A distributed immune algorithm for learning experience in complex industrial process control

In modern industrial field, some complex processes have multi subprocesses, which are homo-structural variable-parameter systems and have quite a few control parameters. When the subprocesses have nonlinear characteristics, it is difficult to turn these control parameters manually. To solve this problem, a distributed immune algorithm for controlling the complex industrial process is designed on the basis of theory of immune network, which is able to learn control knowledge of different operators online and optimize the existing control knowledge by immune evolution and learning in terms of control results. Experiments and simulations show that it has the ability of distributed learning and its control results are superior to that of the manual. The algorithm can solve the problem of intelligent control for homo-structural variable-parameters systems in complex industrial process.