Correlation between stability and energy variations in control strategies for mobile base robot with manipulators subjected to external disturbances

被引:2
作者
Son, Changman [1 ]
机构
[1] DanKook Univ, Sch Elect & Elect Engn, Yongin 16890, South Korea
关键词
Correlation (stability vs; energy variations); system energy function; comparison (computed force; torque vs; adaptive compensators); external disturbances (base tipping motion; load mass changes);
D O I
10.1177/1729881419844656
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
The correlation between stability and energy variations in control strategies for a mobile base robot with manipulators subjected to external disturbances is introduced. The correlation results can be used to stabilize and control problems when a mobile base robot is subjected to various types of external disturbances. This is because different robot system energy values display different stability distribution curves. Mobile base robot stability based on varying system's energy is described. Two control strategies, computed force/torque and adaptive compensators, are applied to minimize uncertainties accompanied by the robot's movements. The two compensators are then compared by simulating applied external disturbances, such as mobile base tipping motion and load mass changes on the robot end-effector. A comprehensive comparison with other methods is also described. The proposed technique is a useful tool in the maintenance of the degree of control and stability of the system and has various applications in the mobile robot tasks including choosing and placing operations, maneuvering around the workspace with protruding obstacles on sinuous shape paths, and manufacturing tasks.
引用
收藏
页数:13
相关论文
共 33 条
[1]   A fuzzy approach to redundancy resolution for underwater vehicle-manipulator systems [J].
Antonelli, G ;
Chiaverini, S .
CONTROL ENGINEERING PRACTICE, 2003, 11 (04) :445-452
[2]   ADAPTIVE-CONTROL OF MECHANICAL MANIPULATORS [J].
CRAIG, JJ ;
HSU, P ;
SASTRY, SS .
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 1987, 6 (02) :16-28
[3]   Discrete time control based in neural networks for pendulums [J].
de Jesus Rubio, Jose .
APPLIED SOFT COMPUTING, 2018, 68 :821-832
[4]   Robot Motion Planning in Dynamic, Uncertain Environments [J].
Du Toit, Noel E. ;
Burdick, Joel W. .
IEEE TRANSACTIONS ON ROBOTICS, 2012, 28 (01) :101-115
[5]   PLANNING MOBILE MANIPULATOR MOTIONS CONSIDERING VEHICLE DYNAMIC STABILITY CONSTRAINTS [J].
DUBOWSKY, S ;
VANCE, EE .
PROCEEDINGS - 1989 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOL 1-3, 1989, :1271-1276
[6]  
Feng G., 1999, Adaptive Control Systems
[7]  
Fu KS, 1987, ROBOTICS CONTROL SEN
[8]   A technique for time-jerk optimal planning of robot trajectories [J].
Gasparetto, Alessandro ;
Zanotto, Vanni .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2008, 24 (03) :415-426
[9]   Lifted system iterative learning control applied to an industrial robot [J].
Hakvoort, W. B. J. ;
Aarts, R. G. K. M. ;
van Dijk, J. ;
Jonker, J. B. .
CONTROL ENGINEERING PRACTICE, 2008, 16 (04) :377-391
[10]   Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning [J].
He, Wei ;
Dong, Yiting .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (04) :1174-1186