of the distribution of liquid crystal molecules and the optical transmission of the panel, finite difference method and extended Jones matrix method were used to deal effectively with highly nonlinear and complicated motional equations of the liquid crystal molecules and to obtain the oblique transmission characteristics of the LCD panel. As simulation results, the optimal lengths of the 3 electrodes of the Fresnel lens are 4.0 ${\mu}m$, 30 ${\mu}m$ and 83 ${\mu}m$, respectively, and the locations of the second and third electrodes are 32.9-33.0 ${\mu}m$ and 45.9-46.0 ${\mu}m$, respectively. The optimal applied voltage of the 3 electrodes are found to be 5.75 V, 7.80 V and 11.9 V, respectively.

• Journal of Advanced Marine Engineering and Technology
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• v.15 no.1
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• pp.85-95
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• 1991

All physical systems are nonlinear to some degree. The examples are relay, backlash, deadzone, saturation element and so on. In the linear control system design, it is useful method to restrict the nonlinearity to the linearity of system over the operation range. It is worth noting that nonlinearities may be intentionally introduced in to a system. A simple of an intentional non-linearity is the Bang-Bang controller which uses the On-Off relay. In this paper, an angular position servosystem made of a DC servomotor controlled by a microcomputer is discribed. Authors use two methods in the design of controller. The one is linear controller designed by the optimal feedback control theory only and the other is nonlinear controller designed by On-Off relay with optimal feedback control theory. To do the real time control, the controller is designed by using 16bit personal computer and A/D.D/A converter(12bit) is used in order to convert the signal. According to this way, the results from real time control are as follows. 2) Under the On-Off controller with hysterisis the influence of disturbance is considerably smaller than the linerar controller. 3) An increase in the sampling period has a destabilizing effect. 4)In the controller performance, the response time of the On-Off controller is longer than that of the linear controller. To close, we note that the On-Off controller with hysterisis is more attractive than the linear controller in the presence of the input limit.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.44-51
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• 2008

In this paper, the optimal tuning of a cross-coupled controller linked with the feedforward controller is studied to reduce contouring and tracking errors of a bi-axial servomechanisms by using the previously developed integrated tuning method. The CCC system for an arbitrary curve, which is combined with the feedforward controller, is formulated by a state-space based on a series of linear motion trajectories. An optimal tuning problem is formulated as a nonlinear constrained optimization problem including relevant controller parameters of the servo. To verify the effectiveness of the proposed optimal tuning procedure, linear and circular motion experiments are performed on the xy-table. Experimental results confirm that both tracking and contouring errors are significantly reduced by applying the proposed control and tuning system.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.408-421
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• 2015

Because the wheel of V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor (PMSM) has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming job. In order to overcome difficulties for design of the linear controllers, a hybrid recurrent Chebyshev neural network (NN) control system is proposed to control for a PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Chebyshev NN control system consists of an inspector control, a recurrent Chebyshev NN control with adaptive law and a recouped control. Moreover, the online parameters tuning methodology of adaptive law in the recurrent Chebyshev NN can be derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, the optimal learning rate of the parameters based on discrete-type Lyapunov function is derived to achieve fast convergence. The recurrent Chebyshev NN with fast convergence has the online learning ability to respond to the system's nonlinear and time-varying behaviors. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

• Journal of Industrial Convergence
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• v.16 no.2
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• pp.33-39
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• 2018

This paper describes the design method of the linear PID controller and proposed the design method in the future. The first PID design method is to ensure phase margin and gain margin. This method guarantees stability by designing in the frequency domain. The second method is an internal model control method. This method is to design the PID controller using the parameters of the internal model after identifying the internal model for the control model. Therefore, this method has a strong disturbance characteristic. Finally, a proposed Cascade and smith-Predictor controller. The combination of the cascade controller and the smith-predator of this method is a controller structure that has two advantages: robust control and optimal control. This method can obtain the performance evaluation index as the optimal controller design method. This PID controller design method becomes the basis of the nonlinear method and is being continuously studied.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1449-1454
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• 2003

For many industrial processes there are good static models used for process design and steady state operation. By using system identification techniques, it is possible to obtain black-box models with reasonable complexity that describe the system well in specific operating conditions [1]. But black-box models using inductive modeling(IM) is not suitable for model based control because they are only valid for specific operating conditions. Thus we need to use deductive modeling(DM) for a wide operating range. Furthermore, deductive modeling is several merits: First, the model is possible to be modularized. Second, we can increase and decrease the model complexity. Finally, we are able to use model for plant design. Power plant must be able to operate well at dramatic load change and consider safety and efficiency. This paper proposes a simplified nonlinear model of an industrial boiler, one of component parts of a power plant, by DM method and applies optimal control to the model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.1920-1929
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• 1994

The stochastic finite element method(SFEM) based structural optimal design is presented. Random system response including uncertainties for the design variable is calculated with first order perturbation method. A method for calculating the sensitivity coefficients is developed using the equilibrium equation and first-order perturbed equation. Numerical results are presented for a truss, frame and plate structures with displacement and stress constraints. The sensitivity calculation proposed here is compared with finite difference method. A nonlinear programming technique is used to solve the problem. The procedure is easily incorporated with existing deterministic structural optimization.

• Structural Engineering and Mechanics
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• v.4 no.4
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• pp.425-436
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• 1996

An application of the perturbation method to optimum structural design with random parameters is presented. It is formulated on the basis of the first-order stochastic finite element perturbation method. It also takes into full account the stress, displacement and eigenvalue constraints, together with the rates of change of the random variables. A method for calculating the sensitivity coefficients in regard to the governing equation and the first-order perturbed equation has been derived, by using a direct differentiation approach. A gradient-based nonlinear programming technique is used to solve the problem. The numerical results are specifically noted, where the stiffness parameter and external load are treated as random variables.