Dynamics and Noncollocated Model‐Free Position Control for a Space Robot with Multi‐Link Flexible Manipulators

2018 ◽  
Vol 21 (2) ◽  
pp. 714-724 ◽  
Author(s):  
Xinxin Yang ◽  
Shuzhi Sam Ge ◽  
Jinkun Liu
Keyword(s):  
Position Control ◽  
Space Robot ◽  
Flexible Manipulators ◽  
Model Free ◽  
Free Position

A Hybrid Model and Model-Free Position Control for a Reconfigurable Manipulator

2019 ◽  
Vol 24 (2) ◽  
pp. 785-795 ◽  
Author(s):  
Gaofeng Li ◽  
Dezhen Song ◽  
Shan Xu ◽  
Lei Sun ◽  
Jingtai Liu
Keyword(s):  
Hybrid Model ◽  
Position Control ◽  
Model Free ◽  
Free Position

Flexible-Joint Humanoid Balancing Augmentation via Full-State Feedback Variable Impedance Control

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Emmanouil Spyrakos-Papastavridis ◽  
Jian S. Dai
Keyword(s):  
State Feedback ◽  
Position Control ◽  
Impedance Control ◽  
Humanoid Robots ◽  
Flexible Joint ◽  
Model Free ◽  
Floating Base ◽  
Full State ◽  
Full State Feedback ◽  
Series Elastic Actuators

Abstract This paper attempts to address the quandary of flexible-joint humanoid balancing performance augmentation, via the introduction of the Full-State Feedback Variable Impedance Control (FSFVIC), and Model-Free Compliant Floating-base VIC (MCFVIC) schemes. In comparison to rigid-joint humanoid robots, efficient balancing control of compliant bipeds, powered by Series Elastic Actuators (or harmonic drives), requires the design of more sophisticated controllers encapsulating both the motor and underactuated link dynamics. It has been demonstrated that Variable Impedance Control (VIC) can improve robotic interaction performance, albeit by introducing energy-injecting elements that may jeopardize closed-loop stability. To this end, the novel FSFVIC and MCFVIC schemes are proposed, which amalgamate both collocated and non-collocated feedback gains, with power-shaping signals that are capable of preserving the system's stability/passivity during VIC. The FSFVIC and MCFVIC stably modulate the system's collocated state gains to augment balancing performance, in addition to the non-collocated state gains that dictate the position control accuracy. Utilization of arbitrarily low-impedance gains is permitted by both the FSFVIC and MCFVIC schemes propounded herein. An array of experiments involving the COmpliant huMANoid reveals that significant balancing performance amelioration is achievable through online modulation of the full-state feedback gains (VIC), as compared to utilization of invariant impedance control.

Design of Neural Network Controller for Space Robot with Flexible Manipulators

2013 ◽  
Vol 394 ◽  
pp. 393-397
Author(s):  
Jing Ma ◽  
Wen Hui Zhang ◽  
Zhi Hua Zhu
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Control Method ◽  
System Optimization ◽  
Space Robot ◽  
Learning Ability ◽  
Flexible Manipulators ◽  
Neural Network Controller ◽  
Non Linear System ◽  
Self Learning

Neural network self-learning optimization PID control algorithm is put forward for free-floating space robot with flexible manipulators. Firstly, dynamics model of space flexible robot is established, then, neural network with good learning ability is used to approach non-linear system. Optimization algorithm of network weights is designed to speed up the learning speed and the adjustment velocity. Error function is offered by PID controller. The neural network self-learning PID control method can improve the control precision.

SOFT FOOD ROBOTIC PICK AND PLACE OPERATION WITH EMBEDDED CONTROL STRUCTURE

2013 ◽  
Vol 37 (3) ◽  
pp. 273-282
Author(s):  
Shiuh-Jer Huang ◽  
Wei-Han Chang ◽  
Janq-Yann Lin
Keyword(s):  
Position Control ◽  
Control Structure ◽  
Sliding Mode ◽  
Low Cost ◽  
Embedded Control ◽  
Model Free ◽  
Pick And Place ◽  
Position Controller ◽  
Soft Objects ◽  
Pick And Place Operation

Robotic pick-and-place operation is planned for handling hard objects with on-off control gripper. It does not have force monitoring capability for safe grasping soft objects. Current force/torque sensor is too expensive and difficult to implement. Here, a low cost embedded control structure is designed with distributed FPGA robotic position control and gripper Arduino force control kernels. A model-free intelligent fuzzy sliding mode control strategy is employed to design the position controller of each robotic joint and gripper force controller. Experimental results show that the position and force tracking control errors of this robotic system are less than 1 mm and 0.1 N, respectively for pick-and-place different soft foods.

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