Statistically Consistent Inverse Optimal Control for Linear-Quadratic Tracking with Random Time Horizon
Paper in proceeding, 2022

The goal of Inverse Optimal Control (IOC) is to identify the underlying objective function based on observed optimal trajectories. It provides a powerful framework to model expert's behavior, and a data-driven way to design an objective function so that the induced optimal control is adapted to a contextual environment. In this paper, we design an IOC algorithm for linear-quadratic tracking problems with random time horizon, and prove the statistical consistency of the algorithm. More specifically, the proposed estimator is the solution to a convex optimization problem, which means that the estimator does not suffer from local minima. This enables the proven statistical consistency to actually be achieved in practice. The algorithm is also verified on simulated data as well as data from a real world experiment, both in the setting of identifying the objective function of human tracking locomotion. The statistical consistency is illustrated on the synthetic data set, and the experimental results on the real data shows that we can get a good prediction on human tracking locomotion based on estimating the objective function. It shows that the theory and the model have a good performance in real practice. Moreover, the identified model can be used as a control target in personalized rehabilitation robot controller design, since the identified objective function describes personal habit and preferences.

Inverse optimal control

human locomotion modelling

trajectory tracking

system identification

semidefinite programming

rehabilitation

convex optimization

Author

Han Zhang

Shanghai Jiao Tong University

Axel Ringh

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Weihan Jiang

Shanghai Jiao Tong University

Shaoyuan Li

Shanghai Jiao Tong University

Xiaoming Hu

Royal Institute of Technology (KTH)

Chinese Control Conference, CCC

19341768 (ISSN) 21612927 (eISSN)

Vol. 2022-July 1515-1522
9789887581536 (ISBN)

41st Chinese Control Conference, CCC 2022
Hefei, China,

Subject Categories

Computational Mathematics

Robotics

Probability Theory and Statistics

Control Engineering

DOI

10.23919/CCC55666.2022.9902327

More information

Latest update

10/27/2023