Biblio

This work proposes an event-triggered adaptive asymptotic tracking control scheme for flexible robotic manipulators. Firstly, by employing the command filtered backstepping technology, the ``explosion of complexity'' problem is overcame. Then, the event-triggered strategy is utilized which makes that the control input is updated aperiodically when the event-trigger occurs. The utilized event-triggered mechanism reduces the transmission frequency of computer and saves computer resources. Moreover, it can be proved that all the variables in the closed-loop system are bounded and the tracking error converges asymptotically to zero. Finally, the simulation studies are included to show the effectiveness of the proposed control scheme.

The cyber-physical security of smart grid is of great importance since it directly concerns the normal operating of a system. Recently, researchers found that organized sequential attacks can incur large-scale cascading failure to the smart grid. In this paper, we focus on the line-switching sequential attack, where the attacker aims to trip transmission lines in a designed order to cause significant system failures. Our objective is to identify the critical line-switching attack sequence, which can be instructional for the protection of smart grid. For this purpose, we develop an evolutionary computation based vulnerability analysis framework, which employs particle swarm optimization to search the critical attack sequence. Simulation studies on two benchmark systems, i.e., IEEE 24 bus reliability test system and Washington 30 bus dynamic test system, are implemented to evaluate the performance of our proposed method. Simulation results show that our method can yield a better performance comparing with the reinforcement learning based approach proposed in other prior work.