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Lixia Yan

Publications and source records attributed to Lixia Yan.

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Sharp bounds on the $A_α$-index of graphs in terms of the independence number

Given a graph $G$, the adjacency matrix and degree diagonal matrix of $G$ are denoted by $A(G)$ and $D(G)$, respectively. In 2017, Nikiforov \cite{0007} proposed the $A_α$-matrix: $A_α(G)=αD(G)+(1-α)A(G),$ where $α\in [0, 1]$. The largest eigenvalue of this novel matrix is called the $A_α$-index of $G$. In this paper, we characterize the graphs with minimum $A_α$-index among $n$-vertex graphs with independence number $i$ for $α\in[0,1)$, where $i=1,\lfloor\frac{n}{2}\rfloor,\lceil\frac{n}{2}\rceil,{\lfloor\frac{n}{2}\rfloor+1},n-3,n-2,n-1,$ whereas for $i=2$ we consider the same problem for $α\in [0,\frac{3}{4}{]}.$ Furthermore, we determine the unique graph (resp. tree) on $n$ vertices with given independence number having the maximum $A_α$-index with $α\in[0,1)$, whereas for the $n$-vertex bipartite graphs with given independence number, we characterize the unique graph having the maximum $A_α$-index with $α\in[\frac{1}{2},1).$

math.CO

Distributed Containment Reference Signal for Nonholonomic Planar Vehicles

Cooperative of multiple nonholonomic vehicles can be converted into tracking problems of a single-vehicle. The reference trajectory design within distributed features for each vehicle in the group is addressed in this note. The motivation is that nonholonomic vehicles cannot achieve asymptotical stabilization of non-feasible reference signals, and modifications about the virtual reference trajectory design are needed. Reduced-order design and time-varying technique, and some simple geometry tricks are applied to derive the dynamic reference trajectory.

eess.SY

Distributed Leader-Follower Formation Tracking Control of Multiple Quad-rotors

The leader-follower formation control analysis for multiple quad-rotor systems is investigated in this paper. To achieve predefined formation in the three-dimensional air space ($x,y$ and $z$), a novel local tracking control law and a distributed observer are obtained. The local tracking control law starts with finding a bounded continuous yet greater-than-zero control in $z$, based on which following a feedback linearization controls derived for errors associated with $x$ and $y$. The distributed observer achieves position coordination among followers, though there are only partial followers can know the leader's states and only neighboring communication is available. Simulation results validate the proposed formation scheme.

eess.SY