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Planar, Large Excursion Bond Graph Model for Full Suspension Mountain Biking

[+] Author Affiliations
Robin C. Redfield

U. S. Air Force Academy

Paper No. IMECE2005-81334, pp. 1157-1167; 11 pages
  • ASME 2005 International Mechanical Engineering Congress and Exposition
  • Dynamic Systems and Control, Parts A and B
  • Orlando, Florida, USA, November 5 – 11, 2005
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 0-7918-4216-9 | eISBN: 0-7918-3769-6
  • Copyright © 2005 by ASME


A bond graph model of a fully suspended mountain bike and non-seated rider is created to develop predictions for the performance of mountain bikes during large excursion maneuvers such as drops, jumps, crashes, and rough terrain riding. The model assumes planar dynamics, a single pivot full suspension bicycle, and a rigid-body rider suspended from the bicycle. The main frame, front fork, rear triangle, two wheels, and rider are modeled as separate bodies interconnected at the main pivot, telescoping fork, pedals, handlebars, and axles. Suspensions are between the main frame and front fork, main frame and rear triangle, handlebars and rider (arms) and pedals and rider (legs). An algorithm is used to allow tracking of a virtual tire-ground contact point for events that separate the wheels from the ground. Significant excursions of motion are allowed to model major slope changes, separations from the ground, and large rotational events (endos). The bond graph approach allows kinematics to drive the significant dynamic interactions with the effort (force and torque) relationships being derived for “free”. Simulations of a ground profile with a rise followed by a steep drop are performed for various initial conditions to qualitatively validate the predictions of the model. Rider strategies for negotiating the drop are examined in the process. Overarching goals of the research are to examine and understand the dynamics and control of interactions between a cyclist and mountain bike. Specific, longer term, goals are to understand the improvement in performance afforded by an experienced rider, to hypothesize human control algorithms that allow riders to perform maneuvers well and safely, to predict structural bike and body forces from these maneuvers, and to quantify performance differences between hard-tail and various full suspension bicycles.

Copyright © 2005 by ASME



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