摘要
基于流体流理论的网络简化模型,将NSGA-Ⅱ与PGA相结合的复合遗传优化算法应用于PID控制器参数优化,提出了一种多目标PID优化设计方法——在满足系统鲁棒性的前提下,以超调量、上升时间和调整时间最小作为多目标优化的子目标,并用复合遗传算法对其求解。该算法求得的Pareto最优解分布均匀,收敛性和鲁棒性好,根据网络主动队列管理控制系统的要求在解集中选择满意解。仿真结果表明,在大时滞和突发业务流的冲击两种情况下,该方法设计的控制器的动静态性能优于GA、SPSO、QDPSO算法的优化结果。
A simplified network model based on fluid flow theory is derived in this paper,and based on this model,an improved algorithm,that is,composite genetic optimization algorithm of combining NSGA-Ⅱ with PGA is applied to optimize PID controller parameters.A multi-objective PID optimization design method is proposed.When the system robustness is satisfied,the minimum of overshoot,rise time and adjusting time is taken as the sub-object of multi-objective optimization,and through this composite genetic algorithm,the objectives are gained.The Pareto optimal solutions obtained by this algorithm distributes evenly,and has good convergent and robust attributs.According to the requirement of the networked Active Queue Management control system,a satisfying solution from the solution set is chosen.The simulation experimental results show that under the two conditions of large time delay or sudden business flow,the dynamic state and steady state performances of the proposed algorithm are obviously superior to those of the existing GA,SPSO and QDPSO algorithms.
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2010年第4期487-491,507,共6页
Journal of Nanjing University of Science and Technology
基金
国家自然科学基金（60974016）
江苏省自然科学基金（BK2008188）
江苏省“六大人才高峰”资助项目（07-E-13）
江苏省现代教育技术研究“十一.五”规划课题（2007-R-6145）
南通市科技应用研究项目（K2007004）
关键词
主动队列管理
网络拥塞
PID控制
复合遗传算法
active queue management
network congestion
PID control
composite genetic algorithm
