Neural Network Robot Model with Not-inverted Inertia Matrix
نویسندگان
چکیده
Abstract. The mathematical model of industrial robot is well known for example in the form of Lagrange-Euler equations, Newton-Euler equations or generalized d’Alambert equations. However, these equations require the physical parameters of robot like masses, inertia momentums, etc. that are very hard to measure. In the paper, the method for calculation of Lagrange-Euler robot model using neural networks is presented. The proposed networks structure base on approach where not-inverted inertia matrix is calculated. The model uses control signals as training outputs and has good performance for presented training and testing data.
منابع مشابه
Comparison of Neural Network Robot Models with Not Inverted and Inverted Inertia Matrix
The mathematical model of an industrial robot is usually described in the form of Lagrange-Euler equations, Newton-Euler equations or generalized d’Alambert equations. However, these equations require the physical parameters of a robot that are difficult to obtain. In this paper, two methods for calculation of a Lagrange-Euler model of robot using neural networks are presented and compared. The...
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