• Title/Summary/Keyword: Multi-Fuzzy Controller

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The implementation of the Multi-population Genetic Algorithm using Fuzzy Logic Controller

  • Chun, Hyang-Shin;Kwon, Key-Ho
    • Proceedings of the KAIS Fall Conference
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    • 2003.11a
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    • pp.80-83
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    • 2003
  • A Genetic algorithm is a searching algorithm that based on the law of the survival of the fittest. Multi-population Genetic Algorithms are a modified form of genetic algorithm. Therefore, experience with fuzzy logic and genetic algorithm has proven to be that a combination of them can efficiently make up for their own deficiency. The Multi-population Genetic Algorithms independently evolve subpopulations. In this paper, we suggest a new coding method that independently evolves subpopulations using the fuzzy logic controller. The fuzzy logic controller has applied two fuzzy logic controllers that are implemented to adaptively adjust the crossover rate and mutation rate during the optimization process. The migration scheme in the multi-population genetic algorithms using fuzzy logic controllers is tested for a function optimization problem, and compared with other group migration schemes, therefore the groups migration scheme is then performed. The results demonstrate that the migration scheme in the multi-population genetic algorithms using fuzzy logic controller has a much better performance.

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Tracking Control of Servo System using Fuzzy Logic Cross Coupled Controller (퍼지 논리형 상호결합 제어기를 이용한 서보 시스템의 추적제어)

  • 신두진;허욱열
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.50 no.8
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    • pp.361-366
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    • 2001
  • This thesis proposes a fuzzy logic cross coupled controller for a multi axis servo system. The overall control system consists of three elements: the axial position controller, the speed controller, and a fuzzy logic cross coupled controller. In conventional multi axis servo system, the motion of each axis is controlled independently without regard to the motion of other axes, in which the contour error, defined as the shortest distance between the desired and actual contours is compensated only by the position error of each axis. This decoupled control approach may result in degraded contouring performance due to such factors as mismatch of axial dynamics and axial loop gains. In practice, such systems contain many uncertainties, Therefore, the multi axis servo system must receive and evaluate the motion of all axes for a better contouring accuracy. Cross coupled controller utilizes all axis position error information simultaneously to produce accurate contours. However the existing cross coupled controllers cannot overcome friction, backlash and parameter variation. Also, since it is difficult to obtain an accurate mathematical model of multi axis system, here we investigate a fuzzy logic cross coupled controller method. Some simulations and experimental results are presented to illustrate the performance of the proposed controller.

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Design of the flexible switching controller for small PWR core power control with the multi-model

  • Zeng, Wenjie;Jiang, Qingfeng;Du, Shangmian;Hui, Tianyu;Liu, Yinuo;Li, Sha
    • Nuclear Engineering and Technology
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    • v.53 no.3
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    • pp.851-859
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    • 2021
  • Small PWR can be used for power generation and heating. Considering that small PWR has the characteristics of flexible operating conditions and complex operating environment, the controller designed based on single power level is difficult to achieve the ideal control of small PWR in the whole range of core power range. To solve this problem, a flexible switching controller based on fuzzy controller and LQG/LTR controller is designed. Firstly, a core fuzzy multi-model suitable for full power range is established. Then, T-S fuzzy rules are designed to realize the flexible switching between fuzzy controller and LQG/LTR controller. Finally, based on the core power feedback principle, the core flexible switching control system of small PWR is established and simulated. The results show that the flexible switching controller can effectively control the core power of small PWR and the control effect has the advantages of both fuzzy controller and LQG/LTR controller.

PWM DC-AC Converter Regulation using a Multi-Loop Single Input Fuzzy PI Controller

  • Ayob, Shahrin Md.;Azli, Naziha Ahmad;Salam, Zainal
    • Journal of Power Electronics
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    • v.9 no.1
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    • pp.124-131
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    • 2009
  • This paper presents a PWM dc-ac converter regulation using a Single Input Fuzzy PI Controller (SIFPIC). The SIFPIC is derived from the signed distanced method, which is a simplification of a conventional fuzzy controller. The simplification results in a one-dimensional rule table, that allows its control surface to be approximated by a piecewise linear relationship. The controller multi-loop structure is comprised of an outer voltage and an inner current feedback loop. To verify the performance of the SIFPIC, a low power PWM dc-ac converter prototype is constructed and the proposed control algorithm is implemented. The experimental results show that the SIFPIC performance is comparable to a conventional Fuzzy PI controller, but with a much reduced computation time.

Development of Control Algorithm for Effective Simultaneous Control of Multiple MR Dampers (다중 MR 감쇠기의 효과적인 동시제어를 위한 제어알고리즘 개발)

  • Kim, Hyun-Su;Kang, Joo-Won
    • Journal of Korean Association for Spatial Structures
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    • v.13 no.3
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    • pp.91-98
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    • 2013
  • A multi-input single-output (MISO) semi-active control systems were studied by many researchers. For more improved vibration control performance, a structure requires more than one control device. In this paper, multi-input multi-output (MIMO) semi-active fuzzy controller has been proposed for vibration control of seismically excited small-scale buildings. The MIMO fuzzy controller was optimized by multi-objective genetic algorithm. For numerical simulation, five-story example building structure is used and two MR dampers are employed. For comparison purpose, a clipped-optimal control strategy based on acceleration feedback is employed for controlling MR dampers to reduce structural responses due to seismic loads. Numerical simulation results show that the MIMO fuzzy control algorithm can provide superior control performance to the clipped-optimal control algorithm.

Fuzzy logic control of a planar parallel manipulator using multi learning algorithm (다중 학습 알고리듬을 이용한 평면형 병렬 매니퓰레이터의 Fuzzy 논리 제어)

  • Song, Nak-Yun;Cho, Whang
    • Journal of Institute of Control, Robotics and Systems
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    • v.5 no.8
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    • pp.914-922
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    • 1999
  • A study on the improvement of tracking performance of a 3 DOF planar parallel manipulator is performed. A class of adaptive tracking control sheme is designed using self tuning adaptive fuzzy logic control theory. This control sheme is composed of three classical PD controller and a multi learning type self tuning adaptive fuzzy logic controller set. PD controller is tuned roughly by manual setting a priori and fuzzy logic controller is tuned precisely by the gradient descent method for a global solution during run-time, so the proposed control scheme is tuned more rapidly and precisely than the single learning type self tuning adaptive fuzzy logic control sheme for a local solution. The control performance of the proposed algorithm is verified through experiments.

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Fuzzy Formation Controlling Phugoid Model-Based Multi-Agent Systems (장주기모델로 구성된 다개체시스템의 퍼지 군집제어)

  • Moon, Ji Hyun;Lee, Jaejun;Lee, Ho Jae
    • Journal of Institute of Control, Robotics and Systems
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    • v.22 no.7
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    • pp.508-512
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    • 2016
  • This paper discusses a Takagi-Sugeno (T-S) fuzzy controller design problem for a phugoid model-based multi-agent system. The error between the state of a phugoid model and a reference is defined to construct a multi-agent system model. A T-S fuzzy model of the multi-agent system is built by introducing a nonlinear controller. A fuzzy controller is then designed to stabilize the T-S fuzzy model, where the synthesis condition is represented in terms of linear matrix inequalities.

A design of a robust adaptive fuzzy controller globally stabilizing the multi-input nonlinear system with state-dependent uncertainty (상태변수 종속 불확실성이 포함된 다입력 비선형 계통에 대한 전역 안정성이 보장되는 견실한 적응 퍼지 제어기 설계)

  • Park, Young-Hwan;Park, Gwi-Tae
    • Journal of Institute of Control, Robotics and Systems
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    • v.2 no.4
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    • pp.297-305
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    • 1996
  • In this paper a novel robust adaptive fuzzy controller for the nonlinear system with state-dependent uncertainty is proposed. The conventional adaptive fuzzy controller determines the function of state variable bounding the state-dependent uncertain term in the system dynamics on the local state space by off-line calculation. Whereas the proposed method determines that function by the fuzzy inference so that it guarantees the stability of the closed loop system globally on the whole state space. In addition, the method is applicable to the multi-input system. We applied the proposed method to the Burn Control of the Tokamak fusion reactor whose dynamics contains the state-dependent uncertainty and proved the effectiveness of the scheme by using the simulation results.

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Load Frequency Control of Multi-area Power System using Auto-tuning Neuro-Fuzzy Controller (자기조정 뉴로-퍼지제어기를 이용한 다지역 전력시스템의 부하주파수 제어)

  • Jeong, Hyeong-Hwan;Kim, Sang-Hyo;Ju, Seok-Min;Heo, Dong-Ryeol;Lee, Gwon-Sun
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.49 no.3
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    • pp.95-106
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    • 2000
  • The load frequency control of power system is one of important subjects in view of system operation and control. That is even though the rapid load disturbances were applied to the given power system, the stable and reliable power should be supplied to the users, converging unconditionally and rapidly the frequency deviations and the tie-line power flow one on each area into allowable boundary limits. Nonetheless of such needs, if the internal parameter perturbation and the sudden load variation were given, the unstable phenomenal of power system can be often brought out because of the large frequency deviation and the unsuppressible power line one. Therefore, it is desirable to design the robust neuro-fuzzy controller which can stabilize effectively the given power system as soon as possible. In this paper the robust neuro-fuzzy controller was proposed and applied to control of load frequency over multi-area power system. The architecture and algorithm of a designed NFC(Neuro-Fuzzy Controller) were consist of fuzzy controller and neural network for auto tuning of fuzzy controller. The adaptively learned antecedent and consequent parameters of membership functions in fuzzy controller were acquired from the steepest gradient method for error-back propagation algorithm. The performances of the resultant NFC, that is, the steady-state deviations of frequency and tie-line power flow and the related dynamics, were investigated and analyzed in detail by being applied to the load frequency control of multi-area power system, when the perturbations of predetermined internal parameters. Through the simulation results tried variously in this paper for disturbances of internal parameters and external stepwise load stepwise load changes, the superiorities of the proposed NFC in robustness and adaptive rapidity to the conventional controllers were proved.

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Evolutionary Design of a Fuzzy Logic Controller for Multi-Agent Systems

  • Jeong, Il-Kwon;Lee, Ju-Jang
    • 제어로봇시스템학회:학술대회논문집
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    • 1998.10a
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    • pp.507-512
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    • 1998
  • It is an interesting area in the field of artificial intelligence to and an analytic model of cooperative structure for multi-agent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent co-operative behavior: A modified genetic algorithm was applied to automating the discovery of a fuzzy logic controller jot multi-agents playing a pursuit game. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to and the fuzzy logic controller seems to be promising.

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