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Neural network mechanical arm dynamics modeling method based on genetic algorithm optimization

A dynamic modeling and genetic algorithm technology, applied in neural learning methods, biological neural network models, genetic laws, etc., can solve problems such as the inability to meet the real-time requirements of computing speed, and achieve the effect of accurate construction

Pending Publication Date: 2022-04-29
QINGDAO UNIV OF SCI & TECH
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Problems solved by technology

The least squares method has high identification accuracy, but it cannot meet the real-time requirements of calculation speed when there is a large amount of data
Therefore, the complexity of the identification process and the amount of calculation have become an important factor affecting the control effect, and it is also a problem to be solved by other existing invention patents

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  • Neural network mechanical arm dynamics modeling method based on genetic algorithm optimization
  • Neural network mechanical arm dynamics modeling method based on genetic algorithm optimization
  • Neural network mechanical arm dynamics modeling method based on genetic algorithm optimization

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Embodiment Construction

[0038] see Figure 1 to Figure 8 As shown, the present invention relates to a dynamic modeling method of a mechanical arm, the modeling method involves a joint motion variable of a mechanical arm and the relationship between the output torque, and the joint motion variables include joint angle, joint velocity and Joint acceleration, the modeling method adopts a cascaded forward neural network structure and a genetic algorithm structure to construct the dynamic model of the mechanical arm, and the described cascaded forward neural network structure includes a plurality of hidden layers, each layer All are connected to its input layer, and the layers are cascaded with each other. By continuously optimizing the parameters in the input-output relationship, the final output deviation converges to the minimum value, and the nonlinear approximation capability is realized;

[0039] The cascaded forward neural network structure includes a plurality of parameters and a training method, ...

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Abstract

The invention relates to a neural network mechanical arm dynamics modeling method based on genetic algorithm optimization, the modeling method relates to a joint motion variable of a mechanical arm and a relation between the joint motion variable and an output torque, and the joint motion variable comprises a joint angle, a joint speed and a joint acceleration. According to the modeling method, a cascade forward neural network structure and a genetic algorithm structure are adopted to construct a dynamic model of the mechanical arm, and the cascade forward neural network structure comprises a plurality of hidden layers. According to the neural network, parameters subjected to primary optimization are trained, the output of a more accurate network model is used as an evaluation index to carry out iterative updating on the evaluation index, deep optimization is carried out on the network parameters, and accurate construction of the network model is realized.

Description

technical field [0001] The invention belongs to the technical field of intelligent robots, and in particular relates to a neural network mechanical arm dynamics modeling method based on genetic algorithm optimization. Background technique [0002] At present, the design of the motion control system of the manipulator is mostly based on the following two forms: the closed-loop control method that only uses kinematics characteristics and the control method that combines dynamics characteristics on this basis. The former has a simple structure and a control method that is easy to implement. However, this method cannot solve the nonlinear time-varying and strong coupling problems of the robot, and it is difficult to ensure that the control system has good static and dynamic performance, and it is easy to cause large control amount deviation and system oscillation. After combining the dynamic characteristics, the dynamic characteristics of the control system can be compensated, ...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06K9/62G06N3/04G06N3/08G06N3/12G06F119/14
CPCG06F30/27G06N3/126G06N3/04G06N3/086G06F2119/14G06F18/214
Inventor 祖丽楠单宝明张明月鞠云鹏周春丽刘志远刘聪
Owner QINGDAO UNIV OF SCI & TECH
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