Overhead crane is an industrial structure that used widely in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane and genetic algorithm, a fuzzy controller is designed with parallel distributed compensation and Linear Quadratic Regulation. Using genetic algorithm, important fuzzy rules are selected and so the number of rules decreased and design procedure need less computation and its computation needs less time. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. The stability analysis and control design problems is reduced to linear matrix inequality (LMI) problems. Simulation results illustrated the validity of the proposed parallel distributed fuzzy LQR control method and it was compared with a similar method parallel distributed fuzzy controller with same fuzzy rule set.

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In this paper, a hybrid method for diagnosing hepatitis diseases is introduced. The proposed method consists of two stages: the feature selection and the classification. The feature selection has been performed by Genetic algorithm (GA) as a fast and common intelligent method for feature selection to reduce the number of employed features. For the classification, a major intelligent classification method, Adaptive Network Fuzzy Inference System (ANFIS), is employed. In this way, a hybrid method of GA-ANFIS is developed and evaluated via a set of experimental data. The results are representative of the out-performance the proposed methods with respect to other methods in the literature considering the classification accuracy as the comparison tool.

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This paper is concerned with secure state estimation of non-linear systems under malicious cyber-attacks. The application of target tracking over a wireless sensor network is investigated. The existence of rotational manoeuvre in the target movement introduces non-linear behaviour in the dynamic model of the system. Moreover, in wireless sensor networks under cyber-attacks, erroneous information is spread in the whole network by imperilling some nodes and consequently their neighbours. Thus, they can deteriorate the performance of tracking. Despite the development of target tracking techniques in wireless sensor networks, the problem of rotational manoeuvring target tracking under cyberattacks is still challenging. To deal with the model non-linearity due to target rotational manoeuvres, an unscented Kalman filter is employed to estimate the target state variables consisting of the position and velocity. A diffusion-based distributed unscented Kalman filtering combined with a trust-based scheme is applied to ensure robustness against the cyber-attacks in manoeuvring target tracking applications over a wireless sensor network with secured nodes. Simulation results demonstrate the effectiveness of the proposed strategy in terms of tracking accuracy, while random attacks, false data injection attacks, and replay attacks are considered.Recently, cyber-physical systems (CPSs) have received widespread attention in different fields of studies, such as industrial automation systems, transportation networks, smart grids, and wireless sensor networks (WSNs) [1,2]. WSNs have a wide range of applications, among which, target tracking is one of the most practical applications. Other applications include environmental monitoring, information collection, and control of unmanned aerial vehicles [3,4]. A typical distributed WSN consists of several sensors that communicate with the rest of the network. In a distributed WSN, a sensor node collaborates with its neighbouring sensors to estimate the states of the target based on a given graph topology. Thus, the problem of target tracking over a WSN is considered as a distributedThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.