Fu Lin

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—We design sparse and block sparse feedback gains that minimize the variance amplification (i.e., the norm) of distributed systems. Our approach consists of two steps. First, we identify sparsity patterns of feedback gains by incorporating sparsity-promoting penalty functions into the optimal control problem, where the added terms penalize the number of(More)
(3) is satisfied. APPENDIX B PROOF OF THEOREM 2 The proof is similar to that of Theorem 1. Let a, b, , and be as defined in Appendix A. Then, due to (8), (18), (4), and Lemma 1, we have ^ xi(k) 2 [a; b] 8k 2 8i 2 V and x 3 2 [a; b]. From Lemma 3, lim k!1 V (x(k)) = c for some c 0. To show that c = 0, assume to the contrary that c > 0 and let be as defined(More)
—We are interested in assigning a pre-specified number of nodes as leaders in order to minimize the mean-square deviation from consensus in stochastically forced networks. This problem arises in several applications including control of vehicular formations and localization in sensor networks. For networks with leaders subject to noise, we show that the(More)
— We examine the leader selection problem in multi-agent dynamical networks where leaders, in addition to relative information from their neighbors, also have access to their own states. We are interested in selecting an a priori specified number of agents as leaders in order to minimize the total variance of the stochastically forced network. Combinatorial(More)
We have developed a new method, i.e., XLOGP3, for logP computation. XLOGP3 predicts the logP value of a query compound by using the known logP value of a reference compound as a starting point. The difference in the logP values of the query compound and the reference compound is then estimated by an additive model. The additive model implemented in XLOGP3(More)
— We consider networks of single-integrator systems, where it is desired to optimally assign a predetermined number of systems to act as leaders. Performance is measured in terms of the H2 norm of the overall network, and the leaders are assumed to always follow their desired state trajectories. We demonstrate that, after applying a sequence of relaxations,(More)
We study the optimal design of a conductance network as a means for synchronizing a given set of oscillators. Synchronization is achieved when all oscillator voltages reach consensus, and performance is quantified by the mean-square deviation from the consensus value. We formulate optimization problems that address the trade-off between synchronization(More)
— We study the design of feedback gains that strike a balance between the H2 performance of distributed systems and the sparsity of controller. Our approach consists of two steps. First, we identify sparsity patterns of feedback gains by incorporating sparsity-promoting penalty functions into the H2 problem, where the added terms penalize the number of(More)
— We consider social networks which contain agents that spread misinformation and refuse to change their opinion. For a fixed number of information disseminating agents, we formulate an optimization problem to find their optimal location within the network such that the spread of misinformation is countered and public awareness is maximally raised. Once the(More)
—We consider the design of optimal localized feedback gains for one-dimensional formations in which vehicles only use information from their immediate neighbors. The control objective is to enhance coherence of the formation by making it behave like a rigid lattice. For the single-integrator model with symmetric gains, we establish convexity, implying that(More)