Predictive Dynamic Simulation of Cycling Using Olympic Cyclist and Bicycle Models

pedaling;track cycling;predictive simulation;forward dynamics;direct collocation;optimal control;standing start;bicycle

Jansen, Conoradvisor:McPhee, John ; affiliation1:Faculty of Engineering ; McPhee, John ;

University of Waterloo

关键词: direct collocation;    standing start;    Master Thesis;    bicycle;    predictive simulation;    forward dynamics;    optimal control;    track cycling;    pedaling;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/13733/3/Jansen_Conor.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

Predictive dynamic simulation is a useful tool for analyzing human movement andoptimizing performance. Such simulations do not require experimental data collectionand provide the opportunity to analyze a variety of potential scenarios. This presentsinteresting possibilities for investigating the optimal technique in sports applications, suchas cycling. Much of the previous research on modeling and simulation of cycling has focusedon seated pedaling and models the bicycle or ergometer with an e ective resistive torqueand inertia. This study was focused on modeling standing starts, a component of certaintrack cycling events in which the cyclist starts from rest and attempts to accelerate to topspeed 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 usingMapleSim and utilized for predictive simulations of standing starts. A joint torque modelwas incorporated to represent musculoskeletal dynamics, including scaling based on jointangle 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 forwheel-ground contact. GPOPS-II, a direct collocation optimal control software, was usedto solve the optimal control problem for the predictive simulation.First, a modi ed version of this model was used to simulate ergometer pedaling. Themodel was validated by comparing simulated ergometer pedaling against ergometer pedalingperformed by seven Olympic-level track cyclists from the Canadian team. A kinematicdata tracking approach was used to assess the abilities of the model to match experimentaldata. Following the successful matching of experimental data, a purely predictivesimulation was performed for seated maximal start-up ergometer pedaling with an objectivefunction of maximizing the crank progress. These simulations produce joint angles,crank torque, and power similar to experimental results, indicating that the model was areasonable representation of an Olympic cyclist.Subsequently, experimental data were collected for a single member of the Canadianteam performing standing starts on the track. Data collected included crank torque, cadence,and joint kinematics. Predictive simulations of standing starts were performed usingthe combined cyclist and bicycle model. Key aspects of the standing start technique, includingthe drive and reset, were captured in the predictive simulations. The results showthat optimal control can be used for predictive simulation with a combined cyclist andbicycle model. Future work to improve upon the current model is discussed.