جستجوی مقالات مرتبط با کلیدواژه "formation control" در نشریات گروه "برق"

The unique features of multi-rotor unmanned aerial vehicles (MRUAVs) have led to a variety of applications. However, the carrying capacity of MRUAVs remains one of the most critical challenges. Forming an MRUAV swarm can be an effective solution. In MRUAV swarms, followers require a formation control scheme to track one or more leaders. This formation control must address measurement noise, communication delays, model uncertainty, actuator and sensor faults, and cyber-attacks. In this paper, we propose a new cooperative adaptive fault-tolerant formation control for quadrotor swarms in the presence of deception during cyber-attacks. Quadrotors are connected to neighboring drones and a central leader. We evaluate the proposed method's ability to handle model uncertainty, actuator faults, cyber-attacks, and measurement noise through simulation studies. Considering all these challenges simultaneously and evaluating the presented formation control method stand as one of the primary contributions of this paper

The robust adaptive leader-follower formation control of uncertain unmanned surface vehicles (USVs) subject to stochastic environmental loads is investigated in this paper. The stochastic additive noises are included in the kinematics which stands for the un-modeled dynamics and uncertainty. The disturbances induced by waves, wind and ocean currents in the kinetics are also separated into deterministic and stochastic components. A comprehensive model including kinematics and kinetics of each USV agent is then derived as stochastic differential equations including standard Wiener processes. Thus, the problem formulation is much more challenging and practical since both the exogenous disturbances and kinematics states are defined by stochastic differential equations. In order to guarantee that all the tracking errors converge to a ball centered at the origin in probability, quartic Lyapunov functions synthesis, dynamic surface control (DSC) technique, the projection algorithm, and neural networks (NNs) are employed. Finally, the simulation experiments quantify the effectiveness of proposed approach.