Critical Problem of Optimal Stabilization Without Control Constraints

This paper investigates the linear–quadratic optimal stabilization problem in the so-called critical case, that is, the situation in which the spectrum of the system matrix contains purely imaginary eigenvalues or the standard positive-definiteness conditions on the weight matrices of the objective functional are violated. To address these challenges, new regularization methods for critical problems via perturbation of the system matrices and the functional are studied, and novel algorithms for decomposing multidimensional problems into a collection of one-dimensional canonical systems are developed. The main contribution of this work lies in providing a systematic framework for critical cases where standard methods fail. The results are of practical significance for the construction of optimal synthesis in various engineering and applied systems; in particular, they are applicable to the stabilization of unmanned aerial vehicles, robotic complexes, and intelligent power grids.