Pole vault performance for anthropometric variability via a dynamical optimal control model

Abstract

Optimal performance of a dynamical pole vault process was modeled as a constrained nonlinear optimization problem. That is, given a vaulter’s anthropomorphic data and approach speed, the vaulter chose a specific take-off angle, pole stiffness and gripping height in order to yield the greatest jumping height compromised by feasible bar-crossing velocities. The optimization problem was solved by nesting a technique of searching an input-to-output mapping arising from the vaulting trajectory and a method of nonlinear sequential quadratic programming (SQP). It was suggested from the optimization results that the body’s weight has an important influence on the vaulting performance beside the vaulter’s height and approach speed; the less skilled vaulter should gradually adopt a longer pole to improve the performance.