Integrated controller for an over-constrained cable driven parallel manipulator: KNTU CDRPM

Vafaei, Alaleh; Aref, Mohammad M.; Taghirad, Hamid D.

doi:10.1109/robot.2010.5509991

Cited by 23 publications

(14 citation statements)

References 24 publications

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“…While a PD controller is the basis of robot control, a feedforward term is commonly included in CDPR control strategies [17], [18] to predict the dynamics of the platform and improve the robot accuracy. Based on the dynamic model, it is possible to add a term to the controller that will anticipate the MP dynamics and compensate for the payload mass along the trajectory.…”

Section: Compensationmentioning

confidence: 99%

Pulleys and Force Sensors Influence on Payload Estimation of Cable-Driven Parallel Robots

Picard

Caro

Claveau

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The subject of this paper is about the use of a suspended Cable-Driven Parallel Robot (CDPR) for pickand-place operations of heavy and heterogeneous objects. The knowledge of the payload mass and its center of mass in realtime is an asset for robust control of the device, which is required to ensure a good stability, especially when the objects have different shapes, sizes and masses. Accordingly, this paper aims at experimentally evaluating the effects of (i) the pulleys modeling and (ii) the use of force sensors for the payload estimation. It turns out that the consideration of the pulleys into the geometric model of the robot improves the mass and center of mass estimations of the payload. A comparison is made between the estimation of cable tensions from force sensors and from motor currents. Finally, a torque controller with a feedforward term for real-time mass compensation is proposed and implemented on a CDPR prototype.

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Section: Compensationmentioning

confidence: 99%

Pulleys and Force Sensors Influence on Payload Estimation of Cable-Driven Parallel Robots

Picard

Caro

Claveau

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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show abstract

“…While a PD controller is the basis of robot control, an feedforward term is commonly included in CDPR control strategies [7,15] to predict the dynamics of the platform and improve the accuracy of the robot.…”

Section: Mass Compensationmentioning

confidence: 99%

“…From the evaluation of the payload mass a new controller is designed, which updates the value of the mass in the feedforward term expressed in Eqn. (15) in order to adapt the controller to a new payload. This time m p describes the estimated total payload, namely the MP and a possible metal plate.…”

Section: Pd Controller With Real-time Mass Estimation and Compensationmentioning

confidence: 99%

Control Solution for a Cable-Driven Parallel Robot With Highly Variable Payload

Picard

Caro

Plestan

et al. 2018

Volume 5B: 42nd Mechanisms and Robotics Conference

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This paper deals with the design of a robust control scheme for a suspended Cable-Driven Parallel Robot (CDPR), composed of eight cables and a moving platform (MP), suitable for pick-and-place operations of heterogeneous objects with different shapes, sizes and masses, up to a total load of 700 kg. Dynamometers measure the force applied by each cable onto the moving-platform and are used to assess the payload mass at any time. In the proposed control solution, each motor of the CDPR is directly driven by a PD torque controller, which takes benefit of the real-time payload estimation in a feedforward term. In order to evaluate its performance, experiments on a typical pick and place trajectory are realized for different payloads. As a result, three control schemes: (i) a Proportional-Derivative (PD) torque controller; (ii) a PD controller with compensation of the MP mass only and (iii) a PD controller with real-time mass estimation and compensation are experimentally compared with respect to their positioning accuracy. It turns out that a good estimation of the payload is obtained in real-time thanks to the dynamometers. Moreover, the higher the payload mass, the more accurate the proposed controller with respect to its two counterparts.

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“…Using the generalized model presented in [18], the interaction forces acting on the system joints were determined by (11). The simulated trajectory for this example was a roll motion trajectory (left to right tilting of the head).…”

Section: B Eight-link 24-dof Neck-inspired Manipulatormentioning

confidence: 99%

Inverse Dynamics of Multilink Cable-Driven Manipulators With the Consideration of Joint Interaction Forces and Moments

2015

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Joint interaction forces and moments play a significant role within multilink cable-driven manipulators (MCDMs). In this paper, the consideration of joint interaction forces and moments in the objective function and constraints specific to the inverse dynamics of MCDMs are considered for the first time. By formulating the relationship between the joint interactions and cable forces, it is shown that the minimization of the joint interactions results in a convex quadratic program. Furthermore, the inclusion of constraints to maintain the stability of unilateral spherical joints results in a quadratically constrained quadratic program. Simulation results of the proposed formulations on two-link eight-cable and eight-link 76-cable manipulators are compared with the traditional two-norm cable force minimization. Results show that the formulations are able to take advantage of the actuation redundancy in considering the joint interactions within the inverse dynamics of MCDMs.

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Integrated controller for an over-constrained cable driven parallel manipulator: KNTU CDRPM

Cited by 23 publications

References 24 publications

Pulleys and Force Sensors Influence on Payload Estimation of Cable-Driven Parallel Robots

Pulleys and Force Sensors Influence on Payload Estimation of Cable-Driven Parallel Robots

Control Solution for a Cable-Driven Parallel Robot With Highly Variable Payload

Inverse Dynamics of Multilink Cable-Driven Manipulators With the Consideration of Joint Interaction Forces and Moments

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