Runge-Kutta type periodic rhythm neural network method capable of resisting periodic noise
A neural network and periodic noise technology, applied to manipulators, program-controlled manipulators, manufacturing tools, etc., can solve problems such as numerical solutions deviating from ideal values, inability to overcome initial errors and error accumulation, and failure of manipulator motion planning
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[0065] The implementation of the present invention will be further described in detail below in conjunction with the embodiments and drawings, but the implementation of the present invention is not limited thereto.
[0066] Such as figure 1 and figure 2 A Runge-Kutta type periodic rhythmic neural network method that can resist periodic noise as shown, comprises the following steps:
[0067] S1. Given the terminal task, the quadratic optimization model uses the weighted sum of the mixed torque and the square of the square of the angle offset as the optimization index on the acceleration layer, and its function is to reduce the external force on the body during the tracking process Torque and joint angle offset, inverse kinematics analysis of the trajectory of the robot arm, the formula of the weighted sum is as follows:
[0068]
[0069]
[0070] Where t is time, σ=0.8 is the weight of torque in the optimization index, θ(t), The joint angle vector, joint angular velo...
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