Study on Non-modeled Error Compensation Algorithm for Parallel Robots Based on Extreme Learning Machine
Aiming at the problem that the precision performance of industrial parallel robot limits its application in high-end manufacturing fields such as precision assembly,taking a six-degree-of-freedom Stewart parallel robot as the research object,a model-free error compensation algorithm based on the extreme learning machine was proposed,which aimed to effectively improve the positional accuracy of the six-degree-of-freedom Stewart parallel robot.Firstly,the coordinate system framework of the six-de-gree-of-freedom Stewart parallel robot was established.Secondly,the model-free error compensation algorithm based on the ex-treme learning machine was proposed.The network framework of the extreme learning machine and the neuron number in the hidden layer were determined through experimental analysis.The experimental results show that the proposed model-free error compensation algorithm based on the extreme learning machine effectively reduces the maximal positional and pose errors of the six-degree-of-freedom Stewart parallel robot by 90.96%and 97.01%.The accuracy improvement is better than that of the tradi-tional BP neural system and CSBP neural network.
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