主汽温量子粒子群-PID优化控制

Steam Temperature PID Quantum Particle Swarm Optimization Control

基于基本粒子群(PSO)优化算法容易发生早熟、收敛速度慢,研究了一种改进的粒子群算法——量子粒子群优化算法(QPSO)。将这种算法应用于某电厂主汽温控制系统PID参数优化,得到了最优参数。仿真结果表明,QPSO使得主汽温控制系统具有更好的控制品质,提高了系统的静动态特性。

This paper based on the basic of particle swarm optimization（PSO） algorithm is easy to premature,slow convergence speed,studied a kind of improved particle swarm algorithm—quantum apply quantum-behaved particle swarm optimization（QPSO）.And this algorithm is applied to a main steam temperature control system to optimized the PID parameters,got the Most parameters.Simulation results show that QPSO make main steam temperature control system has better control of quality,improve the system of static and dynamic characteristics.