Disclosed are a D-FNN direct inverse control method and system based on a pruning strategy. By means of hierarchical learning, a more concise structure and shorter learning time can be ensured. A major advantage of the hierarchical learning strategy is to alleviate the oscillation problem in the learning process. When disturbances occur in an external environment, a D-FNN has strong adaptability and robustness, so that the trajectory error can be quickly reduced to close to zero. The D-FNN can automatically generate or delete fuzzy rules according to the importance of the inverse control system and the complexity of the system. The model does not need to be preset during online learning, and an order can be set according to training data for learning adaptively, thereby compensating for non-linear system modeling errors to treat external disturbances. By means of simulation research, the dynamic fuzzy neural network will be applied in many real-time automatic control systems.
D-FNN DIRECT INVERSE CONTROL METHOD AND SYSTEM BASED ON PRUNING STRATEGY