摘要
针对焦炉集气管压力这类多变量强扰动非线性耦合系统,提出了一种基于Multi-Agent system(MAS)的焦炉集气管压力智能多级协调控制系统方案.采用基于Agent单元系统梯级协调体系和基于任务分解的实时Agent的组织与演化机制,通过Agent模态变迁进行模式切换,以适应快速突变环境.在控制Agent中采用Actor-critic强化学习方法,运用TS回归模糊神经网络实现行动和评判模块,使用分布式学习算法对多个Agent协调优化.工程应用表明,提出的控制策略有效地解决了高压氨水大干扰对集气管压力的冲击控制问题.
For the multi-variable nonlinear coupled system with strong disturbance such as the gas pressure of collectors of coke ovens, this paper proposes an intelligent multi-level coordinated control strategy based on multi-agent system. It adopts the multi-level coordination architecture with agent agency and the organization and evolution mechanism based on task decomposition. The system can be switched to different modes using the state change of agents in order to operate in rapidly time-varying environments. The reinforcement learning method is used in Agent learning, the TS type recurrent fuzzy neural network（TSRPNN） is employed to realize the actor-critic elements. The agents in system are optimized coordinately by using the distributed learning algorithm. The real-world application shows that the proposed control strategy has successfully solved the process coordination control problem of the gas pressure of collectors of coke ovens with the strong disturbance produced by high pressure ammonia.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2006年第10期1847-1851,共5页
Acta Electronica Sinica
基金
国家杰出青年科学基金(No.60425310)
湖南教育厅资助项目(No.04C718)
中国包装总公司重点科研项目(No.2005-83)
关键词
焦炉集气管
梯级协调
多智能体
强化学习
TS回归模糊神经网络
gas collectors of coke ovens
cascade coordination
multi-Agent system
reinforcement learning
TS recurrent fuzzy neural network
