In this article, we investigate the optimal control problems of heterogeneous node-based information epidemics. A node-based susceptible-infected-recovered-susceptible 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 toward 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 the Pontryagin maximum principle and the forward-backward sweep method. Moreover, numerical experiments validate the use of the node-based SIRS model by comparing it with the exact 3 ^N -state Markov chain model. The effectiveness of the proposed control rules is demonstrated on both models. Furthermore, discussion on the influence of the parameters provides insights into the strategies of guiding information diffusion processes.

This article investigates the tracking problem of an uncertain $n$ -link robot manipulator with guaranteed safety and performance. To tackle parametric uncertainties, the torque filtering-augmented concurrent learning (CL) method is introduced for online identification of the unknown system without requirements of joints acceleration. By using CL, the parameter convergence is guaranteed by exploiting the current and historical data simultaneously. This technique enjoys practicability compared with common methods that need to incorporate external noises to satisfy the persistence of excitation condition for the parameter convergence. Based on the estimated model, we design a barrier Lyapunov function (BLF)-based adaptive control law by the backstepping technique and Lyapunov analysis. By ensuring the boundness of the BLF, the system output and the tracking error are proved to lie in the safety set and performance set, respectively. Numerical simulation results and experiment tests validate the proposed strategy.

Opinion dynamics on social networks with coopetitive (cooperative-competitive) interactions may result in polarity, consensus or neutrality under different opinion protocols. The antecedent of protocol design is to study the accessibility problem: whether or not there exist admissible control rules to polarize, consensus, or neutralize individual opinions in a large population. From an operational perspective, this technical note is aimed at the investigation of polarizability, consensusability, and neutralizability of opinion dynamics in question. Particular emphasis is on the joint impact of the dynamical properties of individuals and the interaction topology among them on polarizability, consensusability, and neutralizability, respectively. Sufficient and/or necessary conditions for those accessibility problems are provided by using powerful tools from spectral analysis and algebraic graph theory. To characterize the individual diversity in real life, we further investigate the solvability of opinion formation problems in heterogeneous systems with nonidentical dynamics. Accordingly, sufficient and/or necessary criteria for heterogeneous network polarizability, consensusability, and neutralizability are derived.

Piecewise affine (PWA) systems are attractive models that can represent various hybrid systems with local affine subsystems and polyhedral regions due to their universal approximation properties. The identification problem of PWA systems amounts to estimating the number of subsystems, parameters of each subsystem, and the corresponding polyhedral partitions via state-input vectors. In this paper, we propose a novel approach to address the online identification problem of continuous-time PWA systems in state-space form. Specifically, an online active mode recognition algorithm and a generalized integral concurrent learning identifier are presented to acquire the number of subsystems, the switching sequence, and the parameter of each subsystem. In addition, we develop the optimization problem for the polyhedral partition estimation, which is solved by using the estimated switching sequence and subsystem parameters. The effectiveness of the proposed identification approach is demonstrated via simulation results.