Abstract

AbstractPrecise position force control is the core and difficulty of robot technology, especially for robots with redundant degrees of freedom. For example, track-based controls often fail to grind the robot due to the intolerable impact force applied to the end-effector. The main difficulties lie in the coupling of motion and contact forces, redundancy analysis and physical constraints. In this chapter, we propose a new motion force control strategy under the framework of recursive neural network. The tracking error and contact force are described respectively in the orthogonal space. By choosing the minimum joint torque as the secondary task, the control problem is transformed into the QP problem under multi-constraint conditions. In order to obtain real-time optimization of the joint torque relative to the non-convex joint angle, the original QP is reconstructed at the velocity level, and the original objective function is replaced by the time derivative. Then a convergent dynamic neural network is established to solve the improved QP problem online. The robot position control based on recursive neural network is extended to the robot position control based on position force, which opens a new way for the robot to turn from simple control angle to crossover design with convergence and optimality. Numerical results show that the proposed method can realize precise position force control, deal with inequality constraints such as joint angular velocity and torque limitation, and reduce joint torque consumption by 16% on average.

Highlights

  • Redundant manipulators, which have more DOFs than those required to complete a given task, are more flexible than non-redundant ones

  • We propose a position force control scheme based on RNN, which is an important extension of recursive neural network in robot control

  • We focus on position-force control problem for redundant manipulators

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Summary

Introduction

Redundant manipulators, which have more DOFs than those required to complete a given task, are more flexible than non-redundant ones. The motion control of redundant robot based on RNN has obtained good research results, as far as we know, there is no research report on the application of RNN in motion force control of robot On this basis, we propose a position force control scheme based on RNN, which is an important extension of recursive neural network in robot control. As far as we know, this is the first research to study the motion force control of redundant robots in the framework of RNNs. It is worth noting that force sensitive manipulators, such as milling robots and polishing robots, cannot successfully control the use of existing results in RNNs, while the RNN model built in this work can do this. The contribution of this chapter lies in the realization of real-time optimization of joint torque, which is helpful to save energy in industrial applications

Problem Formulation
Joint Torque and Physical Constraints
Optimization Problem Formulation
Reconstruction of Objective Function
Reconstruction of Constraints
Reformulation and Convexification
RNN Design
Stability Analysis
Illustrative Examples
Position-Force Control Without Optimization
Position-Force Control with Optimization
Question and Answer
Findings
Summary
Full Text
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