Modeling and Dynamic Parameter Identification of the SCHUNK Powerball Robotic Arm

2015 ◽  
Author(s):  
Amirhossein H. Memar ◽  
Ehsan T. Esfahani
Keyword(s):  
Parameter Identification ◽  
Viscous Friction ◽  
Dynamic Parameter ◽  
Human Robot Interaction ◽  
Least Square ◽  
Robotic Arm ◽  
Inverse Dynamic ◽  
Compact Design ◽  
Dynamic Parameter Identification ◽  
Linear Least Square

This paper presents the modeling and dynamic parameter identification of the 6-DoF SCHUNK Powerball LWA 4P robotic arm. Precise positioning, zero backlash and compact design of the joints which integrate two perpendicular axes, make this robot ideal for service robotics applications and human-robot interaction. Due to the significant effect of the lubricant temperature on the behavior of viscous friction in the harmonic drives, a systematic procedure is developed to overcome this problem. A series of experiments have been conducted to model the friction at each joint, then the procedure of identification has been applied based on an inverse dynamic model and linear least-square techniques. Finally, a verification trajectory is executed by the robot to validate the estimated parameters of the system.

scholarly journals Dynamic Parameter Identification of Overactuated Parallel Robots

2015 ◽  
Vol 137 (11) ◽  
Author(s):  
Sébastien Briot ◽  
Sébastien Krut ◽  
Maxime Gautier
Keyword(s):  
Parameter Identification ◽  
A Priori ◽  
Dynamic Parameter ◽  
Parallel Robot ◽  
Parallel Robots ◽  
Current Loop ◽  
Dynamic Identification ◽  
Inverse Dynamic ◽  
Identification Process ◽  
Dynamic Parameter Identification

Offline robot dynamic identification methods are based on the use of the inverse dynamic identification model (IDIM), which calculates the joint forces/torques (estimated as the product of the known control signal (the input reference of the motor current loop) with the joint drive gains) that are linear in relation to the dynamic parameters, and on the use of the linear least squares technique to calculate the parameters (IDIM-LS technique). However, as actuation-redundant parallel robots are overactuated, their IDIM has an infinity of solution for the force/torque prediction, depending on the value of the desired overconstraint that is a priori unknown in the identification process. As a result, the IDIM cannot be used as it is for such a class of parallel robots. This paper proposes a procedure for the dynamic parameter identification of actuation-redundant parallel robots. The procedure takes advantage of two possible modified formulations for the IDIM of actuation-redundant robots that can be used for identification purpose. The modified IDIM formulations project some or all input torques/forces onto the robot bodies, thus leading to a unique solution of the model that can then be used in the identification process. A systematic and straightforward way to compute these modified IDIM is presented. The identification of the inertial parameters of a planar parallel robot with actuation redundancy, the DualV, is then carried out using these modified IDIM. Experimental results show the validity of the methods.

scholarly journals Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

2019 ◽  
Vol 9 (2) ◽  
pp. 324 ◽  
Author(s):  
Fusheng Zha ◽  
Wentao Sheng ◽  
Wei Guo ◽  
Shiyin Qiu ◽  
Jing Deng ◽  
...  
Keyword(s):  
Lower Extremity ◽  
Parameter Identification ◽  
Dynamic Parameter ◽  
Search Space ◽  
Least Square ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Experimental Conditions ◽  
Dynamic Parameter Identification

The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton’s controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO.

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