Date of Award
12-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mechanical Engineering
Committee Chair/Advisor
Dr. Ardalan Vahidi
Committee Member
Dr. Phanindra Tallapragada
Committee Member
Dr. Ge Lv
Committee Member
Dr. Randolph Hutchison
Abstract
This thesis develops a physiologically inspired three-tank model of human bioenergetics to describe and optimize fatigue and recovery during exercise. The aerobic, phosphagen, and glycogen systems are modeled as coupled reservoirs whose states constrain instantaneous power output. Building on this representation, dynamic programming is used to compute pacing strategies that respect physiological limits while maximizing task-specific goals such as peak power or total work over a fixed duration, enabling individualized, goal-driven exercise prescriptions. The three-tank dynamics are formulated in continuous time and then discretized under a zero-order-hold assumption for simulation and optimal control. Model parameters are linked to standard field and laboratory tests: a three-minute all-out test to estimate critical power and finite anaerobic work capacity, and an incremental ramp test with gas-exchange measurements to characterize maximal oxygen uptake and threshold behavior. For a trained cyclist, these initial estimates are refined by fitting the model’s maximal-power envelope to power data from multiple high-intensity sessions. Several training objectives are then posed as finite-horizon optimal-control problems and solved on a discretized state grid, with power held over short dwell blocks to resemble practical intervals. Across objectives emphasizing work above critical power, time near high fractions of maximal oxygen uptake, or total work, optimized profiles for two subjects outperform simple fixed interval baselines of equal duration, indicating that hydraulic bioenergetic models combined with dynamic programming can support individualized high-intensity training design.
Recommended Citation
Lotfi, Asal, "Optimal Control of Human Exercise Using an Individualized Physiological Three-Tank Model and Dynamic Programming" (2025). All Theses. 4671.
https://open.clemson.edu/all_theses/4671