Continuous self-learning control under transformation of neural network into isomorphic eauivalent form of mechanism model
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.
black box systemtime varying systemnon mechanism modelingneural network modelingisomorphic equivalent expressionmodel prediction and inversion controlcontinuous self-learning controlmanipulator control
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