Consensus-based adaptive guaranteed-performance formation control for second-order multi-agent systems

Adaptive guaranteed-performance formation analysis and design problems for second-order multi-agent systems are studied, where the global information is not required, which means the main results of this article are fully distributed. First, an adaptive guaranteed-performance formation control protocol is presented for second-order multi-agent systems, where the control input is constructed using neighboring state errors and adaptively adjustable interaction weights. Then, an adaptive guaranteed-performance formation control is proposed based on Riccati inequalities. Furthermore, the guaranteed-performance cost is determined and the adjusting approach of the formation control gain is presented in terms of linear matrix inequalities. Finally, a numerical simulation is provided to demonstrate the effectiveness of the theoretical results.

Adaptive Consensus of High Order Multi-Agent Systems with Unknown Nonlinear Dynamics

In this paper, high order consensus problem of multi-agent systems with non-identical unknown nonlinear dynamics following an unknown and nonlinear leader is investigated in networks with fixed and switching topologies. By parameterizations of unknown nonlinear dynamics of agents in the system, neighbor-based adaptive consensus algorithms are proposed by incorporating consensus errors in addition to relative position feedback. Base on algebraic graph theory, Lyapunov theory, Riccati inequalities and PE condition, analysis of stability and parameter convergence of the proposed algorithm are conducted. Finally, a simulation in switching networks is worked out to illustrate the effectiveness of the theoretical results.

RobustH∞Filtering for a Class of Uncertain Markovian Jump Systems with Time Delays

This paper studies the problem of robustH∞filtering for a class of uncertain time-delay systems with Markovian jumping parameters. The system under consideration is subject to norm-bounded time-varying parameter uncertainties. The problem to be addressed is the design of a Markovian jump filter such that the filter error dynamics are stochastically stable and a prescribed bound on theℒ2-induced gain from the noise signals to the filter error is guaranteed for all admissible uncertainties. A sufficient condition for the existence of the desired robustH∞filter is given in terms of two sets of coupled algebraic Riccati inequalities. When these algebraic Riccati inequalities are feasible, the expression of a desiredH∞filter is also presented. Finally, an illustrative numerical example is provided.