Modeling and analysis of complex network systems, including collaborative optimization control, and path planning algorithms for mobile robots. The main research are summarized as follows:

1、Model simplification method for complex network systems: For complex network systems, with the goal of optimizing system cooperative control, theoretical methods for structural analysis and model simplification of such network systems have been established, such as the second-order network system, undirected and directed strongly connected multi-agent network systems. The main results have published 1 Automatica paper, 1 IEEE TAC paper, 1 IEEE Transactions on Circuits and Systems-I paper, and several top international conference papers in the field of control IEEE CDC, IEEE ACC, etc.

2、Stability analysis of collaborative optimization control algorithm: For the biased min-consensus protocol and a continuous-time Adaptive Bellman-Ford algorithm, the global asymptotic stability analysis have been proposed, points out that its equilibrium point exists and is unique, and converges exponentially. The main innovation of this part lies in the design of Lyapunov function and the promotion of various applications, such as the shortest path solution under non-Euclidean distance, Voronoi partition problem and weighted region problem.  The main results have published 1 paper on Systems & Control Letters, 1 paper on IET, 1 paper on Asian Journal of Control, etc. 

Ongoing research work: multi-mobile robot path planning optimization algorithm and robotic arm path planning based on event-triggered learning algorithm.