Reinforcement Learning for Energy Optimization Under Human Fatigue Constraints of Power-Assisted Wheelchairs

In the last decade, Power-Assisted Wheelchairs (PAWs) have been widely used for improving the mobility of disabled persons. The main advantage of PAWs is that users can keep a suitable physical activity. Moreover, the metabolic-electrical energy hybridization of PAWs provides more flexibility for optimal control design. In this context, we propose an optimal control for minimizing the electrical energy consumption under human fatigue constraints, including a human fatigue model. The electrical motor has to cooperate with the user over a given distance-to-go. As the human fatigue model is unknown in practice, we use model-free Policy Gradient methods to directly learn controllers for a given driving task. We verify that the model-free solution is near-optimal by computing the model-based controller, which is generated by Approximate Dynamic Programming. Simulation results confirm that the model-free Policy Gradient method provides near-optimal solutions.