Force feedback equipment kinetic parameter estimation algorithm based on particle swarm algorithm

A dynamic parameter, particle swarm algorithm technology, applied in the direction of instrument, adaptive control, control/regulation system, etc., can solve problems such as poor effect and large torque error

Active Publication Date: 2020-05-15
NANCHANG UNIV
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AI Technical Summary

Problems solved by technology

However, the estimated torque error is large, the effect of parameter estimation is poor, and it is easier to fall into local optimum

Method used

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  • Force feedback equipment kinetic parameter estimation algorithm based on particle swarm algorithm
  • Force feedback equipment kinetic parameter estimation algorithm based on particle swarm algorithm
  • Force feedback equipment kinetic parameter estimation algorithm based on particle swarm algorithm

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Experimental program
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Embodiment Construction

[0045] The invention will be further illustrated by the following examples.

[0046] Step 1: Set the ideal trajectory of the joint angle of the force feedback device;

[0047] The force feedback device has 3 rotatable joints J 1 、J 2 、J 3 , the corresponding three joint angles are θ 1 , θ 2 , θ 3 . The ideal motion trajectories for setting the three joint angles are: angle θ 1 (t), θ 2 (t), θ 3 (t), angular velocity angular acceleration details as follows:

[0048] Angle: θ 1 =0.5sint θ 2 =0.5sint θ 3 =0.2sint

[0049] Angular velocity:

[0050] Angular acceleration:

[0051] Step 2: Track the position of the ideal motion trajectory of the joint angle;

[0052] Use the PID control algorithm to control the three joints of the force feedback device to track the ideal trajectory, and obtain the input torque τ of the three joints 1 , τ 2 , τ 3 and angle θ 1 (t), θ 2 (t), θ 3 (t). Using nonlinear tracking-differentiator to get the angular velocity o...

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Abstract

The invention provides a force feedback equipment kinetic parameter estimation algorithm based on a particle swarm algorithm. Specifically, an improved comprehensive learning particle swarm algorithmis used for better estimating the kinetic parameters of the force feedback equipment. The algorithm mainly comprises the following steps: step 1, setting an ideal motion trail of a joint angle of force feedback equipment; step 2, performing position tracking on the ideal motion trail of the joint angle; step 3, sampling a joint angle movement track and an input torque; and step 4, estimating the parameters of the force feedback equipment by utilizing an ICLPSO algorithm. According to the method, four strategies, namely a hierarchical updating strategy, a position learning strategy, a guide particle learning strategy and a global optimal learning strategy, are used, so that the problems of two-step forward and one-step backward and the premature problem in the traditional PSO algorithm areeffectively solved. Compared with a traditional PSO algorithm, the ICLPSO algorithm is higher in convergence speed, the obtained model parameters are more accurate, accurate torque estimation values can be provided for all joints, and the performance of the control algorithm is improved.

Description

technical field [0001] The invention relates to the estimation of parameters of force feedback equipment, in particular to an estimation algorithm of dynamic parameters of force feedback equipment based on particle swarm algorithm. Background technique [0002] Due to the prominent position of haptics in human perception, researchers have tried to introduce haptic characteristics into teleoperated robots, virtual reality and other research fields to provide haptic feedback information for operators in the process of human-computer interaction. For example, when an accident occurs in a nuclear power plant, it is necessary to complete tasks such as detection and maintenance through teleoperated robots. At this time, video surveillance is facing serious interference. The operator can effectively improve the accuracy and reliability of the operation with the help of tactile feedback information. In virtual surgery, doctors conduct real-time surgical simulation training on a virt...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 李春泉何永华杨峰
Owner NANCHANG UNIV
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