This paper investigates a distributed fuzzy adaptive formation control for quadrotor multiple unmanned aerial vehicles under unmodeled dynamics and switching topologies. The unmanned aerial vehicles dynamics model is described by the Newton-Euler formula, and the actuator faults are considered in the system model in the form of multiplicative factors and additive factors. Due to the underactuated characteristics of the unmanned aerial vehicles, two objective attitude commands are generated by designing a virtual control signal, which are transmitted to the attitude subsystem, and then the position controller is solved. By constructing a distributed communication mechanism between unmanned aerial vehicles, an adaptive formation control strategy is proposed, which can enable unmanned aerial vehicles to update their position and speed online according to their neighbor information, and then achieve the required formation. In addition, a fuzzy adaptive sliding mode controller is designed to ensure that the tracking errors of unmanned aerial vehicles converge to the neighborhood of the origin. Finally, the simulation results verify the effectiveness of the proposed control strategy.

This study investigates the fixed-time stability of stochastic nonlinear systems described by Itô differential equations. The improved fixed-time Lyapunov theorem is given and an important corollary is obtained. It is proved that the upper-bound estimate of the settling time is less conservative. We establish a new definition of practically fixed-time stability in probability (PFxTSp) and propose the corresponding Lyapunov criterion theorem. In addition, different cases of the parameters are discussed to provide a less conservative upper-bound estimate of the settling time. The effectiveness of the proposed stability and controller is demonstrated by two simulation examples.

A collision-free formation control problem for multiple unmanned aerial vehicles (UAVs) with directed switching topologies and disturbances is investigated. A novel distributed control algorithm that uses unmanned aerial vehicles' local directed switching information is proposed for achieving the required flight formation and ensuring a safe distance between UAVs; the algorithm involves the incorporation of the artificial potential field method in the virtual leader formation scheme. Two command signals generated by the virtual position controller are transmitted to the attitude subsystem. For each UAV, an adaptive composite controller is designed by combining a fuzzy system and fast terminal sliding mode control (FTSMC) technique to guarantee that tracking errors converge to a stable area around zero. Finally, the feasibility of the proposed composite control algorithm is demonstrated through a simulation.

Microgrid formation (MF) is a core solution for increasing the resilience of distribution systems in extreme situations. However, a significant power imbalance at the MF onset will result in the violation of dynamic frequency constraints, especially in low-inertia wind power penetrated distribution systems (WPP-DSs). To ensure the secure MF after emergencies, this paper proposes a frequency constrained proactive scheduling method for WPP-DSs. The primary frequency response (PFR) model is proposed to describe the microgrid frequency dynamics after islanding, where wind turbines are deloaded to provide a primary reserve. Then the PFR model is incorporated into the scheduling model to ensure frequency security during MF process. The proposed method proactively dispatches controllable units to mitigate the power imbalance and to reserve power for frequency regulation. After emergencies, the primary reserve is released, and adaptive microgrids are securely formed to sustain critical services. The PFR model is formulated as algebraic differential equations (ADEs), which makes it difficult to solve the model directly. Thus, the PFR model is discretized into difference equations and further linearized to facilitate solution. Simulation results validate the merits of the proposed method in reducing the conservatism of proactive scheduling strategies and improving the microgrid security during MF process.

This article studies the optimal synchronization of linear heterogeneous multiagent systems (MASs) with partial unknown knowledge of the system dynamics. The object is to realize system synchronization as well as minimize the performance index of each agent. A framework of heterogeneous multiagent graphical games is formulated first. In the graphical games, it is proved that the optimal control policy relying on the solution of the Hamilton–Jacobian–Bellmen (HJB) equation is not only in Nash equilibrium, but also the best response to fixed control policies of its neighbors. To solve the optimal control policy and the minimum value of the performance index, a model-based policy iteration (PI) algorithm is proposed. Then, according to the model-based algorithm, a data-based off-policy integral reinforcement learning (IRL) algorithm is put forward to handle the partially unknown system dynamics. Furthermore, a single-critic neural network (NN) structure is used to implement the data-based algorithm. Based on the data collected by the behavior policy of the data-based off-policy algorithm, the gradient descent method is used to train NNs to approach the ideal weights. In addition, it is proved that all the proposed algorithms are convergent, and the weight-tuning law of the single-critic NNs can promote optimal synchronization. Finally, a numerical example is proposed to show the effectiveness of the theoretical analysis.