Latest Papers

ASME Journal of Mechanisms and Robotics

  • Stable Inverse Dynamics for Feedforward Control of Nonminimum-Phase Underactuated Systems
    on January 25, 2023 at 12:00 am

    AbstractAn enhanced inverse dynamics approach is here presented for feedforward control of underactuated multibody systems, such as mechanisms or robots where the number of independent actuators is smaller than the number of degrees of freedom. The method exploits the concept of partitioning the independent coordinates into actuated and unactuated ones (through a QR-decomposition) and of linearly combined output, to obtain the internal dynamics of the nonminimum-phase system and then to stabilize it through proper output redefinition. Then, the exact algebraic model of the actuated sub-system is inverted, leading to the desired control forces with just minor approximations and no need for pre-actuation. The effectiveness of the proposed approach is assessed by three numerical test cases, by comparing it with some meaningful benchmarks taken from the literature. Finally, experimental verification through an underactuated robotic arm with two degrees of freedom is performed.

Efficient Model-Free Calibration of a 5-Degree of Freedom Hybrid Robot


The pose accuracy is a crucial issue that limits the application of hybrid robots. The model-free calibration instead of complex error modeling is investigated to improve the pose accuracy of a 5-degrees-of-freedom (DOF) hybrid robot efficiently. To overcome the difficult problem of model-free calibration in high-dimension joint space that the required measurement data for accurate prediction increase exponentially, a dimensionality reduction method is proposed to decompose high-dimension joint space into two low-dimension subspaces. Then the pose errors can be respectively measured in two subspaces based on the calibrated standard poses to train their corresponding pose error predicators. The standard poses ensure the measured pose errors in two subspaces do not affect each other. Thus, a merging operation obtained by kinematic analysis can finally merge the predicted pose errors of two subspaces into the complete pose error. The error predicators established by several regression methods including artificial neural network, extreme learning machine (ELM) and Twin Gaussian process regression are compared on multi aspects, and ELM stands out among them due to its outstanding prediction accuracy, good anti-noise ability, and low training data requirements. In addition, different representations of pose and pose error are adopted at different calibration stages to deal with the influence of parasitic motion of hybrid robot for the implementation of proposed calibration method. The compensation experiment is executed and the results show that position and orientation errors are reduced by 92.4% and 88.2% on average after calibration and the pose accuracy can meet application requirements.

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