Extension of the divide-and-conquer algorithm for the efficient inverse dynamics analysis of multibody systems

Cameron Kingsley, Mohammad Poursina

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


This paper presents a new mathematical framework to extend the Generalized Divide-and-Conquer Algorithm (GDCA) for the inverse dynamics analysis of fully actuated constrained multibody systems. Inverse-GDCA (iGDCA) is a highly parallelizable method which does not create the mass and Jacobian matrices of the entire system. In this technique, generalized driving forces and constraint loads due to kinematic pairs are clearly and separately differentiated from each other in the equations of motion. As such, it can be easily used for control scheme purposes. iGDCA works based on a series of recursive assembly and disassembly passes to form and solve the equations governing the inverse dynamics of the system. Herein, the mathematical formulations to efficiently combine the dynamics of consecutive bodies in the assembly pass for the purpose of inverse dynamics analysis are presented. This is followed by generating the disassembly pass algorithm to efficiently compute generalized actuating forces. Furthermore, this paper presents necessary mathematical formulations to efficiently treat the inverse dynamics of multibody systems involving kinematic loops with various active and passive boundary conditions. This is followed by the design of a new strategy to efficiently perform the assembly–disassembly pass in these complex systems while avoiding unnecessary computations. Finally, the presented method is applied to selected open-chain and closed-chain multibody systems.

Original languageEnglish (US)
Pages (from-to)145-167
Number of pages23
JournalMultibody System Dynamics
Issue number2
StatePublished - Feb 1 2018


  • Constrained multibody system
  • Control
  • Divide-and-conquer algorithm
  • Fully actuated systems
  • Generalized driving force
  • Inverse dynamics

ASJC Scopus subject areas

  • Modeling and Simulation
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications
  • Control and Optimization


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