# Resilient Consensus Control for Multi-Agent Systems: A Comparative Survey
## 4 Conclusions and Future Directions  
In conclusion, we have provided a survey regarding some recent developments on resilient consensus control of MASs. To sum up, for the security consensus of MAS under network attack, there are two main solutions: designing elastic control structure or anomaly observer. It involves a wide range of research fields, such as adaptive control, feedback control, robust control for controller design, stochastic process theory and probability statistics knowledge for attack modeling, and \(H_{\infty}\)-control theory and optimization methods for system optimization. Some screening algorithms and judgment algorithms in the computer field can even be applied. The main thinking directions for different cyber attacks can be summarized as the following: for DoS attacks with communication interruption, the key is how to intervene in the control of MASs during the period of network paralysis, so that it will not have irreparable consensus deviation. For example, building a tighter topology or a more accurate state observer is a good method. Since deception attacks that tamper information involve data operations, we can choose from schemes such as detecting and moving out of topology or constructing observers and compensators to compensate the error value in real time; For the Byzantine attack that implements induced confusion, it is necessary to design a better malicious agent screening algorithm or repair scheme. However, the survey is by no means complete. Note that there are still many interesting and yet critical issues concerning MASs under cyber attacks that deserve further study, even though a variety of efficient tools have been successfully developed to solve various challenging problems in this active research field. Some interesting yet important future research issues are provided as follows. 1. Since the effectiveness of network attacks is often accompanied by the appearance of physical faults, an interesting problem is to study network security issues together with fault detection, removal and isolation or integrating other abnormal work issues to improve the practicability of the conclusions, such as saturation problems, measurement noise, communication delay, quantization errors and parameter uncertainty. Especially when complex situations such as mismatched disturbances and multiple time delays are involved, some of the elastic control structures obtained for specific models need to be converted into data-driven ones. Otherwise, it is difficult to play its role. When the detection mechanism confuses faults and attacks, the system may crash. Therefore, the screening methods of the two also belong to the feasible scope of discussion. 2. In the consideration of improving the existing research, another interesting topic is to optimize the control structure, improve the attack stochastic model, achieve more accurate consensus conclusions, relax the theoretical assumptions of the original system and improve the performance of the trigger structure. The further relaxation of the assumptions on the topology network and communication environment is beneficial to the compatibility of the elastic control technology. 3. In the case of actual application, an elastic control scheme need to be designed and modified based on the site situation, so as to boost the feasibility of the excellent theory. For example, in reality, the event-triggered mechanism is often less reliable than the periodic sampling control within the allowable range of communication costs due to noise interference, data clutter, processor performance, sensor sensing abnormalities caused by external factors and actuator failures. Compared with information systems, the control parameter requirements applied to industrial systems are often more stringent because of their high risks. **Funding:** This work was supported in part by the National Natural Science Foundation of China through Grant No. 61803319, 61903278, in part by the Foundation of State Key Laboratory of Automotive Simulation and Control under Grant No.