A Convex Optimization Approach to Inverse Optimal Control

In this paper, the problem of inverse optimal control (lOC) is investigated, where the quadratic cost function of a dynamic process is required to be recovered based on the observation of optimal control sequences. In order to guarantee the feasibility of the problem, the IOC is reformulated as an infinite-dimensional convex optimization problem, which is then solved in the primal-dual framework. In addition, the feasibility of the original IOC could be determined from the optimal value of reformulated problem, which also gives out an approximate solution when the original problem is not feasible. In addition, several simplification methods are proposed to facilitate the computation, by which the problem is reduced to a boundary value problem of ordinary differential equations. Finally, numerical simulations are used to demonstrate the effectiveness and feasibility of the proposed methods.