控制理论与应用2024，Vol.41Issue(5)：885-894,10.DOI:10.7641/CTA.2023.20771
神经网络类机理建模下的持续自学习控制
Continuous self-learning control under transformation of neural network into isomorphic eauivalent form of mechanism model
谭天乐 1张万超 2何永宁 2周恒杰2
作者信息
- 1. 上海航天控制技术研究所,上海 201109||上海市空间智能控制技术重点实验室,上海 201109
- 2. 上海航天控制技术研究所,上海 201109
- 折叠
摘要
Abstract
Aiming at the problem of dynamic modeling of unknown and time-varying complex dynamic systems in model-based control,a forward fully connected neural network is used to model the dynamic system with data-driven non mechanism fitting.Through dynamic linearization and normalization/anti normalization data processing,based on the forward propagation algorithm,the topological calculation process of neural network is transformed into isomorphic equivalent expression of dynamic system mechanism model.Combined with model-based prediction and inversion con-trol,a continuous self-learning control method based on the neural network mechanism modeling is proposed,and the interpretability of neural network in dynamic system modeling and control is explored.The simulation results with the manipulator as the control object show that the neural network mechanism model is similar to the mechanism model in form,approximate or equivalent in parameters,and can be used for the qualitative and quantitative analysis of the control quality of the control system.Continuous self-learning control has good dynamic adaptability to nonlinear unknown and time-varying complex systems.关键词
黑箱系统/时变系统/非机理建模/神经网络建模/同构等价表达/模型预测与反演控制/持续自学习控制/机械臂控制Key words
black box system/time varying system/non mechanism modeling/neural network modeling/isomorphic equivalent expression/model prediction and inversion control/continuous self-learning control/manipulator control引用本文复制引用
谭天乐,张万超,何永宁,周恒杰..神经网络类机理建模下的持续自学习控制[J].控制理论与应用,2024,41(5):885-894,10.
