This article focuses on the identification of switched nonlinear systems, which are characterized as a collection of nonlinear dynamical systems. Each nonlinear subsystem is activated by a discrete-valued variable (switching signal). Specifically, we consider the continuous-time switched nonlinear systems in the state-space form in our article. The identification of switched nonlinear systems amounts to simultaneous estimation of the switching signal and the nonlinear dynamic subsystems via all measured state-input vectors. However, the problem is challenging and generally requires a large computational complexity to be solved. In this article, we propose a novel online approach to address the identification problem of switched nonlinear systems, which is capable to handle the measured state-input vectors in sequence. In particular, the principle used for estimating the switching signal is developed based on the subspace method. Subsequently, the integral concurrent learning identifier is extended to identify the dynamics of each subsystem recursively. The effectiveness of the proposed identification approach is demonstrated via simulation results.

In this paper, we investigate the optimal control problems of heterogeneous node-based information epidemics. A node-based Susceptible-Infected-Recovered-Susceptible (SIRS) model is introduced to describe the information diffusion processes taking into account heterogeneities in both network structures and individual characters. Aiming at guiding information dissemination processes towards the desired performance, we propose an optimal control framework with respect to two typical scenarios, i.e., impeding the spread of rumors and enhancing the spread of marketing or campaigning information. We prove the existence of the solutions and solve the optimal control problems by Pontryagin Maximum Principle and forward-backward sweep method. Moreover, numerical experiments validate the using of the node-based SIRS model by comparing with the exact 3 N-state Markov chain model. The effectiveness of the proposed control rules are demonstrated on both models. Further discussion on the influence of the parameters provides insights into the strategies of guiding information diffusion processes.

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This paper is concerned with fully distributed consensus control of linear multi-agent systems with undirected graphs. Two kinds of reduced-order adaptive output-feedback protocols are proposed. For the edge-based protocol, each edge is adapted by a scalar that is determined by the output information of the associated two agents; for the node-based protocol, each agent multiplies the connecting weights by a scalar that is determined by the relative output information of all neighbouring agents. Sufficient conditions in terms of the solvability of some matrix equations are derived for the existence of the two protocols. Furthermore, a tractable algorithm is constructed for designing the protocol gains. Compared with the existing related results, the proposed protocols have the following three merits simultaneously: of lower dimension, using relative output information about neighbouring agents and in the fully distributed fashion. A simulation example on formation flying of spacecrafts is presented to illustrate the efficacy of the proposed method.