Visible to the public Biblio

Filters: Author is Navid Aghasadeghi, University of Illinois at Urbana-Champaign  [Clear All Filters]
Navid Aghasadeghi, University of Illinois at Urbana-Champaign, Huihua Zhao, Texas A&M University, Levi J. Hargrove, Northwestern University, Aaron D. Ames, Texas A&M University, Eric J. Perreault, Northwestern University, Timothy Bretl, University of Illinois at Urbana-Champaign.  2013.  Learning Impedance Controller Parameters for Lower-Limb Prostheses. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Impedance control is a common framework for control of lower-limb prosthetic devices. This approach requires choosing many impedance controller parameters. In this paper, we show how to learn these parameters for lower-limb prostheses by observation of unimpaired human walkers. We validate our approach in simulation of a transfemoral amputee, and we demonstrate the performance of the learned parameters in a preliminary experiment with a lower-limb prosthetic device.

Miles Johnson, University of Illinois at Urbana-Champaign, Navid Aghasadeghi, University of Illinois at Urbana-Champaign, Timothy Bretl, University of Illinois at Urbana-Champaign.  2013.  Inverse Optimal Control for Deterministic Continuous-time Nonlinear Systems. 52nd Conference on Decision and Control.

Inverse optimal control is the problem of computing a cost function with respect to which observed state and input trajectories are optimal. We present a new method of inverse optimal control based on minimizing the extent to which observed trajectories violate first-order necessary conditions for optimality. We consider continuous-time deterministic optimal control systems with a cost function that is a linear combination of known basis functions. We compare our approach with three prior methods of inverse optimal control. We demonstrate the performance of these methods by performing simulation experiments using a collection of nominal system models. We compare the robustness of these methods by analysing how they perform under perturbations to the system. To this purpose, we consider two scenarios: one in which we exactly know the set of basis functions in the cost function, and another in which the true cost function contains an unknown perturbation. Results from simulation experiments show that our new method is more computationally efficient than prior methods, performs similarly to prior approaches under large perturbations to the system, and better learns the true cost function under small perturbations.