• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.11
• /
• pp.2554-2561
• /
• 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.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.1
• /
• pp.85-90
• /
• 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.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.2
• /
• pp.276-282
• /
• 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.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.1
• /
• pp.88-95
• /
• 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.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.957-966
• /
• 2023

In this paper, we propose a new real-time adaptive PID temperature control technique. This is a technique that prevents overshoot by introducing a model that represents the control object. To prevent excessive integration that causes overshoot, integral control adjusts the integral gain to track the heat loss of the model in real time. In the conventional PID control, the integration was dependent on proportional control and the gain was fixed to a constant. As a result, applying two gains that mismatch each other could cause excessive overshoot. However, the proposed adaptive control actively eliminates overshoot so that the integral control amount does not always exceed the heat loss. The cause of overshoot in PID control is integration. Basically, proportional control does not cause overshoot. Therefore, according to the proposed technique, adaptive PID control without the need for tuning experiments can be realized.

• Proceedings of the KIEE Conference
• /
• 2005.07d
• /
• pp.2525-2527
• /
• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.2
• /
• pp.701-704
• /
• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1995.10b
• /
• pp.258-263
• /
• 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.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.44 no.6
• /
• pp.1-10
• /
• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.1
• /
• pp.289-292
• /
• 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.