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.
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.
We looked at the background of fault-detection and fault-tolerant control algorithms to propose a new high efficiency one with a focus on Tennessee Eastman process through fuzzy-based neural network representation. Due to the fact that the openloop system may not be stabilized, an advanced control strategy to generate proper control signals needs to be designed. At first, to detect and identify the fault, data preprocessing theories have been considered. Based upon the matter disclosed, to provide a reliable decision-maker block, fusion classifier idea has been realized. For this one, raw data, time, and frequency characteristics are divided into various classification tools and finally the obtained knowledge combination regarding each one of them is adopted. It should be noted that the proposed implementation tools are taken into real consideration as the fuzzy-based neural network representation. Subsequently, the fault-tolerant control approach based on local controller regulation in case of each fault occurrence has been researched, which the investigated outcomes emphasize the effectiveness of the approach proposed here.
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.
In this paper, a new type of anti-synchronization called adaptive modified function projective antisynchronization was presented. In this study, state variables of drive system would be antisynchronized with state variables of response system up to desired scale function matrix. The adaptive control law and the parameter updates were determined to make the states of two Lorenz systems modified function projective anti-synchronized by using Lyapunov stability theory. Numerical simulations were presented to verify the effectiveness of this control method.
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