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Author

Ahmad T. Ramadan

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Conference Jul 2026

An Integrated Mechatronic Framework for Real-Time Trajectory Planning and Workspace Analytics of A 6-DOF Parallel Manipulator

This paper presents a mechatronic framework for generating predictable trajectories and safely executing hardware-in-the-loop operations of a low-cost, rotary-actuated six-degree-of-freedom (6-DOF) parallel manipulator. Unlike traditional prismatic Stewart platforms, this system explicitly accounts for servo-horn geometry and angular deadbands in its kinematic structure. To safely process chaotic, human-generated inputs, a deterministic software pipeline was developed. It uses generalized arc-length parameterization to maintain a constant task-space velocity for any spatial path and 7th-order minimum-jerk polynomials for optimal navigation through multiple waypoints. A vectorized digital twin checks all trajectories before execution, ensuring real-valued inverse kinematics and hardware compliance. The architecture was validated with a thorough analysis of the volumetric workspace. This analysis shows significant kinematic coupling issues and proves that the deterministic time-allocation engine effectively avoids actuator velocity saturation during complex movements.

Kassab Fakhoury, Ahmad T. Ramadan, Obada Alhoora et al. · 0 citations
Conference Jul 2026

Physics-Informed Observable Selection for EDMDc of Nonlinear Mechanical Systems

The Koopman operator enables nonlinear systems to be represented in an approximately linear form for linear control design. However, Extended Dynamic Mode Decomposition with Control (EDMDc) is often limited by the closure problem when generic observables fail to capture the system dynamics. This paper addresses this issue using physicsinformed observables derived from Lie derivatives for nonlinear mechanical systems. The resulting lifted models accurately capture nonlinear dynamics and enable standard LQR design with improved performance over Jacobian-based LQR during large transients. Results further demonstrate that complete physics-informed lifting is essential to minimize closure errors. The main contributions are: (1) analysis of the closure problem, (2) a Lie-derivative-based observable selection framework, and (3) comparison of Koopman-based and Jacobian-based LQR for polynomial nonlinear systems.

Kassab Fakhoury, Ahmad T. Ramadan, Abdalrahman Matar et al. · 0 citations

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