Neural network sliding mode control of overhead crane in three-dimensional motion mode
The three-dimensional motion mode of overhead crane has higher productivity,but its positioning and anti-swing control is also more challenging.To address this problem,this paper proposes a neural network sliding mode control method based on minimum parameter learning.Firstly,a full-drive dynamics model including mechanical friction and air resistance is established to solve the problem that the system is difficult to design the controller due to the underdrive char-acteristics;then a sliding mode controller based on the exponential convergence law is designed and a minimal parameter learning method based on radial basis functions(RBF)neural network is introduced to approximate the uncertainty model of the system.A rigorous mathematical proof of the stability of the controller is presented.The simulation and experimental results show that the proposed control method can achieve the precise positioning of the crane and the effective suppression of the load swing with and without external disturbances.
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