Projects
Below are the projects I have been involved in and the publications I've written during my PhD studies at Clemson University
Optimal Pacing of Cyclists
We are exploring dynamics of muscular fatigue and recovery. This is important in human-machine Interactions and in adaptive balancing of the physical load on a human operator. In the crowded research field of muscle physiology, we are introducing a novel perspective by modeling the dynamics of fatigue; and attempting to use high-tech wearable sensors for noninvasive measurement of fatigue. Currently, in collaborations with other faculty at Clemson and Furman Universities, we are working with human subject cyclists to help them pace themselves optimally along a mountainous trail using optimal control methods. Most recent results can be found in:
[1] Ashtiani, Faraz, et al. "Optimal Pacing of a Cyclist in a Time Trial Based on Experimentally Calibrated Models of Fatigue and Recovery." arXiv preprint arXiv:2007.11393 (2020).
[2] Sreedhara, Vijay Sarthy M., Ashtiani, Faraz, et al. "Modeling the Recovery of W'in the Moderate to Heavy Exercise Intensity Domain." Medicine and Science in Sports and Exercise (2020).
[3] Ashtiani, Faraz, et al. "Experimental Modeling of Cyclists Fatigue and Recovery Dynamics Enabling Optimal Pacing in A Time Trial." 2019 American Control Conference (ACC). IEEE, 2019.
Truck Platooning
In a project in collaboration with Cummins Inc. and Department of Energy, we have been developing an optimal controller to enable truck platooning. The practice of truck platooning utilizes following distances as small as a few meters of each vehicle in a string to benefit from slipstream effects and reduce aerodynamic drag. By this, fuel economy is then improved in the vehicles. In this work, the impact of a connected and anticipative cruise controller in a truck platooning application is explored. We utilize two separate optimal controllers: 1) a time-invariant kinematic model with a first-order lag on the acceleration of the vehicle - intended strictly as a connected gap-tracking controller, and 2) a time-varying dynamic model which considers linearized aerodynamic drag terms - intended as an engine demand optimizer. The utilization of time-invariant and time-variant controllers resulted in 14% and 20% better fuel economy, respectively, compared to an Intelligent Driver Model baseline modeled after human response. The results of this study are presented in:
[4] Ard, Tyler, Ashtiani, Faraz, et al. "Optimizing Gap Tracking Subject to Dynamic Losses via Connected and Anticipative MPC in Truck Platooning." 2020 American Control Conference (ACC). IEEE, 2020.
Intelligent Intersection Control
we explored the added efficiency afforded by vehicle autonomy via better coordination with smart traffic control infrastructure when traffic signals can be eliminated. This study was done by a former PhD student of out lab, Dr. Alireza Fayazi. In his paper, the vehicle arrival scheduling is formulated as a mixed-integer linear program (MILP), and is solved by IBM CPLEX optimization package. In continuation of his work, I helped him apply the same formulation to a grid of 3 by 3 intersections. In addition to the V2I connectivity, I added an I2I network scheme in which intersections pass on information regarding the vehicles subscribed to them, with the neighboring intersections. The results of positive impact on traffic flow and vehicles' fuel economy by adapting the mentioned network is demonstrated in:
[5] Ashtiani, Faraz, S. Alireza Fayazi, and Ardalan Vahidi. "Multi-Intersection Traffic Management for Autonomous Vehicles via Distributed Mixed Integer Linear Programming." 2018 Annual American Control Conference (ACC). IEEE, 2018.
