Torque minimisation of the 2-DOF serial manipulators based on minimum energy consideration and optimum mass redistribution

Arakelian, Vigen; Baron, Jean-Paul Le; Mottu, Pauline

doi:10.1016/j.mechatronics.2010.11.009

Cited by 32 publications

(8 citation statements)

References 32 publications

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“…The trajectory planning of a robotic arm is extremely important in production systems because it allows to carry out all the operations it is programmed to do in less time as possible, increasing the productivity and reducing the production costs [1][2][3]. Moreover, the trajectory planning can be optimized to minimize the distance traveled [4], the energy used [5], avoid obstacles [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…[16 f 5 a f 5 b J max1 π 5 t t 4 1 t 2 5 +16 f 5 a f 5 b J max1 π 5 t 5 1 t 2 5 +16 f 5 a f 5 b J max1 π 5 t t 3 1 t 2 t 2 5 + +24 f 5 a f 5 b J max1 π 5 t 4 1 t 2 t 2 5 +8 f 5 a f 5 b J max1 π 5 t 3 1 t 2 2 t 2 5 +16 f 5 a f 5 b J max1 π 5 t 4 1 t 4 t 2 5 +16 f 5 a f 5 b J max1 π 5 t 3 1 t 2 t 4 t 2 5 + +32 f 5 a f 5 b J max1 π 5 t 4 1 t 3 5 +32 f 5 a f 5 b J max1 π 5 t 3 1 t 2 t 3 5 − 16 f 5 a f 5 b J max2 π 5 t t 2 1 t 4 5 − 16 f 5 a f 5 b J max2 π 5 t 2 1 t 5 5 + +16 f 5 a f 5 b J max1 π 5 t 4 1 t 2 5 t 6 +16 f 5 a f 5 b J max1 π 5 t 3 1 t 2 t 2 5 t 6 − 16 f 5 a f 5 b J max2 π 5 t t…”

mentioning

confidence: 99%

“…cos(2 f b π t) − 6 f 5 a f b J max2 π t2 1 t 5 cos(2 f b π (t + t 5 ))+ +6 f5 a f b J max2 π t 2 1 t 5 cos(2 f b π (t + t 5 +t 6 )) + 6 f 5 a f b J max2 π t 2 1 t 5 cos(2 f b π (t + 2 t 5 +t 6 ))+ − 3 f 2 a f 4 b J max1 π t 1 t2 5 cos(2 π (−f a t 4 +f b (t + 2 t 5 +t 6 )))+ +3 f a f5 b J max1 π t 1 t 2 5 cos(2 π (−f a t 4 +f b (t + 2 t 5 +t 6 )))+ +3 f 2 a f 4 b J max1 π t 1 t 2 5 cos(2 π (f a t 4 +f b (t + 2 t 5 +t 6 )))+ +3 f a f 5 b J max1 π t 1 t 2 5 cos(2 π (f a t 4 +f b (t + 2 t 5 +t 6 )))+ − 3 f 2 a f 4 b J max1 π t 1 t 2 5 cos(2 π (−f a (t 1 +t 4 )+f b (t + 2 t 5 +t 6 )))+ +3 f a f 5 b J max1 π t 1 t 2 5 cos(2 π (−f a (t 1 +t 4 )+f b (t + 2 t 5 +t 6 )))+ +3 f 2 a f 4 b J max1 π t 1 t 2 5 cos(2 π (f a (t 1 +t 4 )+f b (t + 2 t 5 +t 6 )))+ +3 f a f 5 b J max1 π t 1 t 2 5 cos(2 π (f a (t 1 +t 4 )+f b (t + 2 t 5 +t 6 )))+ +3 f 2 a f 4 b J max1 π t 1 t 2 5 cos(2 π (−f a (t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 3 f a f 5 b J max1 π t 1 t 2 5 cos(2 π (−f a (t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 3 f 2 a f 4 b J max1 π t 1 t 2 5 cos(2 π (f a (t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 3 f a f 5 b J max1 π t 1 t 2 5 cos(2 π (f a (t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ +3 f 2 a f 4 b J max1 π t 1 t 2 5 cos(2 π (−f a (2 t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 3 f a f 5 b J max1 π t 1 t 2 5 cos(2 π (−f a (2 t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 3 f 2 a f 4 b J max1 π t 1 t 2 5 cos(2 π (f a (2 t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 3 f a f 5 b J max1 π t 1 t 2 5 cos(2 π (f a (2 t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 6 f 5 a J max2 t 2 1 sin(2 f b π t)+6 f 5 a J max2 t 2 1 sin(2 f b π (t + t 5 ))+6 f 5 a J max2 t 2 1 sin(2 f b π (t + t 5 +t 6 ))+ − 6 f 5 a J max2 t 2 1 sin(2 f b π (t + 2 t 5 +t 6 ))+3 f a f 4 b J max1 t 2 5 sin(2 π (−f at 4 +f b (t + 2 t 5 +t 6 )))+ − 3 f 5 b J max1 t 2 5 sin(2 π (−f a t 4 +f b (t + 2 t 5 +t 6 )))+3 f a f 4 b J max1 t 2 5 sin(2 π (f a t 4 +f b (t + 2 t 5 +t 6 )))+ +3 f 5 b J max1 t 2 5 sin(2 π (f a t 4 +f b (t + 2 t 5 +t 6 )))− 3 f a f 4 b J max1 t 2 5 sin(2 π (−f a (t 1 +t 4 )+f b (t + 2 t 5 +t 6 )))+ +3 f 5 b J max1 t 2 5 sin(2 π (−f a (t 1 +t 4 )+f b (t + 2 t 5 +t 6 )))− 3 f a f 4 b J max1 t 2 5 sin(2 π (f a (t 1 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 3 f 5 b J max1 t 2 5 sin(2 π (f a (t 1 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 3 f a f 4 b J max1 t 2 5 sin(2 π (−f a (t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ +3 f 5 b J max1 t 2 5 sin(2 π (−f a (t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 3 f a f 4 b J max1 t 2 5 sin(2 π (f a (t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 3 f 5 b J max1 t 2 5 sin(2 π (f a (t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ +3 f a f 4 b J max1 t 2 5 sin(2 π (−f a (2 t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ − 3 f 5 b J max1 t 2 5 sin(2 π (−f a (2 t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ +3 f a f 4 b J max1 t 2 5 sin(2 π (f a (2 t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))+ +3 f 5 b J max1 t 2 5 sin(2 π (f a (2 t 1 +t 2 +t 4 )+f b (t + 2 t 5 +t 6 )))]…”

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confidence: 99%

See 2 more Smart Citations

Simulation and Experimental Validation of Novel Trajectory Planning Strategy to Reduce Vibrations and Improve Productivity of Robotic Manipulator

et al. 2020

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This paper aims at investigating vibrational behaviors of the industrial manipulator Racer 7-1.4, designed and manufactured by COMAU S.p.A., with the target of new trajectory planning strategies to improve productivity rate without any loss of positioning accuracy. Starting from the analysis of a 9DoF multi-body system with lumped parameter, the first natural frequency of the robot was calculated in seven reference positions. Then, static and dynamic simulations were run by applying saturated ramp input and large motions to analyze the vibrational behavior of the manipulator. This research underlines that the optimal way to design the robot move is to set its duration at twice a period of free oscillation according to the first vibrational mode. Due to strong analogy of dynamic response of both 1DoF and 9DoF robot models, the closed-form solution of the 1DoF undamped system—featured by natural frequency equal to the first frequency of the 9DoF system—may be successfully adopted by the real-time trajectory planning process to predict residual vibration at move end-condition. This strategy was confirmed by experimental tests, allowing either residual vibration decrease and execution time reduction as well.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Simulation and Experimental Validation of Novel Trajectory Planning Strategy to Reduce Vibrations and Improve Productivity of Robotic Manipulator

et al. 2020

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“…The strategies of robot arm control are classified into two main groups: classical methods include PID control, and nonclassical methods such as learning control [1][2][3][4], adaptive control [5][6] , optimal control [7][8][9][10] and sliding model control [11][12][13][14][15]. However, because of the combinatorial explosion of the states and control action spaces, it is difficult to apply learning control approaches based on a reinforcement learning algorithms to control robot arm.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear tracking control design of a robot arm using robust right coprime factorization and sliding mode approaches

Zheng

Liao

Yin

et al. 2014

Proceedings of the 2014 International Conference on Advanced Mechatronic Systems

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In this paper, a nonlinear tracking control of a robot arm in the presence of uncertainties is proposed by using robust right coprime factorization and sliding mode approaches. In general, there exist unknown modeling errors in measuring structural parameters of the robot arm and external disturbances in real situations. In the present control system design, the effect of the modeling errors and disturbance on the system performance is considered to be uncertainties of the robot arm dynamics. Considering the uncertainties, a nonlinear tracking control to a robot arm in the presence of unknown uncertainties is studied. That is, firstly a nonlinear robust stable control system is designed based on robust right coprime factorization approach. Secondly, a tracking system using sliding mode control based on the improved exponential trending law is proposed to improve tracking performance. Finally, the effectiveness of the designed system is confirmed by simulation results.

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“…Kinematic and dynamic parameters are analyzed using ANNOVA to imitate the real time performance measurement of 2-DOF planar manipulator [5]. Optimal dynamic balancing is formulated by minimization of the root-mean-square value of the input torque of 2DOF serial manipulator and results are simulated using ADAMS software [6]. The dynamic parameters of 2 DOF are estimated and results are validated through simulation [7].…”

Section: Introductionmentioning

confidence: 99%

Performance Measurement and Dynamic Analysis of Two Dof Robotic Arm Manipulator

Patel¹

2013

IJRET

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Forward and inverse kinematic analysis of 2DOF robot is presented to predict singular configurations. Cosine function is used for servo motor simulation of kinematics and dynamics using Pro/Engineer. The significance of joint-2 for reducing internal singularities is highlighted. Performance analysis in terms of condition number, local conditioning index and mobility index is carried out for the manipulator. Dynamic analysis using Lagrangian's and Newton's Euler approach is worked out analytically using MATLAB and results are plotted for their comparison.

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Torque minimisation of the 2-DOF serial manipulators based on minimum energy consideration and optimum mass redistribution

Cited by 32 publications

References 32 publications

Simulation and Experimental Validation of Novel Trajectory Planning Strategy to Reduce Vibrations and Improve Productivity of Robotic Manipulator

Simulation and Experimental Validation of Novel Trajectory Planning Strategy to Reduce Vibrations and Improve Productivity of Robotic Manipulator

Nonlinear tracking control design of a robot arm using robust right coprime factorization and sliding mode approaches

Performance Measurement and Dynamic Analysis of Two Dof Robotic Arm Manipulator

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