Abstract
In this paper, we consider two problems which can be posed as spectral radius minimization problems. Firstly, we consider the fastest average agreement problem on multi-agent networks adopting a linear information exchange protocol. Mathematically, this problem can be cast as finding an optimal W ∈ R n × n such that x ( k + 1 ) = W x ( k ) , W 1 = 1 , 1 T W = 1 T and W ∈ S ( E ) . Here, x ( k ) ∈ R n is the value possessed by the agents at the k th time step, 1 ∈ R n is an all-one vector and S ( E ) is the set of real matrices in R n × n with zeros at the same positions specified by a network graph G ( V , E ) , where V is the set of agents and E is the set of communication links between agents. The optimal W is such that the spectral radius ρ ( W − 1 1 T / n ) is minimized. To this end, we consider two numerical solution schemes: one using the q th-order spectral norm (2-norm) minimization ( q -SNM) and the other gradient sampling (GS), inspired by the methods proposed in [Burke, J., Lewis, A., & Overton, M. (2002). Two numerical methods for optimizing matrix stability. Linear Algebra and its Applications, 351–352, 117–145; Xiao, L., & Boyd, S. (2004). Fast linear iterations for distributed averaging. Systems & Control Letters, 53(1), 65–78]. In this context, we theoretically show that when E is symmetric, i.e. no information flow from the i th to the j th agent implies no information flow from the j th to the i th agent, the solution W s ( 1 ) from the 1-SNM method can be chosen to be symmetric and W s ( 1 ) is a local minimum of the function ρ ( W − 1 1 T / n ) . Numerically, we show that the q -SNM method performs much better than the GS method when E is not symmetric. Secondly, we consider the famous static output feedback stabilization problem, which is considered to be a hard problem (some think NP-hard): for a given linear system ( A , B , C ) , find a stabilizing control gain K such that all the real parts of the eigenvalues of A + B K C are strictly negative. In spite of its computational complexity, we show numerically that q -SNM successfully yields stabilizing controllers for several benchmark problems with little effort.
Highlights
Introduction and Problem StatementA typical scenario in multi-agent missions is for the agents to agree upon a certain quantity or decision based on their current information
Q-SNM is nothing but 1-SNM when E is symmetric
We considered two numerical solution schemes, the qth-order spectral norm minimization method (q-SNM) and the constrained gradient sampling method (CGSM)
Summary
A typical scenario in multi-agent missions is for the agents to agree upon a certain quantity or decision based on their current information. One may conclude that the mission heavily hinges on the ramifications of the limited information exchange pattern Such a decentralized tracking problem that requires each agent (processor) to do iterative weighted average operations in a decentralized manner is called the average consensus problem, and has been studied for numerous applications, e.g. mobile ad-hoc and wireless sensor networks. Finding W ∗ or minimizing the spectral radius matrix function ρ(·) is known as a very hard problem in general This is because ρ(·) is continuous but neither convex nor locally Lipschtz [16].
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