Statistically consistent inverse optimal control for discrete-time indefinite linear–quadratic systems
Artikel i vetenskaplig tidskrift, 2024

The Inverse Optimal Control (IOC) problem is a structured system identification problem that aims to identify the underlying objective function based on observed optimal trajectories. This provides a data-driven way to model experts’ behavior. In this paper, we consider the case of discrete-time finite-horizon linear–quadratic problems where: the quadratic cost term in the objective is not necessarily positive semi-definite; the planning horizon is a random variable; we have both process noise and observation noise; the dynamics can have a drift term; and where we can have a linear cost term in the objective. In this setting, we first formulate the necessary and sufficient conditions for when the forward optimal control problem is solvable. Next, we show that the corresponding IOC problem is identifiable. Using the conditions for existence of an optimum of the forward problem, we then formulate an estimator for the parameters in the objective function of the forward problem as the globally optimal solution to a convex optimization problem, and prove that the estimator is statistical consistent. Finally, the performance of the algorithm is demonstrated on two numerical examples.

Time-varying system matrices

Indefinite linear quadratic regulator

Convex optimization

Inverse optimal control

Inverse reinforcement learning

System identification

Semidefinite programming


Han Zhang

Shanghai Jiao Tong University

Axel Ringh

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik


0005-1098 (ISSN)

Vol. 166 111705






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