• Journal of Power Electronics
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• v.11 no.4
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• pp.393-400
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• 2011

The main objective of this paper is to control the speed of Nonlinear Hybrid Electric Vehicle (HEV) by controlling the throttle position. Various control techniques such as well known Proportional-Integral-Derivative (PID) controller in conjunction with state feedback controller (SFC) such as Pole Placement Technique (PPT), Observer Based Controller (OBC) and Linear Quadratic Regulator (LQR) Controller are designed. Some Intelligent control techniques e.g. fuzzy logic PD, Fuzzy logic PI along with Adaptive Controller such as Self Organizing Controller (SOC) is also designed. The design objective in this research paper is to provide smooth throttle movement, zero steady-state speed error, and to maintain a Selected Vehicle (SV) speed. A comparative study is carried out in order to identify the superiority of optimal control technique so as to get improved fuel economy, reduced pollution, improved driving safety and reduced manufacturing costs.

• Transactions of Materials Processing
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• v.4 no.3
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• pp.214-221
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• 1995

In the paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in the neural network. The architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of a neural network, an optimal billet is determined by applying the nonlinear mathematical relationship between the aspect ratios in the initial billet and the final products. The amount of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet aspect ratios and those of the unfilled volumes. After learning, the system is able to predict the filling regions which are exactly the same or slightly different to the results of finite element simulation. This new method is applied to find the optimal billet size for the plane strain rib-web product in cold forging. This would reduce the number of finite element simulation for determining the optimal billet size of forging product, further it is usefully adapted to physical modeling for the forging design.

• Water for future
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• v.29 no.4
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• pp.131-138
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• 1996

The objective of this study is to develop an optimal operation model for the sewage treatment plants using nonlinear programming (NLP) technique and the QUAL2E model. The model finds the minimum-cost operation of sewage treatment plants while satisfying all design constraints and water quality (BOD) standard. The model is applied to four sewage treatment plants in Han River for the city of Seoul. It has been found that optimal operation schedule for the sewage treatment plants can be computed and it is more economic to operate the plants according to the schedule which satisfies the water quality constraints in the river. In addition, the water quality in the river can be predicted using the model under the treatment policy.

• Journal of Ship and Ocean Technology
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• v.9 no.3
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• pp.1-13
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• 2005

A design procedure for a ship with minimum total resistance has been developed using a numerical optimization method called SQP (Sequential Quadratic Programming) to search for optimized hull form and CFD(Computational Fluid Dynamics) technique. The friction resistance is estimated using the ITTC 1957 model-ship correlation line formula and the wave making resistance is evaluated using a potential-flow panel method based on Rankine sources with nonlinear free surface boundary conditions. The geometry of hull surface is represented and modified using B-spline surface patches during the optimization process. Using the Series 60 hull ($C_B$ =0.60) as a base hull, the optimization procedure is applied to obtain an optimal hull that produces the minimum total resistance for the given constraints. To verify the validity of the result, the original model and the optimized model obtained by the optimization process have been built and tested in a towing tank. It is shown that the optimal hull obtained around $13\%$ reduction in the total resistance and around $40\%$ reduction in the residual resistance at a speed tested compared with that of the original one, demonstrating that the present optimization tool can be effectively used for efficient hull form designs.

• Advances in materials Research
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• v.13 no.1
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• pp.1-18
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• 2024

The present experimental investigation focuses on finding optimal parametric data-set of laser micro-drilling operation with minimum taper and Heat-affected zone during laser micro-drilling of Carbon Nanotube/Epoxy-based composite materials. Experiments have been conducted as per Box-Behnken design (BBD) techniques considering cutting speed, lamp current, pulse frequency and air pressure as input process parameters. Then, the relationship between control parameters and output responses is developed using second-order nonlinear regression models. The analysis of variance test has also been performed to check the adequacy of the developed mathematical model. Using the Response Surface Methodology (RSM) and an Accelerated particle swarm optimization (APSO) technique, optimum process parameters are evaluated and compared. Moreover, confirmation tests are conducted with the optimal parameter settings obtained from RSM and APSO and improvement in performance parameter is noticed in each case. The optimal process parameter setting obtained from predictive RSM based APSO techniques are speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), Air pressure (1 kg/cm²) for Taper and speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), air pressure (3 kg/cm²) for HAZ. From the confirmatory experimental result, it is observed that the APSO metaheuristic algorithm performs efficiently for optimizing the responses during laser micro-drilling process of nanocomposites both in individual and multi-objective optimization.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1323-1328
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• 2011

In the current design process for the lower arm used in automobile parts, an optimal solution of its various design variables should be found through exploration of the design space approximated using the response surface model formulated with a first- or second-order polynomial equation. In this study, a multi-level computational DOE (design of experiment) was carried out to explore the design space showing nonlinear behavior, in terms of factors such as the total weight and applied stress of the lower arm, where a fractional-factorial orthogonal array based on the artificial neural network model was introduced. In addition, the tolerance robustness of the optimal solution was estimated using a tolerance optimization with six sigma constraints, taking into account the tolerances occurring in the design variables.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.1
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• pp.42-48
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• 2006

In this study, a repetitive response surface enhancement technique (RRSET) is proposed as a new system approximation method for the efficient multidisciplinary design and optimization (MDO). In order to represent the highly nonlinear behavior of the response with second order polynomials, RRSET introduces a design space transformation using stretching functions and repetitive response surface improvement. The tentative optimal point is repetitively included to the set of experimental points to better approximate the response surface of the system especially near the optimal point, hence a response surface with significantly improved accuracy can be generated with very small experimental points and system iterations. As a system optimizer, the simulated annealing, which generates a global design solution is utilized. The proposed technique is applied to several numerical examples, and demonstrates the validity and efficiency of the method. With its improved approximation accuracy, the RRSET can contribute to resolve large and complex system design problems under MDO environment.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.210-215
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• 1998

To design a space frame structure by the conventional method is not easy in practical sense since it is generally a three-dimensional complicated form, and stability and nonlinear problems are not easily checked in the design process. This paper describes two modules, the Model Generator which is based on PATRAN user interface that enables users to generate a complicated finite element model; the Optimum Design Module which analyzes output results of analysis program, and designs members of a space frame. The Model Generator is based on PCL while C++ language is used in the Optimum Design Module. Structural analysis is performed by using ABAQUS. All of these modules constitute Space Frame Integrated Design System. The Core of the system is PATRAN database, in which the Model Generator creates information of a finite element model. Then, PATRAN creates input files needed for the analysis program from the information of the finite element model in the database, and in turn, imports output results of analysis program to the database. Finally, the Optimum Design Module processes member grouping of a space frame based on the output results, and performs optimal member selection of a space frame. This process is repeated until the desired optimum structural members are obtained.

• Journal of Korean Society of societal Security
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• v.4 no.1
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• pp.49-55
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• 2011

The primary objective of this paper is to develop optimal algorithms of reinforced concrete frame structural systems by the limit state design(CP 1110) and to look into the possibility of detailed design of these structural systems. The structural formulation is derived on the finite element method. The objective of optimization of a reinforced structure for a specified geometry is mainly to determine the optimum cross-sectional dimensions of concrete and the area of the various sizes of the reinforcement required for each member. In addition to the detail s such as the amount of web reinforcement, cutoff points of longitudinal reinforcedments etc. are also considered as design variables. In this study, the method of "Generalized Reduced Gradient, Rounding and with Neighborhood search" and "the Sequential Linear Programming" are employed as an analytical method of nonlinear optimization.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.60-64
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• 1997

Designing fuzzy controller, there are difficulties that we have to determine fuzzy rules and shapes of membership functions which are usually obtained by the amount of trial-and-error or experiences from the experts. In this paper, to overcome these defects, genetic algorithms which is probabilistic search method based on genetics and evolution theory are used to determine fuzzy rules and fuzzy membership functions. We design a series compensation fuzzy controller, then determine basic structures, input-output variables, fuzzy inference methods and defuzzification methods for fuzzy controllers. We develop genetic algorithms which may search more accurate optimal solutions. For evaluating the fuzzy controller performances through experiments upon an actual system, we design the fuzzy controllers for the speed control of a DC series motor with nonlinear characteristics and show good output responses to reference inputs.