Robust Controller Design for Modified Projective Synchronization of Chen-Lee Chaotic Systems with Nonlinear Inputs

This study demonstrates the modified projective synchronization in Chen-Lee chaotic system. The variable structure control technology is used to design the synchronization controller with input nonlinearity. Based on Lyapunov stability theory, a nonlinear controller and some generic sufficient conditions can be obtained to guarantee the modified projective synchronization, including synchronization, antisynchronization, and projective synchronization in spite of the input nonlinearity. The numerical simulation results show that the synchronization and antisynchronization can coexist in Chen-Lee chaotic systems. It demonstrates the validity and feasibility of the proposed controller

Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT) is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly

Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT) for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed

Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis

Solar energy heliostat fields comprise numerous sun tracking platforms. As a result, fault detection is a highly challenging problem. Accordingly, the present study proposes a cerebellar model arithmetic computer (CMAC) neutral network for automatically diagnosing faults within the heliostat field in accordance with the rotational speed, vibration, and temperature characteristics of the individual heliostat transmission systems. As compared with radial basis function (RBF) neural network and back propagation (BP) neural network in the heliostat field fault diagnosis, the experimental results show that the proposed neural network has a low training time, good robustness, and a reliable diagnostic performance. As a result, it provides an ideal solution for fault diagnosis in modern, large-scale heliostat fields

Circuit Implementation and Synchronization Control of Chaotic Horizontal Platform Systems by Wireless Sensors

Horizontal platform system (HPS) produces a nonlinear behavior from precision machinery systems. This mechanical system is implemented mainly in offshore areas or earthquake engineering. However, elucidating or controlling this non-linear behavior of mechanical systems is extremely difficult and time consuming. Therefore, in addition to developing an electronic circuit to implement HPS, this work designs a sliding mode control (SMC) for synchronizing the state trajectories of two horizontal platform systems, subsequently allowing us to easily understand the HPS, perform more detailed analysis, and achieve further control. Experimental results demonstrate the feasibility of implementing the HPS by the proposed electronic circuit system. Comparing the proposed electronic circuitry designs and the HPS of computer simulation reveals that the results of the non-linear dynamic behavior correlate well with each other. Finally, based on use of the control technology, master-slave chaos synchronization with sliding mode control is achieved by wireless sensors

Application of hybrid microwave thermal extraction techniques for mulberry root bark

The main focus of this paper is the extraction of compounds from the mulberry root bark using a hybrid microwave thermal process. The shearing mechanism and an integrated circulation system, which increases the rate of contact between the solvent and extractive, are studied. The results are analyzed by the Taguchi method and verified by high performance liquid chromatography. Furthermore, the optimal operating parameters of the extraction of mulberry root bark are discussed. The results show that hybrid microwave thermal extraction can successfully extract the active ingredients from mulberry root bark. This is a reliable basis for further researches

Robust Exponential Converge Controller Design for a Unified Chaotic System with Structured Uncertainties via LMI

This paper focuses on the chaos control problem of the unified chaotic systems with structured uncertainties. Applying Schur-complement and some matrix manipulation techniques, the controlled uncertain unified chaotic system is then transformed into the linear matrix inequality (LMI) form. Based on Lyapunov stability theory and linear matrix inequality (LMI) formulation, a simple linear feedback control law is obtained to enforce the prespecified exponential decay dynamics of the uncertain unified chaotic system. Numerical results validate the effectiveness of the proposed robust control scheme

Chaos Analysis and Synchronization Control of Coronary Artery Systems

The present study uses the differential transformation method to solve the governing equations of the coronary artery system and then analyzes the dynamic behavior of the system by means of phase portraits, power spectra, bifurcation diagrams, and Poincaré maps. Also, a master-slave control system is proposed to suppress the nonlinear chaotic behavior of the coronary artery system. The results show that the dynamic behavior of the coronary artery system is significantly dependent on the magnitude of the vibrational amplitude. Specifically, the motion changes from T-periodic to 2T-periodic, then from 4T-periodic to 8T-periodic, and finally to chaotic motion with windows of periodic motion as the vibrational amplitude is increased from 0.3 to 0.6 and from 4.5 to 5.9. In addition, it is shown that the proposed control scheme enables the coronary artery system to be synchronized to any state asymptotically such that the risk of cardiopathy is reduced

New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network

A hybrid method comprising a chaos synchronization (CS)-based detection scheme and an Extension Neural Network (ENN) classification algorithm is proposed for power quality monitoring and analysis. The new method can detect minor changes in signals of the power systems. Likewise, prominent characteristics of system signal disturbance can be extracted by this technique. In the proposed approach, the CS-based detection method is used to extract three fundamental characteristics of the power system signal and an ENN-based clustering scheme is then applied to detect the state of the signal, i.e., normal, voltage sag, voltage swell, interruption or harmonics. The validity of the proposed method is demonstrated by means of simulations given the use of three different chaotic systems, namely Lorenz, New Lorenz and Sprott. The simulation results show that the proposed method achieves a high detection accuracy irrespective of the chaotic system used or the presence of noise. The proposed method not only achieves higher detection accuracy than existing methods, but also has low computational cost, an improved robustness toward noise, and improved scalability. As a result, it provides an ideal solution for the future development of hand-held power quality analyzers and real-time detection devices