Considering the uncertainty

large delay

large inertia and nonlinear property of the main steam temperature control in thermal power plants

an intelligent PID cascade control system based on several calculation methods such as the particle swarm optimization(PSO)algorithm

the ant colony optimization(ACO)algorithm

and the BP neural network was designed.The PSO algorithm was used to optimize the parameters of the ACO algorithm

information heuristic factorα

and expectations heuristic factorβ.The improved ACO algorithm was employed to optimize the initial weights of the BP neural network.Then the optimized BP neural network algorithm was applied to adjust the PID parameters on-line

thus to realize dynamic control of the main steam temperature.An ultra supercritical 600MW unit was taken as an example to conduct simulation test on the ACO-BP and BP neural network PID cascade control system.The results show that this system outperforms conventional PID control systems in control quality and robustness.