Neural Network Adaptive Sliding Mode Positioning and Anti-swing Control of Overhead Crane
A neural network adaptive sliding mode controller was proposed for overhead crane with nonlinear and externally disturb-ance.Firstly,the dynamic model of overhead crane was established by Lagrange method.Then,based on the hierarchical sliding mode controller,the adaptive update rate of the radial basis function neural network weights was designed.The radial basis function neural network was used to compensate the uncertain upper bound caused by the nonlinear and external disturbance in the system,and the particle swarm optimization algorithm was used to optimize the parameters of controller.The stability of the system was proved by con-structing a Lyapunov function.Finally,one set of simulation experiments and one set of validation experiments on a established experi-mental platform were designed.The simulation results show that under the influence of nonlinear and externally disturbance,the neural network adaptive sliding mode controller can quickly achieve the positioning of the trolley and the swing suppression of the load,the controller can eliminate the influence of uncertain upper bound on the system.The experimental results also show that the designed con-troller can make the overhead crane reach the control target,and has a certain ability to resist disturbance.
overhead craneneural networkadaptivesliding mode control
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