Improved Swarm Intelligence Algorithm And Application In The PID Controller Parameters Setting

PID control as one of the earliest control strategy, is the basic guarantee of normal operation in the actual industrial production.With the rapid development of computer technology and applying of artificial intelligence technology to the automatic control, many kinds of intelligent setting method for PID controller parameters has appeared. Swarm intelligence as a new evolutionary computation method, has been widely used, because of characteristics that self-organization, parallelism, distributed, easy implementation and robustness. In this article, improved swarm intelligence algorithms are applied to parameters setting PID controller. Overview of research content is as follows:A new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance(RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird’s nests location. The learning and updating strategy of the worst frog in the shuffled frog leaping algorithm(SFLA) and part of differential evolution(DE) thought are introduced into the construction of the disturbance factor. The repeat-cycle asymptotic mode is to narrow the disturbance scope based on the last disturbance results and then go on the next disturbance. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, The results show that the improved cuckoo search algorithm has a better search performance.Mathematical model of electroslag remelting(RSR) process is establish based on its technique features and dynamic characteristics. A new multivariable self-tuning PID controller tuned optimally by an improved cuckoo search algorithm is proposed to control the two-input-two output(TITO) ESR process. For solving the problem that the search space size and scope can not be sure, in this paper, a self-tuning dynamic search space is put forward. Finally, the new searching mechanism cuckoo search algorithm with self-tuning dynamic search space(DMCS) is applied to decoupling control strategy of multivariable self-tuning PID controller. The experiments results show that the proposed control strategy has an excellent control quality.A grey wolf optimization algorithm with evolution and elimination mechanism is proposed in this paper. The biological evolution and the "survival of the fittest" principle of biological updateing of nature are added to the basic wolf algorithm. differential evolution(DE) is chose as the evolutionary pattern of wolves. In order to reduce the probability of the algorithm falling into local optimum, update wolf pack according to the "survival of the fittest" principle. Finally, 12 benchmark functions are adopted to carry out simulation experiments. The experimental results show that the improved grey wolf algorithm has better convergence speed and optimization accuracy.Shell-and-tube condenser is a heat exchanger for cooling steam with high temperature and pressure, which is one of the main heat exchange equipment in thermal, nuclear and marine power plant. Based on the lumped parameter modeling method, the dynamic mathematical model of the simplified steam condenser is established. Then the pressure PI control system of steam condenser is designed. grey wolf optimization(GWO) algorithm is used to realize the fine tuning of PI controller parameters. Simulation results show that GWO algorithm has better control performance.