摘要
针对含有有界扰动和模型参数跳变的离散时间系统,提出基于动态模型库的多模型切换控制方法.在模型参数范围未知情况下,利用在线学习的多模型自适应控制算法自动建立多模型,并对模型库中的子模型进行优化.采用具有积分特性的指标函数作为切换准则,在每一采样时刻根据其最小值来选择与实际系统最接近的模型,并将基于此模型的控制器切换为当前控制器.文中证明了该算法能够保证闭环系统的稳定性和跟踪误差的渐近收敛性.计算机仿真结果表明该算法的有效性.
Multiple-model switching control（MMSC） based on a dynamic model bank is proposed to deal with discretetime systems with bounded disturbance and parameter variations. An online learning algorithm is applied to build multiple models automatically, and optimize the model bank. At each sampling time, a model, which best matches the current dynamics of the system, is chosen; and the corresponding controller is applied to the system based on the switching index function with integral property. The closed-loop system stability is established; and the tracking error is proved to be asymptotically convergent. Simulation results confirm the validity of the proposed method.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2009年第12期1410-1414,共5页
Control Theory & Applications
基金
国家自然科学基金重点资助项目(60835001)
国家自然科学基金资助项目(60904020)
教育部博士点基金资助项目(20070286039
20070286040)
关键词
多模型
切换控制
动态模型库
multiple models
switching control
dynamic model bank
