Robot Adaptive Impedance Control Method Based on Biologically Inspired Neural Network

A neural network and impedance control technology, applied in the direction of program control manipulators, manipulators, manufacturing tools, etc., can solve problems such as the inability to realize real-time precise impedance control of robots, improve adaptive estimation ability, high control accuracy, and ensure global convergence. Effect

Active Publication Date: 2021-10-08
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

[0005] In order to solve the above problems in the prior art, that is, the prior art cannot realize the real-time precise impedance control of the robot in the complex nonlinear system, the present invention provides a robot adaptive impedance control method based on a biologically inspired neural network, The method includes:

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  • Robot Adaptive Impedance Control Method Based on Biologically Inspired Neural Network
  • Robot Adaptive Impedance Control Method Based on Biologically Inspired Neural Network
  • Robot Adaptive Impedance Control Method Based on Biologically Inspired Neural Network

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[0050] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, not to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

[0051] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0052] The present invention provides a kind of robot self-adaptive impedance control method based on biological heuristic neural network, this method designs a kind of self-adaptive impedance control based on biological heuristic network, combines full state feedback and network unk...

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Abstract

The invention belongs to the field of robot control and nonlinear systems, and specifically relates to a method for adaptive impedance control of robots based on biologically inspired neural networks, aiming to solve the problem that the existing technology cannot realize real-time precise control of robots in complex nonlinear systems . The present invention includes: acquiring the initial control torque, expected impedance, and motion trajectory of the system; constructing the dynamic equation and expected impedance model of an n-degree-of-freedom manipulator system containing impedance to obtain the real state and expected state of the system at time t of the robot; based on full state feedback Build an adaptive controller with the bio-inspired network and obtain the control torque at time t+1; cycle through state acquisition, adaptive impedance control, and motion control until the robot arm completes the motion trajectory. The invention combines the biological heuristic network structure and time-delay feedback, adopts the sea-bian algorithm for adjusting the reward value, and the structure combining network estimation and full state feedback, so the system is stable and the control precision is high.

Description

technical field [0001] The invention belongs to the field of robot control and nonlinear systems, and in particular relates to a robot self-adaptive impedance control method based on a biologically inspired neural network. Background technique [0002] Impedance control is used to solve the problem of safe interaction between the robot and the environment. Impedance control of robots is a complex problem in the field of robotic applications. Due to the complexity of the robot structure, there are often a lot of uncertainties in the actual robot system, such as strict nonlinearity, unknown environment, unknown system parameters and so on. [0003] For the problem of system impedance control with uncertainties, traditional methods are mainly divided into two types: feedback control, such as PID control; predictive control, such as model control. A PID controller (proportional-integral-derivative controller) is a common feedback loop component in industrial control applicatio...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B25J9/16
CPCB25J9/161B25J9/163B25J9/1656
Inventor 高洁康二龙乔红
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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