• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.729-731
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• 2004

This paper deals with the design of a low order $H_{\infty}$ controller by using an iterative linear matrix inequality (LMI) method. The low order $H_{\infty}$ controller is represented in terms of LMIs with a rank condition. To solve the non-convex rank-constrained LMI problem, a linear penalty function is incorporated into the objective function so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Numerical experiments show the effectiveness of the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.11
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• pp.747-752
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• 2004

This paper presents an iterative linear matrix inequality (LMI) approach to the design of a static output feedback (SOF) stabilization controller. A linear penalty function is incorporated into the objective function for the non-convex rank constraint so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. Hence, the overall procedure results in solving a series of semidefinite programs (SDPs). With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Extensive numerical experiments are Deformed to illustrate the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.12
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• pp.655-660
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• 2004

This paper deals with the design of a fixed-structure $H_\infty$ power system stabilizer (PSS) by using an iterative linear matrix inequality (LMI) method. The fixed-structure $H_\infty$ controller is represented in terms of LMIs with a rank condition. To solve the non-convex rank-constrained LMI problem, a linear penalty function is incorporated into the objective function so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Numerical experiments show the practical applicability of the proposed algorithm.

• Advances in Computational Design
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• v.5 no.3
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• pp.209-231
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• 2020

In order to evaluate the performance of three heuristic optimization algorithms, namely, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO) for optimal stacking sequence of laminated composite plates with respect to critical buckling load and non-dimensional natural frequencies, a multi-objective optimization procedure is developed using the weighted summation method. Classical lamination theory and first order shear deformation theory are employed for critical buckling load and natural frequency computations respectively. The analytical critical buckling load and finite element calculation schemes for natural frequencies are validated through the results obtained from literature. The comparative study takes into consideration solution and computational time parameters of the three algorithms in the statistical evaluation scheme. The results indicate that particle swarm optimization (PSO) considerably outperforms the remaining two methods for the special problem considered in the study.

• International Journal of CAD/CAM
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• v.8 no.1
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• pp.1-10
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• 2009

This paper presents a topology optimization approach using element-free Galerkin method (EFGM) for the optimal design of compliant mechanisms with geometrically non-linearity. Meshless method has an advantage over the finite element method(FEM) because it is more capable of handling large deformation resulted from geometrical nonlinearity. Therefore, in this paper, EFGM is employed to discretize the governing equations and the bulk density field. The sensitivity analysis of the optimization problem is performed by incorporating the adjoint approach with the meshless method. The Lagrange multipliers method adjusted for imposition of both the concentrated and continuous essential boundary conditions in the EFGM is proposed in details. The optimization mathematical formulation is developed to convert the multi-criteria problem to an equivalent single-objective problem. The popularly applied interpolation scheme, solid isotropic material with penalization (SIMP), is used to indicate the dependence of material property upon on pseudo densities discretized to the integration points. A well studied numerical example has been applied to demonstrate the proposed approach works very well and the non-linear EFGM can obtain the better topologies than the linear EFGM to design large-displacement compliant mechanisms.

• Structural Engineering and Mechanics
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• v.77 no.5
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• pp.613-626
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• 2021

In this paper, an enhanced Violation-based Sensitivity analysis and Border-Line Adaptive Sliding Technique (ViS-BLAST) will be utilized for optimization of some well-known structural and mechanical engineering problems. ViS-BLAST has already been introduced by the authors for solving truss optimization problems. For those problems, this method showed a satisfactory enactment both in speed and efficiency. The Enriched ViS-BLAST or EVB is introduced to be vastly applicable to any solvable constrained optimization problem without any specific initialization. It uses one-directional step-wise searching technique and mostly limits exploration to the vicinity of FNF border and does not explore the entire design space. It first enters the feasible region very quickly and keeps the feasibility of solutions. For doing this important, EVB groups variables for specifying the desired searching directions in order to moving toward best solutions out or inside feasible domains. EVB was employed for solving seven numerical engineering design problems. Results show that for problems with tiny or even complex feasible regions with a larger number of highly non-linear constraints, EVB has a better performance compared to some records in the literature. This dominance was evaluated in terms of the feasibility of solutions, the quality of optimum objective values found and the total number of function evaluations performed.

• Current Optics and Photonics
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• v.7 no.1
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• pp.33-37
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• 2023

Real-time adaptive control of laser output polarization is presented in a 10-W-level non-polarization-maintaining (non-PM) fiber amplifier. While the output polarization from a non-PM fiber amplifier tends to be irregular, depending on output power, time, and perturbation, closed-loop polarization control can maintain the polarization extinction ratio at higher than 20 dB. Real-time polarization control can attain the target linear polarization mostly within 1.4-25 ms and shows stability against external perturbations. This approach can satisfy both linear polarization and high output power in a non-PM amplifier, and facilitates optimization of laser performance and maintenance-free operation.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.643-653
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• 2017

The paper presents controller optimization algorithm for a 12-pulse voltage source converter (VSC) based high voltage direct current (HVDC) system. To get an optimum algorithm, three methods namely conventional-Zeigler-Nichols, linear-golden section search (GSS) and stochastic-particle swarm optimization (PSO) are applied to control of 12 pulse VSC based HVDC system and simulation results are presented to show the best among the three. The performance results are obtained under various dynamic conditions such as load perturbation, non-linear load condition, and voltage sag, tapped load fault at points-of-common coupling (PCC) and single-line-to ground (SLG) fault at input AC mains. The conventional GSS and PSO algorithm are modified to enhance their performances under dynamic conditions. The results of this study show that modified particle swarm optimization provides the best results in terms of quick response to the dynamic conditions as compared to other optimization methods.

• Structural Engineering and Mechanics
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• v.45 no.2
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• pp.211-232
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• 2013

In this study a multi-objective optimization problem is solved. The objectives used here include simultaneous minimum construction cost in term of sections weight, minimum structural damage using a damage index, and minimum non-structural damage in term of inter-story drift under the applied ground motions. A high-speed and low-error neural network is trained and employed in the process of optimization to estimate the results of non-linear time history analysis. This approach can be utilized for all steel or concrete frame structures. In this study, the optimal design of a planar eccentric braced steel frame is performed with great detail, using the presented multi-objective algorithm with a discrete population and then a moment resisting frame is solved as a supplementary example.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.225-232
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• 2002

Simple genetic algorithm(SGA) has been used to optimize a lot of structural optimization problems because it can optimize non-linear problems and obtain the global solution. But, because of large evolving populations during many generations, it takes a long time to calculate fitness. Therefore this paper applied micro-genetic algorithm(μ -GA) to structural optimization and compared results of μ -GA with results of SGA. Additionally, the Paper applied μ -GA to gate optimization problem for injection molds by using simulation program CAPA.