A DNN neural network adaptive control method based on tendon-driven dexterous hand

Abstract

The invention discloses a DNN neural network self-adaptive control method based on a tendon-driven dexterous hand. The method constructs a dynamic relational expression when the end manipulator of an n-joint dexterous hand finger is in contact with the external environment, and then inputs an ideal force to The dexterous hand calculates the difference between the torque of the corresponding joints on the dexterous hand and the actual output torque of the dexterous hand; then based on the PID controller, DNN neural network is added to construct the force control model of the dexterous hand finger end manipulator, and the torque difference is input into the force The control model obtains the first moment; then calculates the sum of the tendon length change caused by the joint change in the dexterous hand and the change rate of the actuator in the end manipulator; and obtains the second moment according to the joint force matrix of the dexterous hand; finally calculates The obtained first torque and second torque are transmitted to the constructed dexterous hand dynamics model, and the actual output force and joint angle of the dexterous hand are obtained, so as to realize the force-position hybrid control of the dexterous hand; the control performance of the dexterous hand control system of the present invention is stable.

Description

technical field [0001] The invention belongs to the field of force-position hybrid control of a robot dexterous hand, in particular to a DNN neural network adaptive control method based on a tendon-driven dexterous hand. Background technique [0002] The research and development of the multi-fingered dexterous hand is attracting more and more attention and attention from robotics scholars from all over the world. In view of the fact that it can realize more manual functions such as fine manipulation, etc., and cooperate with the industrial robot arm to expand the working range of the robot and change the single working mode of the existing industrial robots, it has broad application prospects. [0003] Considering that the dynamics of robotic dexterous hands are nonlinear, robotic manipulators face various uncertainties in practical applications, such as load parameters, internal friction, and external disturbances, etc.; so people consider solutions to achieve precise contr...

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Problems solved by technology

[0004] In the prior art, this is adjusted by using a neural network. The traditional neural network uses an input layer, an output layer, and a hidden layer; the input feature vector reaches the output layer through the hidden layer transformation, and the classification result is obtained in the output layer. , but this structure is powerless for complex functions; and when the dexterous finger is in the presence of load changes and disturbances, its system parameters are time-varying, that is, time-varying and complex nonlinear systems; The linear PID controller with fixed parameters is often difficult to obtain the optimal control effect

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