Identification method of kinetic model of six-degree-of-freedom mechanical arm

Abstract

The invention relates to an identification method of a kinetic model of a six-degree-of-freedom mechanical arm. The identification method comprises the steps of establishing a linear kinetic model of the mechanical arm according to joint friction by utilizing an improved Newton-Euler method first, then introducing a PSO (Particle Swarm Optimization) algorithm, establishing an algorithm for estimating unknown kinetic parameters on the basis of the improved PSO algorithm according to the concept of variation in a genetic algorithm at the same time, finally, using an UR (industrial robot) as an experimental subject, exciting joints of the industrial robot to move by designing an exciting track, and sampling joint movement parameters, so that the estimation of the kinetic parameters of the UR is realized, and the kinetic model is verified according to the moment prediction accuracy. Experiments verify the accuracy and effectiveness of the kinetic model, which is identified by the identification method, of the industrial robot.

Description

technical field [0001] The invention relates to a dynamic model identification method of a robot manipulator. Background technique [0002] In recent years, industrial robots have been widely used in various fields of industrial production, especially in shipbuilding, automobile and aviation manufacturing. As the computing power of computers continues to increase and the cost of computing decreases, advanced control techniques applied to robots become more feasible. However, the dynamic model of the manipulator contains some unknown parameters, and many robot control methods rely on these unknown values, especially in the case of high-speed motion. Therefore, the dynamic parameter identification method is of great significance to the development of model-based control methods. [0003] At present, a standard dynamics identification process of a manipulator includes dynamics modeling, excitation trajectory design, data acquisition, parameter identification, and model verifi...

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Problems solved by technology

[0004] The present invention will overcome the above-mentioned shortcoming of prior art, utilize particle swarm optimization algorithm to be easy to realize, be difficult to fall into the advantage of local optimal when dealing with complicated and large-scale parameter identification problem simultaneously, propose a kind of based on improved particle swarm optimization ( Particle swarm optimization (PSO) algorithm is an industrial robot dynamic parameter identification method, which overcomes the shortcomings of genetic algorithms that are easy to fall into local optimum when dealing with complex and large-scale parameter identification problems, and utilizes their advantages

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