Compliant force control method based on fuzzy reinforced learning for mechanical arm

Abstract

The invention discloses a compliant force control method based on fuzzy reinforced learning for a mechanical arm. A fuzzy reinforced learning algorithm is adopted, a real-time adjustment strategy for admittance parameters is trained in a manner of online learning, and a motor is controlled by the converged variable-admittance control strategy according to an external moment applied by an operator, and the current joint speed and acceleration to actively comply with a control intention of the operator, so as to complete an active following task of the mechanical arm, without the need of establishing corresponding task and environment model, so that a higher convergence speed and a stable actual effect are achieved. The method is capable of remarkably lowering the working intensity of the operator and improving the location accuracy, and conducive to reduce the structural dimension and the dead weight of the mechanical arm; a haptic human-machine interaction model is capable of greatly responding to the control intention of the operator, and high in self-adaptive capacity, so that haptic human-machine interaction experience is smoother and more natural, and more similar to haptic interaction experience during operation for an actual object in daily life.

Description

Technical field: [0001] The invention belongs to the technical field of man-machine interactive control, and specifically relates to a method for controlling compliance of a mechanical arm based on fuzzy reinforcement learning. Background technique: [0002] Before performing robot-assisted minimally invasive surgery, medical staff need to formulate a corresponding surgical plan according to the individual characteristics of the patient, select the incision position for minimally invasive surgery, and set the initial posture of each robotic arm accordingly. During the execution, it is necessary to drag each manipulator to the minimally invasive incision position and manually adjust the joint angle of the surgical arm, that is, the operator directly applies external force to the manipulator, and adjusts the position and posture of each link of the manipulator according to the operation intention . Generally, a manipulator uses a reducer as the transmission link of the joint power...

AI Technical Summary

Problems solved by technology

The current active compliance control method uses the method of installing a force sensor at the end of the manipulator to control the position movement of the end effector of the manipulator in the Cartesian space. It often focuses on the position trajectory of the end tool rather than the attitude adjustment, coupled with the fixed force interaction. The position is not convenient for the independent adjustment of the posture of the connecting rod of the manipulator, so it is not suitable for the active positioning requirements of minimally invasive surgical robots

In addition, this type of method also has certain problems. If a fixed control parameter model is used, it is difficult to take into account the control accuracy and operating experience. If a variable control parameter model is used, it is difficult to ensure the suppleness and smoothness of human-computer interaction.

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