Study of cascade PID controller parameter optimization based on IMEA for thermal system

The stable constraint conditions of cascade PID controller were obtained by generalized Hermite-Biehler theorem, and an optimal controller was researched. Through mind evolutionary algorithm(MEA), optimal cascade PID controller parameters were found. According to the defects lying in mind evolutionary algorithm, the niche technique is developed to improve MEA in this paper. The swarm is classified and reduced the searching zone, which keeps the swarm diversity and to speed up optimization. Based on parameter optimization and tuning for the main steam temperature cascade control systems in power plants with serious parameter uncertainty, multi-disturbance and large delay, the simulation results show that this method has the advantages of fast optimization and low computational complexity; it is very effective for cascade PID parameter optimization and endues main steam temperature control systems with better control qualities and better system robustness.