On the role of load motion compensation in high-performance force control

Boaventura, Thiago; Focchi, Michele; Frigerio, Marco; Buchli, Jonas; Semini, Claudio; Medrano-Cerda, Gustavo A.; Caldwell, Darwin G.

doi:10.1109/iros.2012.6385953

Cited by 51 publications

(25 citation statements)

References 18 publications

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“…The controller essentially computes desired valve commands given a desired torque, the valve commands representing a desired flow. The controller we implemented is very much inspired from the work in [2], [18], with the difference that we implemented a simpler version where piston velocity feedback has a constant gain. The control law is v = P ID(F des , F ) + Kẋ piston + d…”

Section: B Low-level Torque Controlmentioning

confidence: 99%

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Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics

Herzog

Righetti

Grimminger

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

119

111

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Recently several hierarchical inverse dynamics controllers based on cascades of quadratic programs have been proposed for application on torque controlled robots. They have important theoretical benefits but have never been implemented on a torque controlled robot where model inaccuracies and real-time computation requirements can be problematic. In this contribution we present an experimental evaluation of these algorithms in the context of balance control for a humanoid robot. The presented experiments demonstrate the applicability of the approach under real robot conditions (i.e. model uncertainty, estimation errors, etc). We propose a simplification of the optimization problem that allows us to decrease computation time enough to implement it in a fast torque control loop. We implement a momentum-based balance controller which shows robust performance in face of unknown disturbances, even when the robot is standing on only one foot. In a second experiment, a tracking task is evaluated to demonstrate the performance of the controller with more complicated hierarchies. Our results show that hierarchical inverse dynamics controllers can be used for feedback control of humanoid robots and that momentum-based balance control can be efficiently implemented on a real robot.

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Section: B Low-level Torque Controlmentioning

confidence: 99%

“…We see that we can express the rate of momentum change either as a function of q as in Eq. (18) or as a function of λ as in Eq. (19).…”

Section: B Balance Control Experimentsmentioning

confidence: 99%

Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics

Herzog

Righetti

Grimminger

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

119

111

View full text Add to dashboard Cite

show abstract

“…In this way, it is easily implemented on our system although the servo-motors can only execute position control instructions. At the same time, this method avoids the complex dynamics calculation such as in [37][38][39]. It decouples the control of each leg such that the position control for each joint does not rely on the state of other legs and no multi-leg Jacobian is calculated.…”

Section: Discussionmentioning

confidence: 99%

An adaptive locomotion controller for a hexapod robot: CPG, kinematics and force feedback

Chen

Ren

Wang

et al. 2014

Sci. China Inf. Sci.

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Insects can perform versatile locomotion behaviors such as multiple gaits, adapting to different terrains, fast escaping, etc. However, most of the existing bio-inspired legged robots do not possess such walking ability, especially when they walk on irregular terrains. To tackle this challenge, a central pattern generator (CPG)-based locomotion control methodology is proposed, integrated with a contact force feedback function. In this approach, multiple gaits are produced by the CPG module. After passing through a post-processing circuit and a delay-line, the control signal is fed into six trajectory generators to generate predefined feet trajectories for the six legs. Then, force feedback is employed to adjust these trajectories so as to adapt the robot to rough terrains. Finally the regulated trajectories are sent to inverse kinematics modules such that the position control instructions are generated to control the actuators. In both simulations and real robot experiments, we consistently show that the robot can perform sophisticated walking patterns. What is more, the robot can use the force feedback mechanism to deal with the irregularity in rough terrain. With this mechanism, the stability and adaptability of the robot are enhanced. In conclusion, the CPG-base control is an effective approach for legged robots and the force feedback approach is able to improve walking ability of the robots, especially when they walk on irregular terrains.

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“…[15] Both the measured joint torque and its derivative are used as feedback, which requires an excellent and expensive hardware, while the identification of the joint flexibility requires a difficult experimental process. A similar approach can be applied to hydraulic actuation, also exploiting measurements of joint torques, [16] by compensating for the natural velocity feedback between the load and the actuator. Other works have focused on improving the performances of these controllers by identifying/observing and compensating for friction [17,18].…”

Section: Related Workmentioning

confidence: 99%

Implementing Torque Control with High-Ratio Gear Boxes and Without Joint-Torque Sensors

Prete

Mansard

Ramos

et al. 2016

Int. J. Human. Robot.

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This paper presents a complete framework (estimation, identification and control) for the implementation of joint-torque control on the humanoid robot HRP-2. While torque control has already been implemented on a few humanoid robots, this is one of the first implementations of torque control on a robot that was originally built to be position controlled (iCub[1] and Asimo[2] being the first two, to the best of our knowledge). The challenge comes from both the hardware, which does not include joint-torque sensors and presents large friction due to the high-ratio gear boxes, and the software interface, which only accepts desired joint-angle commands (no motor current/voltage control). The contribution of the paper is to provide a complete methodology that is very likely to be reproduced as most robots are designed to provide only position control capabilities. Additionally, the method is validated by exhaustive experiments on one leg of the robot, including a comparison with the original position controller. We tested the torque controller in both motion control and cartesian force control. The torque control can track better a reference trajectory while using lower values for the feedback gains (up to 25%). Moreover, we verified the quality of the identified motor models by analyzing the contribution of the feedforward terms of our torque controller, which dominate the feedback terms. * Del Prete, Mansard, Ramos and Stasse are with LAAS

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On the role of load motion compensation in high-performance force control

Cited by 51 publications

References 18 publications

Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics

Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics

An adaptive locomotion controller for a hexapod robot: CPG, kinematics and force feedback

Implementing Torque Control with High-Ratio Gear Boxes and Without Joint-Torque Sensors

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