Kane Formulation Based Dynamic Modelling and Neural Network Adaptive Sliding Mode Control for a Redundantly Actuated Parallel Robot

The research object of this paper is a redundantly actuated parallel robot named RAParM-I. First, Kane formulation together with Lagrange multiplier is applied to analyse the rigid body dynamics of RAParM-I parallel robot, and a compact rigid body dynamic model of the parallel robot is obtained. Then, this article designs a kind of adaptive sliding mode controller based on Radial Basis Function (RBF) neural network and exponential reaching law to solve the issue that it is difficult to establish precise dynamic model of real prototype of the robot in practical engineering. This method uses RBF neural network to approximate uncertainties caused by modeling errors. Finally, the trajectory tracking control simulation experiment of the parallel robot under redundant actuation mode is carried out using the presented control scheme, and the results are compared with those of the traditional PD feedback control scheme. The simulation result suggests that the control performance of adaptive sliding mode controller based on RBF neural network and exponential reaching law is obviously better than the PD control. The research of this paper lay a sound foundation for the fabrication and practical application in electronic packaging of the parallel robot in future.