Jieqiang Wei

Self-triggered control for multi-agent systems with quantized communication or sensing

The consensus problem for multi-agent systems with quantized communication or sensing is considered. Centralized and distributed self-triggered rules are proposed to reduce the overall need of communication and system updates. It is proved that these self-triggered rules realize consensus exponentially if the network topologies have a spanning tree and the quantization function is uniform. Numerical simulations are provided to show the effectiveness of the theoretical results.

Optimization of the H ∞ -norm of Dynamic Flow Networks.

In this paper, we study the

Finite-time attitude synchronization with a discontinuous protocol

\mathcal{H}_{\infty}

On the modeling of neural cognition for social network applications

-norm of linear systems over graphs, which is used to model distribution networks. In particular, we aim to minimize the

Nonlinear consensus protocols with applications to quantized systems

\mathcal{H}_{\infty}

Steady-state Analysis of a Neural-cognition Based Human-social Behavior Model.

-norm subject to allocation of the weights on the edges. The optimization problem is formulated with LMI (Linear-Matrix-Inequality) constraints. For distribution networks with one port, i.e., SISO systems, we show that the

Optimal Weight Allocation of Dynamic Distribution Networks and Positive Semi-definiteness of Signed Laplacians

\mathcal{H}_{\infty}

Multi-agent formation control for circumnavigation of dynamic shapes

-norm coincides with the effective resistance between the nodes in the port. Moreover, we derive an upper bound of the

Optimization of the

\mathcal{H}_{\infty}

\mathcal{H}_{\infty}

-norm, which is in terms of the algebraic connectivity of the graph on which the distribution network is defined.