• Title, Summary, Keyword: 적응 PID 제어기

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Formation Control of Mobile Robots using Adaptive PID Controller (적응 PID 제어기를 이용한 이동로봇의 군집제어)

  • Park, Jin-Hyun;Choi, Young-Kiu
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.11
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    • pp.2554-2561
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    • 2015
  • In this paper, we strengthen the advantages of a simple PID controller as a study on the formation control of mobile robots and propose an adaptive PID controller with robust performance at the dynamics characteristics of following robot. Simulation studies show that the adaptive PID controller has better keeping constant distance and angle such as tracking performance of following robot for the formation control than a conventional PID controller. This is the proposed adaptive PID controller to change the gains is found to represent the best performance. This is able to verify that the performance of the proposed adaptive PID controller is excellent.

Adaptive PID Controller for Nonlinear Systems using Fuzzy Model (퍼지 모델을 이용한 비선형 시스템의 적응 PID 제어기)

  • Kim, Jong-Hua;Lee, Won-Chang;Kang, Geun-Taek
    • Journal of Korean Institute of Intelligent Systems
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    • v.13 no.1
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    • pp.85-90
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    • 2003
  • This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameters of PID controller are adapted using the error. The parameters of TSK fuzzy model also adapted to plant. The proposed algorithm allows designing adaptive PID controller which Is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

Nonlinear Adaptive PID Controller based on a Cell-mediated Immune Response and a Gradient Descent Learning (세포성 면역 반응과 경사감소학습에 의한 비선형 적응 PID 제어기)

  • Park Jin-Hyun;Lee Tae-Hwan;Choi Young-Kiu
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.1
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    • pp.88-95
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    • 2006
  • PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But They we difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PID controller based on a cell-mediated immune response and a gradient descent learning. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

An Adaptive PID Controller Design based on a Gradient Descent Learning (경사 감소 학습에 기초한 적응 PID 제어기 설계)

  • Park Jin-Hyun;Kim Hyun-Duck;Choi Young-Kiu
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.2
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    • pp.276-282
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    • 2006
  • PID controller has been widely used in industry. Because it has a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose an adaptive PID controller based on a gradient descent learning. This algorithm has a simple structure like conventional PID controller and a robustness to system parameters variation and different velocity command. To verify performances of the proposed adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

A controller Design using Immune Feedback Mechanism (인체 면역 피드백 메카니즘을 활용한 제어기 설계)

  • Park, Jin-Hyun;Kim, Hyun-Duck;Choi, Young-Kiu
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • v.9 no.2
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    • pp.701-704
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    • 2005
  • PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But They are difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PID controller based on a cell-mediated immune response and a gradient descent learning. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

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Design of a nonlinear Multivariable Self-Tuning PID Controller based on neural network (신경회로망 기반 비선형 다변수 자기동조 PID 제어기의 설계)

  • Cho, Won-Chul
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.44 no.6
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    • pp.1-10
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    • 2007
  • This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

A study on the PID adaptive position controller using GMDP Neural Network (GMDP 신경망을 이용한 PID 적응 위치 제어기에 관한연구)

  • 추연규;임영도
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.258-263
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    • 1995
  • 본 논문은 일반화된 다중 수상돌기 적 (GMDP : Generalized Multi Dendrite Product) 유닛트 신경망을 이용한 PID 적응 위치제어기를 구성하여 직류 서어보 전동기의 위치제어를 실시간 처리 하였다. 제안한 제어기를 위치제어에 적용시켜 실험한 결과 기존의 MLP 신경망 제어기를 이용한 것 보다도 샘플시간을 줄일 수 있다는 장점으로 정밀한 제어 가 가능하다는 것을 확인할 수 있었다. 학습규칙은 기존의 역전파 학습방법이 GMDP 신경 회로망에 적용되었다.

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Design of PID Controller with Adaptive Neural Network Compensator for Formation Control of Mobile Robots (이동 로봇의 군집 제어를 위한 PID 제어기의 적응 신경 회로망 보상기 설계)

  • Kim, Yong-Baek;Park, Jin-Hyun;Choi, Young-Kiu
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.18 no.3
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    • pp.503-509
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    • 2014
  • In this paper, a PID controller with adaptive neural network compensator is proposed to control the formations of mobile robot. The control system is composed of a kinematic controller based on the leader-following robot and dynamic controller for considering the dynamics of the mobile robot. The dynamic controller is constituted by a PID controller and the adaptive neural network compensator for improving the performance and compensating the change in dynamic characteristics. Simulation results show the performance of the PID controller and the neural network compensator for the circular trajectory and linear trajectory. And it is verified that by improving the performance of a PID controller via the adaptive neural network compensator, the following robot's tracking performance is improved.

Design of Optimized Adaptive PID Control Structures by means of Model Reduction and RLSE (축소모델과 RLSE를 이용한 최적화 적응형 제어구조 설계)

  • Choi, Jeoung-Nae;Cho, Joon-Ho;Hwang, Hyung-Soo
    • Proceedings of the KIEE Conference
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    • pp.2525-2527
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    • 2005
  • 큰 지연시간을 갖는 고차계 시스템에 대하여 일반적으로 적용할 수 있는 PID 제어기의 동조방법중 한 가지 방법으로써 축소모델을 이용하는 방법이 있다. 이 방법은 큰 지연시간을 갖는 고차계 공정을 SOPTD(Second Order Plus Time Delay Model)로 축소를 하여 SOPTD의 고정된 형태의 모델에 대하여 PID 제어기를 동조하는 방법이다. SOPTD로 모델을 축소하는 방법과 최적화 PID 파라미터를 동조하는 방법이 제시되었다. 본 논문에서는 기존의 최적화 PID 제어구조에 RLSE를 추가하여 실시간으로 축소모델의 계수를 보정해주는 최적화 적응형 PID 제어구조를 제안하였고, 기존의 제어구조보다 우수한 적응성을 가짐을 시뮬레이션을 통하여 보였다.

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Efficient Multicasting Mechanism for Mobile Computing Environment (경사 감소 학습을 이용한 적응 PID 제어기)

  • Park, Jin-Hyun;Jun, Hyang-Sig;Choi, Young-Kiu
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • v.9 no.1
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    • pp.289-292
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    • 2005
  • PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose an adaptive PID controller based on a gradient descent learning. This algorithm has a simple structure like conventional PID controller and robustness to system parameters variation. To verify performances of the proposed adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

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