Encrypted control systems have attracted much attention in the control community in recent years. Homomorphic encryption (HE) schemes can not only encrypt the signals transmitted over the communication network but also carry out the calculations needed in dynamic feedback controllers in an encrypted environment. Therefore, both the confidentiality of the signals but also the confidentiality of controller parameters can be protected. In this paper, we focus on several important practical aspects of homomorphic encryption schemes such as computational complexity, network load and storage space. For this purpose, two representative homomorphic encryption schemes, namely, the learning with errors (LWE) scheme and the resilient homomorphic encryption (RHE) scheme will be employed in the investigation with the help of well-established quadruple-tank benchmark process.

Encrypted control systems have received much attention recently to improve the cyber security of industrial control systems. In this paper, an approach is given to reduce network communication load significantly in encrypted control systems. For this purpose, the resilient homomorphic encryption (RHE) scheme proposed in [1] is further developed to explore the Chinese remainder theorem (CRT) so that multiple plaintexts can be encrypted into a single ciphertext. As a result, the amount of ciphertexts transferred over the network is decreased and thus the network communication load is significantly reduced. It is proven that the resulting multi-slot resilient homomorphic encryption (MS-RHE) scheme still keeps the ability of resilience and additive attacks injected into the ciphertexts can still be neutralized. A simulation example of the well-established quadruple-tank benchmark process is used to demonstrate the proposed MS-RHE scheme for encrypted control systems.

Cyber security of networked control systems has attracted much attention in the recent years. A kind of stealthy cyber attacks called mirror attacks, where an adversary replaces the control input signals from the controller by his own attack signals and simultaneously replaces the true sensor output signals by fake output signals generated based on the plant model, can harm the system significantly because the adversary keeps the mirror attack stealthy while taking over the control of the plant. It is also shown that, even without an accurate plant model, the adversary may still manage to keep the mirror attacks stealthy by applying standard control knowledge. Simulation results of the quadruple-tank system are given to illustrate the threat posed by the robust mirror attack.

In this paper a novel data-driven adaptive dynamic programming approach for nonlinear systems with state and input constraints is developed. The basic idea is to transform state constraints into input constraints by introducing a barrier index function. Then a Q-learning approach is designed to solve the optimal control problem considering only input constraints. After that, a data-driven constrained value iteration approach is developed to obtain the optimal control policy. It is shown that the iterative action-value function converges to the optimal action-value function. Finally, a torsional pendulum system is used to illustrate the proposed approach.

MOTIVATION: Within the frame of their genetic capacity, organisms are able to modify their molecular state to cope with changing environmental conditions or induced genetic disposition. As high throughput methods are becoming increasingly affordable, time series analysis techniques are applied frequently to study the complex dynamic interplay between genes, proteins, and metabolites at the physiological and molecular level. Common analysis approaches fail to simultaneously include (i) information about the replicate variance and (ii) the limited number of responses/shapes that a biological system is typically able to take. RESULTS: We present a novel approach to model and classify short time series signals, conceptually based on a classical time series analysis, where the dependency of the consecutive time points is exploited. Constrained spline regression with automated model selection separates between noise and signal under the assumption that highly frequent changes are less likely to occur, simultaneously preserving information about the detected variance. This enables a more precise representation of the measured information and improves temporal classification in order to identify biologically interpretable correlations among the data. AVAILABILITY AND IMPLEMENTATION: An open source F# implementation of the presented method and documentation of its usage is freely available in the TempClass repository, https://github.com/CSBiology/TempClass [58].

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 this paper, a design framework of fault-tolerant wireless networked control systems (NCSs) is developed for industrial automation applications. The main objective is to achieve an integrated parameterization and design of the communication protocols, the control and fault diagnosis algorithms aiming at meeting high real-time requirements in industrial applications. To illustrate the design framework, a laboratory wireless fault-tolerant NCS platform is presented.