Abstract
Developing the analytical model of a multibody system is often the initial step in control and optimization. The analytical model (equations of motion) describes a system's time evolution under specified forcing conditions. Although developing these equations is easy for simple systems, this process becomes more complex for systems composed of multiple bodies. Deriving equations of motion for complex multibody systems requires specialized expertise in multibody dynamics, is time-consuming, and is susceptible to error. To address this issue, this paper presents an open-source, easy-to-use, systematic framework to derive symbolic equations of motion in both Python and MATLAB using the joint coordinate formulation. This formulation results in a set of ordinary differential equations that use the minimum set of coordinates needed to model a system. The symbolic representation provides better insight into the influence of design parameters on system performance, facilitates sensitivity analysis and parameter studies, and supports direct implementation of control and optimization routines. The tool enables numerical simulation for specified parameter sets, is modular for straightforward integration with other tools and libraries, and allows incorporation of hydrodynamics, mooring, and other external forces. The result is a reproducible, extensible pipeline for modeling, simulation, and design of complex multibody systems. The proposed tool is versatile and can be applied to domains such as robotics, control, and design. In addition, we integrated external libraries that provide capabilities for modeling offshore systems such as underwater robots and marine energy converters.
| Original language | American English |
|---|---|
| Number of pages | 30 |
| Journal | Machines |
| Volume | 14 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2026 |
NLR Publication Number
- NLR/JA-5700-98069
Keywords
- control
- dynamics modeling
- external libraries coupling
- joint coordinates method
- marine energy
- multibody dynamics
- open-source software
- optimization
- physics-informed machine learning
- symbolic computation
- wave energy
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