# Resilient Consensus Control for Multi-Agent Systems: A Comparative Survey
## 3 Resilient Control for MASs
### Byzantine Attack  
Imagine a group of unmanned aerial vehicles (UAVs). If the attacker knows several UAVs in the group in advance and continuously sends wrong navigation data to nearby aircraft groups through them, it is obvious that this will cause the group to deviate from the pre-orientation as a whole. It can even cause the group to crash due to the lack of coordination between the air frames. We call these UAVs that continuously launch attacks \"abnormal agents\". We call the problem that the system must achieve consensus to a variety of state variables in this environment \"elastic consensus\". Abnormal agents are generally divided into two types: fault agents and malicious agents. The former is caused by changes in the environment, without human subjective factors, and may cause abnormal updates of the agent's state; The latter is a malicious agent designed by human beings to damage the testability of the system, which may block the operation of the system. Byzantine attacks describe the attack situation when there are malicious agents in the agent network. Specifically, a malicious agent is an agent that satisfies one of the following three conditions:  
1. The state value will not be updated as set. 2. Do not transmit its real state value to at least one outgoing neighbor. 3. The state values transmitted to different outgoing neighbors at the same time are inconsistent. The focus of their attacks is to send malicious state values to normal agents to make the system disordered. That is, the agent group mistakenly regards the malicious agent as the leader of the system, so that the system state value is guided to a harmful range, as shown in Figure 5. The malicious state value refers to a value that does not conform to the change direction of the normal state value of the system. The key to the problem is to screen out the malicious state value or shield its impact to the maximum extent. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methodologies** & **Attack Location** & **Spoofing Attack Type** & **Centralized** & **Scalability** & **References** \\ \hline Impulse control & Sensor–controller channel & Bernoulli distributed FDI & Moderate & High & [64; 66; 68] \\ \hline Impulse control & Controller– actuator channel & Bernoulli distributed FDI & Moderate & High & [65; 66; 67] \\ \hline Secure control & Neighbor information channel & F-local type & Moderate & High & [70; 71; 73] \\ \hline Distributed filter with adaptive compensator & Neighbor information channel & Stochastic and bounded & Fully distributed & Low & [69] \\ \hline Kalman filter based estimator & Information channel & Conditional probability FDI & Moderate & Low & [60] \\ \hline Neural network based observer & Information channel & White noise based FDI & Moderate & Moderate & [77] \\ \hline Event-trigger output feedback controller & Neighbor information channel & Bernoulli distributed FDI & Moderate & High & [76] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recent Work on Spoofing Attacks. Different from the error state value of the above substitution type spoofing attack, the malicious state value of the Byzantine attack is sent by an agent that originally exists in the system topology, while the former is sent by an external attacker. In terms of detection and security control, there will be differences between the two. To achieve security consensus under the Byzantine attack, there are three main solutions: scheme one is to detect and screen malicious agents in advance, scheme two is to set up trusted decision-making agents, and scheme three is to propose an elastic control structure to maintain the system state or output within a tolerable range. Among them, scheme one is based on the robust structure of the graph, while scheme two generally sets up \"trust nodes\" to relax the robustness conditions of the graph. The two principles are similar.