This paper deals with the problem of delay-dependent stability analysis for singular systems with a constant time delay. By employing the Jensen inequality and the Wirtinger-based inequality, new delay-dependent stability criteria are developed. Furthermore, in order to obtain less conservative results, a new method of constructing Lyapunov--Krasovskii functionals (LKFs) is used. It should be pointed out that the positive-definiteness restrictions on some symmetric matrices of the LKFs are removed. All the criteria are proposed in terms of strict linear matrix inequalities. Finally, numerical examples are given to demonstrate the less conservatism of the results.

This article investigates the robust data-driven containment control problem for heterogeneous multiagent systems where the system dynamics of both the leaders and the followers are unknown. During the data collection phase, the follower systems are disturbed by unmeasurable but bounded noises. A data-based linear matrix inequality condition is first constructed from the noisy data to determinate the feasible feedback gain for each follower. Then, the distributed observer which is independent of the system matrix of the leaders is designed to estimate the convex hull of the leaders. Moreover, the approximate solution to the linear matrix equation for heterogeneous system is solved with bounded approximate error where the noisy data of each follower and the normal data of arbitrary leader is utilized. Based on the proposed feedback gain and observer as well as approximate solution, the robust data-driven control protocol is provided to guarantee the uniform boundedness of containment error. Finally, a numerical example and a multivehicle model are given to verify the effectiveness of the designed containment control protocol.

This paper deals with the problem of robust non-fragile guaranteed cost control for a class of uncertain singular Markovian jump systems with time-varying delays. The time-varying and norm-bounded parameter uncertainties exist in both the state matrix and the derivative matrix. A non-fragile proportional-derivative state feedback controller is designed to guarantee the closed-loop singular Markovian jump system is normal and robustly stochastically stable. By proposing an optimization strategy in the framework of linear matrix inequalities, the upper bound of the cost function can be minimized. Finally, a numerical example is given to demonstrate the effectiveness of the results.