Neural Network PID Controller Research Articles

Driving control of an AGV for an automated container terminal using an immunized PID controller based on cell-mediated immunity

In this article, an immunized PID (I-PID) controller using a cell-mediated immune algorithm (CMIA) is proposed to control an autonomous guided vehicle (AGV) more effectively. The proposed controller is based on the specific immune response of the biological immune system that is cell-mediated immunity. Moreover, we designed and manufactured an AGV system for an automated container terminal (ACT), and some experiments are shown to verify the performance of the proposed controller. The tracking error of the AGV driving was investigated for this purpose. As a result, the capabilities of realization and reliability were proved by comparing the response characteristics of the proposed I-PID controllers with those of conventional PID and neural network PID (NNPID) controllers.

면역시스템에 기반한 적응제어기 설계에 관한 연구

In this paper, we proposed two types of adaptive control mechanism which is named HIA(Humoral Immune Algorithm) PID and CMIA(Cell-Mediated Immune Algorithm) controller based on biological immune system under engineering point of view. The HIA PID which has real time control scheme is focused on the humoral immunity and the latter which has the self-tuning mechanism is focused on the T-cell regulated immune response. To verify the performance of the proposed controller, some experiments for the control of AGV which is used for the port automation to carry container without human are performed. The experimental results for the control of steering and speed of an AGV system illustrate the effectiveness of the proposed control scheme. Moreover, in that results, proposed controllers have better performance than other conventional PID controller and intelligent control method which is the NN(neural network) PID controller.

신경회로망-PID복합형제어기를 이용한 직류 전동기의 강인한 속도제어

신경회로망-PID복합형제어기를 이용한 직류 전동기의 강인한 속도제어

Experimental Study of Advanced Controllers for an Underwater Robotic Vehicle Thruster System

ABSTRACTMany underwater robotic vehicles (URVs) use propeller-type thrusters driven by DC motors. The thruster system is known to be nonlinear and time varying. Robust control of the thruster system is crucial to navigate and achieve high performance. This paper presents experimental results of five different thruster control systems based on on-line neural network (NN) control, off-line NN control, fuzzy control, adaptive-learning control and PID control. These control systems were initially designed or trained for the thruster system with no fin. Then each controller was implemented for the thruster system modified with different size fins. The fins changed the system hydrodynamics, especially the drag force. In this paper, each control system is briefly described and its practical advantages and disadvantages are discussed with experimental results.

Fast Neural Network PID Control Algorithm Based on Multi-Step Predictive Index Function

Based on multi-step predictive index function, a fast neural network PID control algorithm is proposed. The algorithm uses multi-step predictive index function to train the weights of neural network PID controller, thereby, the system has the character of predictive control, and has faster dynamic speed than general neural network PID control.