Tuning of a PID controller using an artificial immune network model and local fuzzy set

Abstract

Suggests that immune algorithms can be used effectively for the tuning of PID control structures for nonlinear processes. The controller's attribute behavior mechanism in the plant and the artificial immune system have certain similarities, since both systems deal with various attribute inputs and outputs through interactions among multiple-attribute modules. Since antibodies communicate with each other among different species of antibodies/B-cells through the stimulation and suppression chains among the antibodies that form a large-scale network, the artificial immune network system always has a new parallel decentralized processing mechanism for various situations. In addition to that, the structure of the network is not fixed but varies continuously, i.e. the artificial immune network flexibly self-organizes according to dynamic changes of the external environment. On the other hand, a number of tuning methods on the PID controller have been considered but, with only the P, I and D parameters, it is very difficult to control a plant with complex dynamics, such as large dead time, inverse response and highly nonlinear characteristics. An possibility of applying the flexible arbitration abilities of an artificial immune network has been suggested for the PID controller tuning. Simulation results reveal that immune network algorithms are effective for searching for optimal control against disturbances.