Adaptive Control Based On Neural Network

摘要

In this paper, the adaptive control based on neural network is studied. Firstly, a neural network based adaptive robust tracking control design is proposed for robotic systems under the existence of uncertainties. In this proposed control strategy, the NN is used to identify the modeling uncertainties, and then the disadvantageous effects caused by neural network approximating error and external disturbances in robotic system are counteracted by robust controller. Especially the proposed control strategy is designed based on HJI inequation theorem to overcome the approximation error of the neural network bounded issue. Simulation results show that proposed control strategy is effective and has better performance than traditional robust control strategy. Secondly, an RFNN for realizing fuzzy inference using the dynamic fuzzy rules is proposed. The proposed RFNN consists of four layers and the feedback connections are added in first layer. The proposed RFNN can be used for the identification and control of dynamic system. For identification, RFNN only needs the current inputs and most recent outputs of system as its inputs. For control, two RFNNs are used to constitute an adaptive control system, one is used as identifier (RFNNI) and another is used as controller (RFNNC). Also to prove the proposed RFNN and control strategy robust, it is used to control the robot manipulator and simulation results verified their effectiveness.