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2412.13638

A Constraint Embedding Approach for Dynamics Modeling of Parallel Kinematic Manipulators with Hybrid Limbs

Andreas Müller

correctmedium confidence
Category
Not specified
Journal tier
Specialist/Solid
Processed
Sep 28, 2025, 12:56 AM

Audit review

The paper explicitly derives the local constraint embedding and the block maps H and Ḣ (eqs. (17)–(20)) and states the submersion-based parameterization for each fundamental cycle, matching the model’s part (a) . The inner Newton loop (Algorithm 2) and the nested/compound IK schemes (Algorithms 1 and 3) in the paper align with the model’s part (b) descriptions . The reduction of the limb EOM via virtual power (yielding M̄̄ = H̄ᵀ M̄ H̄ and C̄̄ = H̄ᵀ(M̄ Ḣ̄ + C̄ H̄)) agrees with part (c) . However, in assembling task-space dynamics, the paper gives C_t = Σ_l F̄ᵀ( C̄̄ F̄ + M̄̄ Ḟ ) + P_pᵀ G_p M_p P_p (eq. (41)) , while the model states C_t = P_pᵀ G_p M_p P_p + Σ_l(F̄ᵀ C̄̄ F̄ − F̄ᵀ M̄̄ F̄ L̇_t), which both changes the sign and drops the required L_t^{-1} factor implied by Ḟ = −L_t^{-1} L̇_t F̄. This is dimensionally inconsistent and contradicts the paper’s formula. Hence the paper’s derivation is correct, and the model’s part (d) is incorrect in the Coriolis/centrifugal term.

Referee report (LaTeX)

\textbf{Recommendation:} minor revisions

\textbf{Journal Tier:} specialist/solid

\textbf{Justification:}

The paper delivers a clean, modular derivation of PKM task-space dynamics using local constraint embedding for complex limbs. The approach separates kinematics (with an efficient nested Newton scheme) from dynamics (virtual-power-based reduction and assembly), enabling parallel evaluation across limbs. The derivations are technically sound, and the numerical illustrations are informative. Minor revisions to spell out regularity and convergence assumptions, and to highlight key identities (e.g., relating Ḟ and L̇\_t), would further strengthen rigor and readability.