|Predictive dynamic simulation is a useful tool for analyzing human movement and
optimizing performance. Such simulations do not require experimental data collection
and provide the opportunity to analyze a variety of potential scenarios. This presents
interesting possibilities for investigating the optimal technique in sports applications, such
as cycling. Much of the previous research on modeling and simulation of cycling has focused
on seated pedaling and models the bicycle or ergometer with an e ective resistive torque
and inertia. This study was focused on modeling standing starts, a component of certain
track cycling events in which the cyclist starts from rest and attempts to accelerate to top
speed as quickly as possible. A useful model would need to incorporate bicycle dynamics,
including tire models, and complete cyclist dynamics, including the upper body.
A ten degree-of-freedom, two-legged cyclist and bicycle model was developed using
MapleSim and utilized for predictive simulations of standing starts. A joint torque model
was incorporated to represent musculoskeletal dynamics, including scaling based on joint
angle and angular velocity to represent the muscle force-length and force-velocity relationships.
Tire slip for the bicycle model was represented by the Pacejka tire model for
wheel-ground contact. GPOPS-II, a direct collocation optimal control software, was used
to solve the optimal control problem for the predictive simulation.
First, a modi ed version of this model was used to simulate ergometer pedaling. The
model was validated by comparing simulated ergometer pedaling against ergometer pedaling
performed by seven Olympic-level track cyclists from the Canadian team. A kinematic
data tracking approach was used to assess the abilities of the model to match experimental
data. Following the successful matching of experimental data, a purely predictive
simulation was performed for seated maximal start-up ergometer pedaling with an objective
function of maximizing the crank progress. These simulations produce joint angles,
crank torque, and power similar to experimental results, indicating that the model was a
reasonable representation of an Olympic cyclist.
Subsequently, experimental data were collected for a single member of the Canadian
team performing standing starts on the track. Data collected included crank torque, cadence,
and joint kinematics. Predictive simulations of standing starts were performed using
the combined cyclist and bicycle model. Key aspects of the standing start technique, including
the drive and reset, were captured in the predictive simulations. The results show
that optimal control can be used for predictive simulation with a combined cyclist and
bicycle model. Future work to improve upon the current model is discussed.