In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied to the driving control of the autonomous guided vehicle(AGV). When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged by the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined through this off-line manner, these parameters are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted more accurately through the on-line fine tuning. The experiment for the control of steering and speed of AGV is performed. The results show that the proposed controller provides better performances than other conventional controllers.

In the biological immunity, the immune system of organisms regulates the antibody and T-cells to protect the attack from the foreign materials which are virus, germ cell, and other antigens, and supports their stable state. It has similar characteristics that has the adaptation and robustness to overcome disturbances and to control the plant of engineering application. In this paper, we build a model of the T-cell regulated immune response mechanism. We have also designed an immune response controller(IRC) focusing on the T-cell regulated immune response of the biological immune system that include both a help part to control the response and a suppress part to adjust system stabilization effect. We show some computer simulation to control the vibration of building structure system with strong wind forces excitation also demonstrate the efficiency of the proposed controller for applying a practical system even with existing nonlinear terms.