Abstract

Here we seek to control mechanical power output in outdoor cycling by adjusting commanded cadence of a cyclist. To understand cyclist’s dynamic behavior, we had one participant match their cadence to a range of commanded cadences. We then developed a mathematical model that predicts the actual mechanical power as a function of commanded cadence. The average absolute error between the predicted power of our model and the actual power was 15.9 ± 11.7%. We used this model to simulate our closed-loop controller and optimize for proportional and integral controller gains. With these gains in outdoor cycling experiments, the average absolute error between the target and the actual power was 3.2 ± 1.2% and the average variability in power was 2.9 ± 1.3%. The average responsiveness, defined as the required time for the actual power to reach 95% of the target power following changes in target power, was 7.4 ± 2.0 s.

Highlights

  • Wearable sensors quantify our everyday lives by counting our steps, calories, heartbeats, and more [1]

  • We developed and parameterized a mathematical model that best fit the simulated power to the actual power

  • We considered the participant as a dynamic system that can be experimentally identified by providing controlled inputs to the system and measuring its dynamic response

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Summary

Introduction

Wearable sensors quantify our everyday lives by counting our steps, calories, heartbeats, and more [1] This provides us with valuable feedback about our lifestyle and can encourage us to increase our exercise intensity and improve our health [2]. The controller adjusted the commanded step frequency for the runner in real-time This system reduced the pacing error to under 1% [4]. We develop a similar system to control an athlete’s mechanical power output in cycling We accomplished this using a closed-loop feedback control system that adjusts the cadence of the cyclist in real-time. We first built a microcontroller-operated system that provides the cyclists with changes in cadence and measures the power output Using these data, we developed and parameterized a mathematical model that best fit the simulated power to the actual power. We implemented the optimized feedback controller into the microcontroller to test its performance in controlling the power output in outdoor cycling

Identifying Cyclist Dynamics
Design of Feedback Controller
Testing of Feedback Controller
Experiment 3
Discussion and Conclusions
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