摘要
目的研究变风量空调系统温度-风量PID控制器的整定方法,利用改进粒子群算法的特点设计一种稳定、高效的自适应控制器.方法以PSO-CF(带收缩因子的PSO)PID控制方法的整定结果作为参考,在PSO-CF算法中用一个差分向量扰乱粒子的认知能力,再根据粒子群的演化规则自动完成最优控制.结果采用DPSO-CF(扰乱认知能力的带收缩因子的粒子群)PID自适应控制器时,系统的调节时间约为PSO-CF粒子群PID控制方法的30%,超调量减少了约75%.当系统加入扰动时,相比带收缩因子的PSO,扰乱认知能力的带收缩因子的粒子群PID自适应控制器的调节时间少,超调量小,系统控制品质得到了较大的改善.结论改进的算法不仅具有良好的鲁棒性,而且还有良好的收敛性.采用上述自适应控制器后,整个系统体现了良好的动态性能及较强的鲁棒性.
Aiming at studying the tuning method of the temperature of VAV air-conditioning system-airflow PID controller,we used the characteristics of improved particle swarm optimization to design a more stable and efficient adaptive controller.Taking setting value results of PSO-CF（PSO with a shrinkage factor）PID control method as reference,the researchers used a vector differential cognitive ability to disrupt the particle in the PSO-CF algorithm,to auto-complete optimal control based on the evolution rules of particle swarm.The results reached that,with the DPSO-CF（disrupt cognitive PSO with a shrinkage factor） PID adaptive controller,the systematical adjustment time was about 30% of PID control method with PSO-CF particle swarm,and the overshoot decreasing by about 75%.When adding the disturbance to the system,compared with a shrinkage factor PSO,PID adaptive controller of disrupting cognitive PSO with a shrinkage factor cost less time,small overshoot,and systematic quality control had been improved significantly as well.The simu-lation results show the improved algorithm not only had good robustness,but also good convergence.Adopting the above adaptive controller,the entire system reflected good dynamic performance and robustness.
出处
《沈阳建筑大学学报（自然科学版）》
CAS
北大核心
2010年第3期592-598,共7页
Journal of Shenyang Jianzhu University：Natural Science
基金
住房和城乡建设部科研项目(2007-KP-29)
