On Separable Quadratic Lyapunov Functions for Convex Design of Distributed Controllers
For control engineers designing distributed controllers, this provides a more flexible convex approximation method that can yield better performance than existing diagonal Lyapunov approaches.
The paper characterizes a class of convex restrictions for designing stabilizing and optimal static controllers with a specified sparsity pattern, using separable quadratic Lyapunov functions. This generalizes previous diagonal Lyapunov function approaches, improving feasibility and performance, with numerical validation.
We consider the problem of designing a stabilizing and optimal static controller with a pre-specified sparsity pattern. Since this problem is NP-hard in general, it is necessary to resort to approximation approaches. In this paper, we characterize a class of convex restrictions of this problem that are based on designing a separable quadratic Lyapunov function for the closed-loop system. This approach generalizes previous results based on optimizing over diagonal Lyapunov functions, thus allowing for improved feasibility and performance. Moreover, we suggest a simple procedure to compute favourable structures for the Lyapunov function yielding high-performance distributed controllers. Numerical examples validate our results.