• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.92-95
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• 1999

The design problem of the control system is the ability to synthesize controller that achieve robust stability and robust performance. The paper explains the Finite Inclusions Theorem (FIT) by the procedure namely FIT synthesis. It is developed for synthesizing robustly stabilizing controller for parametrically uncertain system. The fundamental problem in the study of parametrically uncertain system is to determine whether or not all the polynomials in a given family of characteristic polynomials is Hurwitz i.e., all their roots lie in the open left-half plane. By FIT it can prove a polynomial is Hurwitz from only approximate knowledge of the polynomial's phase at finitely many points along the imaginary axis. An example shows the simplicity of using the FIT synthesis to directly search for robust controller of parametrically uncertain system by way of solving a sequence of systems of linear inequalities. The systems of inequalities are solved via the projection method which is an elegantly simple technique fur solving (finite or infinite) systems of convex inequalities in an arbitrary Hilbert space. Results from example show that the controller synthesized by FIT synthesis is better than by H$\sub$$\infty$/ synthesis with parametrically uncertain system as well as satisfied the objectives for a considerably larger range of uncertainty.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.636-642
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• 2015

This paper presents an application of heuristic harmony search (HS) optimization algorithm for the identification of linear continuous time-delay system from step response. Identification model is first-order plus dead time (FOPDT), which describes a linear monotonic process quite well in most chemical processes and HAVC process and is often sufficient for PID controller tuning. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the identification method has been demonstrated through a number of simulation examples.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.5
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• pp.430-442
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• 2013

This paper presents a fast multi-reference frame integer motion estimator for H.264/AVC. The proposed system uses the previously proposed fast multi-reference frame algorithm. The previously proposed algorithm executes a full search area motion estimation in reference frames 0 and 1. After that, the search areas of motion estimation in reference frames 2, 3 and 4 are minimized by a linear relationship between the motion vector and the distances from the current frame to the reference frames. For hardware implementation, the modified algorithm optimizes the search area, reduces the overlapping search area and modifies a division equation. Because the search area is reduced, the amount of computation is reduced by 58.7%. In experimental results, the modified algorithm shows an increase of bit-rate in 0.36% when compared with the five reference frame standard. The pipeline structure and the memory controller are also adopted for real-time video encoding. The proposed system is implemented using 0.13 um CMOS technology, and the gate count is 1089K with 6.50 KB of internal SRAM. It can encode a Full HD video ($1920{\times}1080P@30Hz$) in real-time at a 135 MHz clock speed with 5 reference frames.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.404-408
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• 2001

This paper deals with speed control of induction motors with a gain tuning based on simple Genetic Algorithms, which are search algorithms based on the mechanics of natual selection and genetics. Based on the designed control system structure, the indirect vector control system of induction motors is simulated. The simulation results show that the system has a strong robust to the parameter variation and is insensitive to the load disturbance. Thus, the proposed PI controller based on genetic algorithms is superior to manually tuned classical PI controller in improving the speed control performance of induction motors.

• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.222-227
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• 2008

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.2
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• pp.99-104
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• 2002

In this paper, we construct a hybrid intelligent controller based on a fusion scheme of GA(Genetic Algorithm) and FCM(Fuzzy C-Means) clustering-based ANFIS(Adaptive Neuro-Fuzzy Inference System). In the structure identification, a set of fuzzy rules are generated for a given criterion by FCM clustering algorithm. In the parameter identification, premise parameters are optimally searched by adaptive GA. On the other hand, consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. Finally, we applied the proposed method to the truck backer-upper control and obtained a better performance than previous works.

• ETRI Journal
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• v.39 no.3
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• pp.364-372
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• 2017

For the reliability of controllers in a software defined network (SDN), a dynamic and self-learning schedule method (DSL) is proposed. This method is original and easy to deploy, and optimizes the combination of multiple controllers. First, we summarize multiple controllers' combinations and schedule problems in an SDN and analyze its reliability. Then, we introduce the architecture of the schedule method and evaluate multi-controller reliability, the DSL method, and its optimized solution. By continually and statistically learning the information about controller reliability, this method treats it as a metric to schedule controllers. Finally, we compare and test the method using a given testing scenario based on an SDN network simulator. The experiment results show that the DSL method can significantly improve the total reliability of an SDN compared with a random schedule, and the proposed optimization algorithm has higher efficiency than an exhaustive search.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.117-120
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• 2003

A method for PID controller tuning based on process models for unstable processes was introduced. The optimal. proportional and derivative gains of the AMBs were determined by the tuning method and utilized for the chatter stability analysis in order to search for the chatter-free cutting region. The stability analysis results showed that the optimal gains give wider chatter-free cutting region, and as a result the proposed tuning method was confirmed to be an effective tuning method for determining the optimal gains of the AMBs.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.126-129
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• 1997

This paper propose a new hybrid genetic algorithm for auto-tunig auzzy controller improving the performance. In general, fuzzy controller used pre-determine d moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controller, using hybrid genetic algorithms. The object of the proposed algorithm is to promote search efficiency by overcoming a premature convergence of genetic algorithms. Hybrid genetic algorithm is based on genetic algorithm and modified gradient method. Simulation results verify the validity of the presented method.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.404-406
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• 1998

In this paper, we propose a scaling factor tuning method to improve the performance of fuzzy controller. Tuning rules and reasoning are utilized on-line to determine the scaling factors based on absolute value of the error and its difference. A adaptive evolutionary computation (AEC) is used to search for the optimal tuning rules that will maximize the fitness function. Finally, the proposed fuzzy controller is applied to the angular stabilization of an inverted pendulum.