## 1 Introduction  
In recent years, multi-agent systems (MASs) have been used in wide areas [1]. Distributed systems containing two or more autonomous agents are known as MASs [2], which are the important applications of distributed systems found in real-time systems; one of these systems is multi-UAV systems which can work together to solve complex problems with high efficiency, reliability, and robustness. Formation control of multi-UAV systems is one of the most interesting study issues in the MASs field, and it has a broad range of uses. Agents must communicate to maintain the shape of the formation by exchanging (e.g., velocity, displacement, and/or direction information). The formation control can be classified into different categories: leader-following [3, 4, 5], virtual-based [6, 7], and behavior-based [8, 9, 10]. These days, formation control of multiple UAV systems can be built for military use, personal use, recreation, etc. As technology develops faster than ever before, cyber security has never been more crucial. Multi-UAVs can take care of their cyber security to keep their formation. Most of the previous research considers that attackers insert misleading data (misleading data injection attacks) or attempt to make the system unstable by intercepting transmission (DoS attacks). Then, resilient control or fault-tolerant control can be used to defeat the attacks. The attacks aim to influence robot velocities and displacements in order to produce a physical influence on the formation control of the robot. Robots may vary from desired locations because of displacement attacks. The objectives of velocity attacks are to increase energy consumption and reduce work performance by lowering robot velocity. Secure coordinate control of multiple linear agent systems with linked and unconnected topologies due to connectivity broken/maintained attacks was achieved by [PERSON] et al. [11]. Consequently, an arbitrary Markov model is used to describe a secure coordinate control of multiple linear agent systems under strategic attacks in [12]; however, the secure coordinate control has been designed based on hybrid stochastic control. A secure multi-agent systems synchronization for the case of sensor/controller communication under misleading data injection attacks was addressed in [13]. Accordingly, [PERSON]. [14] investigated a secure consensus control of multi-agent systems for the case of sensor/controller communication under misleading data injection attacks that can result in random packet losses. In addition, [PERSON]. [15] studied the secure consensus control of multi-agent systems based on event-triggered conditions for two cases of controller/actuators and sensor/controller communications under misleading data injection attacks. A secure distributed control of multi-agent systems based on event-triggered control to withstand a certain amount of packet loss caused by denial of service (DoS) attacks was studied in [16]. [PERSON] et al. [17] achieved a secure formation control of multiple nonlinear systems based on hybrid event-triggered control under denial of service (DoS) attacks. Secure model-based formation control of multiple ground vehicles under denial of service (DoS) attacks was proposed in [18]. Secure adaptive formation control of a multi-agent system under actuator attack was developed in [19]. A secure event-triggered control of T-S fuzzy-model-based nonlinear networked systems and T-S fuzzy-model-based five-DOF active semi-vehicle subject to denial of service (DoS) attacks was proposed in [20; 21]. An event-triggered control of stochastic nonlinear systems with unpredictable backlash-like hysteresis and unmeasured states based on adaptive fuzzy was proposed in [22]. The data-driven fuzzy control of tower crane systems was developed in [23]. An adaptive state observer was designed to minimize the impact of cyberattacks on communication. Moreover, the Nussbaum function and back-stepping controller were utilized to stabilize the agent's dynamic model.