• 제목/요약/키워드: Nonlinear Multi-Objective Optimization

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Multi-objective Genetic Algorithm 을 이용한 얀센 메커니즘의 목표 궤적 트래킹 기반 최적 설계 (Optimized design of Jansen mechanism based on target trajectory tracking method using multi-objective genetic algorithm)

  • 허준;허영건
    • EDISON SW 활용 경진대회 논문집
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    • 제5회(2016년)
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    • pp.455-462
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    • 2016
  • Recently, followed by rapid growth of robotics field, multi-linkage mechanism which can even pass by rough road is getting lots of attention. In this paper, I focused on Jansen mechanism. It's a kinematics object which is named after Dutch artist Theo jansen. Jansen mechanism embraces structure and mechanism which creates locomotion with the combination of the power and simple structure. Theo jansen suggests a 'Holy number'. It's an ideal ratio of leg components length. However, if there's desired gait locomotion, you have to adjust the ratio and the length. But even slight change of the length could cause a big change at the end-point. To solve this problem, I suggest a reverse engineering method to get a ratio of each links by nonlinear optimization with pre-set desired trajectory. First, we converted a movement of the joint of Jansen mechanism to vectors by kinematics analysis of multi-linkage structure. And we showed the trajectory at the end-point. After that, we set desired trajectory which we found most ideal. Then we got the length of the leg components which draws a trajectory as same as trajectory we set, using Multi-objective genetic algorithm toolbox in MATLAB. Result is verified by Edison designer and mSketch. And we analyzed if it could pass through the obstruction which is set dynamically.

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Robust Predictive Feedback Control for Constrained Systems

  • Giovanini, Leonardo;Grimble, Michael
    • International Journal of Control, Automation, and Systems
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    • 제2권4호
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    • pp.407-422
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    • 2004
  • A new method for the design of predictive controllers for SISO systems is presented. The proposed technique allows uncertainties and constraints to be concluded in the design of the control law. The goal is to design, at each sample instant, a predictive feedback control law that minimizes a performance measure and guarantees of constraints are satisfied for a set of models that describes the system to be controlled. The predictive controller consists of a finite horizon parametric-optimization problem with an additional constraint over the manipulated variable behavior. This is an end-constraint based approach that ensures the exponential stability of the closed-loop system. The inclusion of this additional constraint, in the on-line optimization algorithm, enables robust stability properties to be demonstrated for the closed-loop system. This is the case even though constraints and disturbances are present. Finally, simulation results are presented using a nonlinear continuous stirred tank reactor model.

Service Composition Based on Niching Particle Swarm Optimization in Service Overlay Networks

  • Liao, Jianxin;Liu, Yang;Wang, Jingyu;Zhu, Xiaomin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제6권4호
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    • pp.1106-1127
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    • 2012
  • Service oriented architecture (SOA) lends itself to model the application components to coarse-grained services in such a way that the composition of different services could be feasible. Service composition fulfills numerous service requirements by constructing composite applications with various services. As it is the case in many real-world applications, different users have diverse QoS demands issuing for composite applications. In this paper, we present a service composition framework for a typical service overlay network (SON) considering both multiple QoS constraints and load balancing factors. Moreover, a service selection algorithm based on niching technique and particle swarm optimization (PSO) is proposed for the service composition problem. It supports optimization problems with multiple constraints and objective functions, whether linear or nonlinear. Simulation results show that the proposed algorithm results in an acceptable level of efficiency regarding the service composition objective under different circumstances.

A numerical study on optimal FTMD parameters considering soil-structure interaction effects

  • Etedali, Sadegh;Seifi, Mohammad;Akbari, Morteza
    • Geomechanics and Engineering
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    • 제16권5호
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    • pp.527-538
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    • 2018
  • The study on the performance of the nonlinear friction tuned mass dampers (FTMD) for the mitigation of the seismic responses of the structures is a topic that still inspires the efforts of researchers. The present paper aims to carry out a numerical study on the optimum tuning of TMD and FTMD parameters using a multi-objective particle swarm optimization (MOPSO) algorithm including soil-structure interaction (SSI) effects for seismic applications. Considering a 3-story structure, the performances of the optimized TMD and FTMD are compared with the uncontrolled structure for three types of soils and the fixed base state. The simulation results indicate that, unlike TMDs, optimum tuning of FTMD parameters for a large preselected mass ratio may not provide a best and optimum design. For low mass ratios, optimal selection of friction coefficient has an important key to enhance the performance of FTMDs. Consequently, a free parameter search of all FTMD parameters provides a better performance in comparison with considering a preselected mass ratio for FTMD in the optimum design stage of the FTMD. Furthermore, the SSI significant effects on the optimum design of the TMD and FTMD. The simulation results also show that the FTMD provides a better performance in reducing the maximum top floor displacement and acceleration of the building in different soil types. Moreover, the performance of the TMD and FTMD decrease with increasing soil softness, so that ignoring the SSI effects in the design process may give an incorrect and unrealistic estimation of their performance.

미지 입력을 가진 기계 시스템을 위한 비선형 관측기 설계 (Design of a Nonlinear Observer for Mechanical Systems with Unknown Inputs)

  • 송봉섭;이지민
    • 제어로봇시스템학회논문지
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    • 제22권6호
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    • pp.411-416
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    • 2016
  • This paper presents the design methodology of an unknown input observer for Lipschitz nonlinear systems with unknown inputs in the framework of convex optimization. We use an unknown input observer (UIO) to consider both nonlinearity and disturbance. By deriving a sufficient condition for exponential stability in the linear matrix inequality (LMI) form, existence of a stabilizing observer gain matrix of UIO will be assured by checking whether the quadratic stability margin of the error dynamics is greater than the Lipschitz constant or not. If quadratic stability margin is less than a Lipschitz constant, the coordinate transformation may be used to reduce the Lipschitz constant in the new coordinates. Furthermore, to reduce the maximum singular value of the observer gain matrix elements, an object function to minimize it will be optimally designed by modifying its magnitude so that amplification of sensor measurement noise is minimized via multi-objective optimization algorithm. The performance of UIO is compared to a nonlinear observer (Luenberger-like) with an application to a flexible joint robot system considering a change of load and disturbance. Finally, it is validated via simulations that the estimated angular position and velocity provide true values even in the presence of unknown inputs.

볼록최적화에 의거한 구조계와 제어계의 동시최적화 - 근사적 어프로치 - (Simultaneous Optimization of Structure and Control Systems Based on Convex Optimization - An approximate Approach -)

  • 손회수
    • 대한기계학회논문집A
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    • 제27권8호
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    • pp.1353-1362
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    • 2003
  • This paper considers a simultaneous optimization problem of structure and control systems. The problem is generally formulated as a non-convex optimization problem for the design parameters of mechanical structure and controller. Therefore, it is not easy to obtain the global solutions for practical problems. In this paper, we parameterize all design parameters of the mechanical structure such that the parameters work in the control system as decentralized static output feedback gains. Using this parameterization, we have formulated a simultaneous optimization problem in which the design specification is defined by the Η$_2$and Η$\_$$\infty$/ norms of the closed loop transfer function. So as to lead to a convex problem we approximate the nonlinear terms of design parameters to the linear terms. Then, we propose a convex optimization method that is based on linear matrix inequality (LMI). Using this method, we can surely obtain suboptimal solution for the design specification. A numerical example is given to illustrate the effectiveness of the proposed method.

Optimum design of steel frame structures considering construction cost and seismic damage

  • Kaveh, A.;Fahimi-Farzam, M.;Kalateh-Ahani, M.
    • Smart Structures and Systems
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    • 제16권1호
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    • pp.1-26
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    • 2015
  • Minimizing construction cost and reducing seismic damage are two conflicting objectives in the design of any new structure. In the present work, we try to develop a framework in order to solve the optimum performance-based design problem considering the construction cost and the seismic damage of steel moment-frame structures. The Park-Ang damage index is selected as the seismic damage measure because it is one of the most realistic measures of structural damage. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. To improve the time efficiency of the proposed framework, three simplifying strategies are adopted: first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication; second, fitness approximation decreasing the number of fitness function evaluations; third, wavelet decomposition of earthquake record decreasing the number of acceleration points involved in time-history loading. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency's (FEMA) recommended seismic design specifications. The results from numerical application of the proposed framework demonstrate the efficiency of the framework in solving the present multi-objective optimization problem.

반응표면모델에 의한 철도 차량 대차의 탄성조인트 최적설계 (Optimization of the Elastic Joint of Train Bogie Using by Response Surface Model)

  • 박찬경;이광기
    • 대한기계학회논문집A
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    • 제24권3호
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    • pp.661-666
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    • 2000
  • Optimization of the elastic joint of train is performed according to the minimization of ten responses which represent driving safety and ride comfort of train and analyzed by using the each response se surface model from stochastic design of experiments. After the each response surface model is constructed, the main effect and sensitivity analyses are successfully performed by 2nd order approximated regression model as described in this paper. We can get the optimal solutions using by nonlinear programming method such as simplex or interval optimization algorithms. The response surface models and the optimization algorithms are used together to obtain the optimal design of the elastic joint of train. the ten 2nd order polynomial response surface models of the three translational stiffness of the elastic joint (design factors) are constructed by using CCD(Central Composite Design) and the multi-objective optimization is also performed by applying min-max and distance minimization techniques of relative target deviation.

Comparative Analysis of Optimization Algorithms and the Effects of Coupling Hedging Rules in Reservoir Operations

  • Kim, Gi Joo;Kim, Young-Oh
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2021년도 학술발표회
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    • pp.206-206
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    • 2021
  • The necessity for appropriate management of water resources infrastructures such as reservoirs, levees, and dikes is increasing due to unexpected hydro-climate irregularities and rising water demands. To meet this need, past studies have focused on advancing theoretical optimization algorithms such as nonlinear programming, dynamic programming (DP), and genetic programming. Yet, the optimally derived theoretical solutions are limited to be directly implemented in making release decisions in the real-world systems for a variety of reasons. This study first aims to comparatively analyze the two prominent optimization methods, DP and evolutionary multi-objective direct policy search (EMODPS), under historical inflow series using K-fold cross validation. A total of six optimization models are formed each with a specific formulation. Then, one of the optimization models was coupled with the actual zone-based hedging rule that has been adopted in practice. The proposed methodology was applied to Boryeong Dam located in South Korea with conflicting objectives between supply and demand. As a result, the EMODPS models demonstrated a better performance than the DP models in terms of proximity to the ideal. Moreover, the incorporation of the real-world policy with the optimal solutions improved in all indices in terms of the supply side, while widening the range of the trade-off between frequency and magnitude measured in the sides of demand. The results from this study once again highlight the necessity of closing the gap between the theoretical solutions with the real-world implementable policies.

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Performance assessment of buildings isolated with S-FBI system under near-fault earthquakes

  • Ozbulut, Osman E.;Silwal, Baikuntha
    • Smart Structures and Systems
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    • 제17권5호
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    • pp.709-724
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    • 2016
  • This study investigates the optimum design parameters of a superelastic friction base isolator (S-FBI) system through a multi-objective genetic algorithm to improve the performance of isolated buildings against near-fault earthquakes. The S-FBI system consists of a flat steel-PTFE sliding bearing and superelastic NiTi shape memory alloy (SMA) cables. Sliding bearing limits the transfer of shear across the isolation interface and provides damping from sliding friction. SMA cables provide restoring force capability to the isolation system together with additional damping characteristics. A three-story building is modeled with S-FBI isolation system. Multiple-objective numerical optimization that simultaneously minimizes isolation-level displacements and superstructure response is carried out with a genetic algorithm in order to optimize S-FBI system. Nonlinear time history analyses of the building with optimal S-FBI system are performed. A set of 20 near-fault ground motion records are used in numerical simulations. Results show that S-FBI system successfully control response of the buildings against near-fault earthquakes without sacrificing in isolation efficacy and producing large isolation-level deformations.