Sparsity Invariance for Convex Design of Distributed Controllers

Abstract

We address the problem of designing optimal linear time-invariant (LTI) sparse controllers for LTI systems, which corresponds to minimizing a norm of the closed-loop system subject to sparsity constraints on the controller structure. This problem is NP-hard in general and motivates the development of tractable approximations. We characterize a class of convex restrictions based on a new notion of Sparsity Invariance (SI). The underlying idea of SI is to design sparsity patterns for transfer matrices Y(s) and X(s) such that any corresponding controller K(s)=Y(s)X(s)^-1 exhibits the desired sparsity pattern. For sparsity constraints, the approach of SI goes beyond the notion of Quadratic Invariance (QI): 1) the SI approach always yields a convex restriction; 2) the solution via the SI approach is guaranteed to be globally optimal when QI holds and performs at least as well as considering a nearest QI subset. Moreover, the notion of SI naturally applies to designing structured static controllers, while QI is not utilizable. Numerical examples show that even for non-QI cases, SI can recover solutions that are 1) globally optimal and 2) strictly more performing than previous methods.

References in corpus (6)

• Distributed Model Predictive Control for Heterogeneous Vehicle Platoons under Unidirectional Topologies
• Design of Optimal Sparse Feedback Gains via the Alternating Direction Method of Multipliers
• An Input-Output Parametrization of Stabilizing Controllers: amidst Youla and System Level Synthesis
• On the Equivalence of Youla, System-level and Input-output Parameterizations
• On Separable Quadratic Lyapunov Functions for Convex Design of Distributed Controllers
• Robust Distributed Control Beyond Quadratic Invariance

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