# Fault Detection and State Estimation in Automatic Control
## 2 An Overview of Published Articles  
With the current state of science and technology, the modern industrial production scale and the complexity of automation in control systems have greatly improved. Additionally, state estimation and fault detection are particularly important in the production process of these systems before a fault causes any damage; further, testing and maintenance can reduce the risk of accidents, improve the system's security, and reduce the economic loss of production. Therefore, the purpose of this Special Issue is to introduce the latest fault detection algorithms and state estimation methods.  
This Special Issue focuses on intelligent control, intelligent modeling, computational intelligence, artificial intelligence, machine learning, and fault detection. This fits within the scope of Applied Sciences, as the practical applications of fault detection and machine learning are incredibly extensive and important. The research areas of this Special Issue include (but are not limited to) the design and application of fault detection algorithms, state estimation methods, machine learning algorithms, intelligent control systems, and analyzing the characteristics of automatic control systems.  
An analysis of the papers published in this Special Issue is shown in Table 1. Many studies have been conducted by scholars on fault detection and state estimation in the context of automatic control, covering many related research areas. Studies of contribution 1, 3 and 5 are related to automation engineering research; studies of contribution 2, 8, 10, and 11 are related to aircraft control; studies of contribution 4, and 5 are related to sensor control; studies of contribution 6, 12, and 13 are related to robot control; and other studies are more related to system control research.  
Interestingly, studies of contribution 1, and 9 are mostly focused on the prediction of working conditions, where contribution 1 uses a support vector regression algorithm to predict vibrational amplitudes, and contribution 9 uses a long short-term memory network to predict the exhaust temperature of a diesel engine. Studies contribution 3 and contribution 10 both focus on monitoring specific working conditions in real-time as well as analyzing and optimizing the system stability. The authors of contribution 3 analyze the stability of a system under different working conditions based on several factors. The authors of contribution 10 establish a continuous trajectory planning model combined with the ant colony optimization algorithm to monitor the optimal trajectory of an unmanned aerial vehicle.  
Furthermore, in contribution 2, abnormal or faulty behavior is detected by efficiently encoding information about a target pose to recognize various human actions more accurately. The authors of contribution 7 focus on fault identification under specific working conditions and use [PERSON]'s discriminant analysis to diagnose the faults of dissolved oxygen sensors in wastewater treatment plants while evaluating both environmental and economic factors. Meanwhile, the authors of contribution 12 deal with obstacle detection in automatic control applications to achieve real-time obstacle avoidance during a multi-target search by swarm robots.  
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Overall, these studies cover a wide range of industries, including manufacturing, transportation, aerospace, materials, chemicals and geological exploration. The authors are mainly from China, but there are also scholars from Pakistan, the UK, Poland and Romania who have contributed to our Special Issue.