基于在线学习优化动态模型库的多模型自适应控制

提出一种通过在线学习优化动态模型库的方法. 当子模型数量增长达到设定规模时, 根据动态模型库中各子模型与实际对象的匹配程度, 选取匹配程度最低的旧模型删除, 并添加新建子模型, 从而维持动态模型库在设定规模以内, 解决了在线学习建立动态模型库子模型数量不断增长的问题, 避免了子模型数量的过度增长而引起的计算量增加和性能下降, 并通过仿真证明了算法的有效性.

Multi-model Adaptive Control Based on Online Learning and Optimizing Dynamic Model Bank

A method of optimizing the dynamic model bank that is based on online learning is proposed in this paper. When the number of models reaches the set limitation, the old model, which has the lowest matching degree according to the matching extent between the plant and the models, is deleted for the maintenance of the size of the dynamic model bank, and then the new model is added to the dynamic model bank. Therefore, the problems that arise when the number of models increases and results in a significant grow in computational complexity as well as a decrease in the performance level are solved. The effectiveness of the proposed method is demonstrated by the simulation results.