In this paper, we propose an approach to design tracking controllers for Boolean control networks to track a time-varying reference output trajectory of finite length by applying the semi-tensor product method. First, we give necessary and sufficient conditions for the trackability of the reference output trajectory. Secondly, in the case of a trackable output trajectory, one control sequence that realizes exact tracking can be found directly by means of backward selection. Otherwise, l_1 and l_inifinity optimization problems can be formulated to find the control sequence that minimizes the tracking error. These optimization problems can be extended to take changes in control inputs into account. An additional advantage of the proposed approaches is that state, transition and input constraints can be easily taken into account during the design of the controllers. Finally, illustrative example is given to demonstrate the proposed approaches.

Rapid developments of offshore wind industry offer a strong demand opportunity for offshore wind turbine remote diagnosis. As offshore wind turbines are often located in harsh and communication-constrained environments, the collection and transmission of data is severely restricted, which poses a serious challenge to the conventional centralized diagnostic paradigm that relies on data aggregation. To address this challenge, we propose a novel event-triggered federated learning framework for decentralized fault diagnosis of offshore wind turbines. Specifically, federated learning is first employed to learn decentralized local knowledge from geographically distributed offshore wind turbines, so that the communication objects are transformed from massive raw data into learned parameters, thereby relieving the communication burden. Then, we design an event-triggered communication mechanism and incorporate it into federated learning, the core of which is to modify the communication requirement from uploading all trained parameters periodically to communicating only when necessary. The proposed framework is verified by a real-world offshore wind turbine dataset from six large wind farms in China. An ablation study shows that the proposed framework can maintain high diagnostic performance while reducing communication costs. A comprehensive comparison based on three benchmark models demonstrates that the proposed framework can reduce the communication burden by up to 63% while obtaining better diagnostic performance. Note to Practitioners —This study was motivated by the problem of collaborative diagnosis of distributed offshore wind turbines under the constraints of data privacy and communication overhead. The method employs a federated learning-based fault diagnosis framework, which permits to obtain global fault diagnosis knowledge without aggregating raw data scattered in each end device, thus avoids the risk of data leakage. Moreover, a strategy integrating parameter variation and accuracy gain is designed to avoid communication redundancy for collaborative training. The practicability and superiority of our proposed framework is demonstrated using extensive experiments against actual industrial data collected from six offshore wind farms.

In cyber-physical systems (CPS) the networked structure not only enhances the accessibility of data but also introduces the risk of cyber attacks. The stealthy attacks that are undetected by the monitoring system, are the most dangerous type of cyber attacks. In this paper, we introduce a new attack strategy to call attention to the problem of stealthy cyber attacks on CPS. The proposed attack scenario does not require access to all control input signals and sensor output signals, so it needs fewer disruption resources and is called a local covert attack. We show that the local covert attack can be made completely stealthy by applying the decoupling technique or by combining the basic idea of a covert attack and a zero-dynamics attack. Simulation results are provided to demonstrate the stealthiness of the proposed local covert attacks.

The widespread use of cyber-physical systems (CPS) not only brings new opportunities with it but also introduces the risk of cyber attacks on the system. One of the most dangerous and sophisticated types of attacks are the stealthy attacks that can hide their influence and are not detectable by conventional anomaly detection schemes. In this paper, a detection method for the replay attack and the covert attack on discrete event systems is proposed. Unexpected behavior is introduced into the transmitted signals by the integration of permutation matrices into the sensor and actuator channels. The permutation disrupts the stealthiness of an attacker and allows the detection of cyber attacks. Moreover, an attack localization scheme is proposed, which allows to determine which signals are attacked in case that the attacker only attacks a subset of communication channels and not the whole system.

In this paper, we propose an approach to design reduced-order state observer for Boolean control networks (BCNs) by applying the semi-tensor product (STP). At first, an approach is given to find reducible state variables. After that, we introduce an approach for reduced-order observer design based on the reducible state variables. Then, it is shown that similar to the Luenberger-like observer, the state estimate provided by the reduced-order observer also converges to real state at time not later than the minimal reconstructibility index. Additionally, the reduced-order observer requires lower computational effort, which facilitates an online implementation.

The reconstructibility analysis and observer design for general Boolean control networks (BCNs) are investigated in this paper. At first, to discriminate reconstructibility from observability, the relationship between different types of observability and reconstructibility existing in the literature is studied. For reconstructibility analysis, explicit and recursive methods are derived. A termination condition is provided to avoid the running of the recursive method longer than necessary. After that, if a general BCN is reconstructible, then an approach for observer design is given. The Luenberger-like observer for general BCNs is introduced to facilitate an online implementation. The state estimate always contains the real state and converges to the real state at a time not later than the minimal reconstructibility index. Finally, examples are given to illustrate the proposed approaches.