A Novel PID Controller Based on Immune Networks Model Utilizing Vector Distance

To solve conflicts between track and restrain disturbance, robustness and control performance, the immune genetic algorithms (IGA) PID controller based on information entropy is used. Nevertheless, this method needs complex calculation. Its constants are determined by experience and redundant information will slow the speed of convergence. In this paper, a novel PID controller based on immune networks model utilizing vector distance (IGAVD-PID) is proposed. The new method, which calculates the antibody consistence using vector distance and optimizes the parameters of PID, can reduce search space of solution group and avoid redundant calculation information or repeated calculating because the fitness function of solution is corresponded directly with antibodies. Therefore, the convergence of PID is faster. The IGAVD-PID controller is applied into a second order inertial element with time lag system to evaluate the adaptation and robustness of new method. And the simulation results show the new controller improves the performance of convergent speed, global stability and robustness of the PID controller significantly.