In this paper, we study the robust global containment control problem for second-order multi-agent systems with bounded input disturbances subject to input saturation under general directed communication graphs. Two types of distributed controllers based on novel sliding mode control ideas are respectively proposed to solve the globally asymptotic containment problem for multi-agent systems with static leaders and the practical containment problem for the case with dynamic leaders subject to unknown control inputs. One distinctive feature of the proposed controllers is that only local velocity measurements, relative position and velocity measurements are involved in designing the controllers which thus effectively reduces the information transmission burden among the agents for real- time implementation. Another favorable property of the designed controllers is that the global information such as the spectrum of the graph Laplacian matrix is not required in designing these controllers under general directed communication graphs. Simulations on containment control of multiple quadrotors are performed to illustrate the effectiveness of the proposed containment controllers.

In building-microgrid communities, renewable generation and time-varying load usually cause power fluctuations, which influence the ancillary support to the main grid. Thermostatically controlled loads (TCLs) can be utilized to compensate such power variations due to their aggregated and controllable power consumptions. Meanwhile, one basic requirement for the users' side of TCLs is to realize the fair sharing of power states and comfort states. This article proposes a distributed event-based control strategy, where information of neighboring TCLs is exchanged only when a dynamic event-triggered condition is satisfied, and thus it intelligently determines the necessary transmission frequency to save communication resources. From a cybersecurity perspective, the communication network of TCLs may be subject to hybrid attacks, for example, denial-of-service (DoS) and false data-injection (FDI) attacks. During DoS attack intervals, no information can be communicated even through the event-triggered condition is satisfied. Furthermore, the control inputs may also be tampered by FDI attacks. By utilizing the Lyapunov stability and hybrid control theories, sufficient conditions regarding the attack parameters are derived such that fair sharing of power states and comfort states of all involved TCLs can be achieved exponentially. The exclusion of Zeno behaviors is proved and a corollary for ideal communication situations is also deduced. Finally, simulation examples with various attack parameters are conducted to verify the effectiveness of the main results.

The distributed robust optimization problem is investigated in this paper for a class of networked agent systems under stochastically switching communication graphs. Unlike most existing results on distributed optimization of networked agent systems, the dynamics of each agent in the present model are allowed to be subjected to matched linearly-parameterized unknown nonlinearities while the underlying communication graph is stochastically switching among some matrix-weighted communication graphs. The objective here is to cooperatively optimize a holistic cost function, which is a sum of all the convex local objective functions, under the mild condition that each agent may only have access to the gradient of its local objective function and the relative local information between itself and its neighbors. To solve such an outstanding optimization problem, a neuro-adaptive optimization protocol is developed based upon parameterizations of the unknown nonlinearities where the neuro-adaptive learning algorithm for estimating the weight matrices therein acts as the key role to compensate for the unknown nonlinearities. Furthermore, the signum function-based feedback law is subtly integrated into the proposed protocol to deal with the effect of the gradient term of the local objective function. Efficient criteria are provided to guide selections of proper control parameters under which the robust optimization problem is successfully resolved in the mean-square sense. Lastly, theoretical results are validated by presenting several illustrative simulation examples.

This article investigates the economic dispatch problem in a smart grid with the event-triggered framework under uncertain communication networks. Unlike most of the existing works on economic dispatch, where the distributed consensus-based algorithms are conducted continuously or periodically, the event-triggered mechanism is employed for saving communication resources. For uncertainties, whose upper bounds may be even more significant than nominal weights, we design a general framework for the distributed event-triggered algorithm and an adaptive law for adjusting the coupling weights. The upper bounds of the communication uncertainties are not required in the design, and the adaptive tuning of coupling weights is proved to be able to compensate for the adverse effects caused by uncertainties in the consensus-seeking phase. Noteworthy, the design of event-triggered parameters is independent of the communication topology, which makes the proposed algorithm fully distributed. We also prove that the Zeno triggering of the event-triggered algorithms does not exist. Finally, a case study with the communication network represented by the random multiradius geographical graph is conducted to verify the effectiveness of the derived algorithms.