Chin-Tsung Hsieh

Nonlinear analysis and control of the uncertain micro-electro-mechanical system by using a fuzzy sliding mode control design

This study analyzes the chaotic behavior of a micromechanical resonator with electrostatic forces on both sides and investigates the control of chaos. A phase portrait, maximum Lyapunov exponent and bifurcation diagram are used to find the chaotic dynamics of this micro-electro-mechanical system (MEMS). To suppress chaotic motion, a robust fuzzy sliding mode controller (FSMC) is designed to turn the chaotic motion into a periodic motion even when the MEMS has system uncertainties.

Maximum power point tracking and optimal Li-ion battery charging control for photovoltaic charging system

Due to the severity of the global energy crisis and environmental pollution, the photovoltaic (PV) system has become one kind of important renewable energy source. Solar energy has the advantages of maximum reserve, inexhaustibleness, and is free from geographical restrictions, thus making PV technology a popular research topic. This study is aimed at developing a PV charging system for Li-ion batteries by integrating Maximum Power Point Tracking (MPPT) and charging control for the battery. In order to enable the solar cell to use the sunlight effectively, a DC/DC boost converter for solar power generation was first designed, which used the MPPT Algorithm of Variable Step Size Incremental Conductance Method (INC) to enable the solar cell to track the MPPT at any time. The output from the DC/DC boost converter then entered the DC/DC buck converter to reduce the voltage for charging purposes. The charging system uses a constant voltage method to charge the Li-ion battery. The PI controller design Constant Voltage (CV) charging method uses a genetic algorithm to determine the optimal gain value. The numerical simulation showed that the PV charging system proposed by this study is easily realized, and can resist the disturbance of external environmental changes, and achieve fast charging.

Research and Development of a Chaotic Signal Synchronization Error Dynamics-Based Ball Bearing Fault Diagnostor

This paper describes the fault diagnosis in the operation of industrial ball bearings. In order to cluster the very small differential signals of the four classic fault types of the ball bearing system, the chaos synchronization (CS) concept is used in this study as the chaos system is very sensitive to a system’s variation such as initial conditions or system parameters. In this study, the Chen-Lee chaotic system was used to load the normal and fault signals of the bearings into the chaos synchronization error dynamics system. The fractal theory was applied to determine the fractal dimension and lacunarity from the CS error dynamics. Extenics theory was then applied to distinguish the state of the bearing faults. This study also compared the proposed method with discrete Fourier transform and wavelet packet analysis. According to the results, it is shown that the proposed chaos synchronization method combined with extenics theory can separate the characteristics (fractal dimension vs. lacunarity) completely. Therefore, it has a better fault diagnosis rate than the two traditional signal processing methods, i.e., Fourier transform and wavelet packet analysis combined with extenics theory.

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.

Study on Unified Chaotic System-Based Wind Turbine Blade Fault Diagnostic System

At present, vibration signals are processed and analyzed mostly in the frequency domain. The spectrum clearly shows the signal structure and the specific characteristic frequency band is analyzed, but the number of calculations required is huge, resulting in delays. Therefore, this study uses the characteristics of a nonlinear system to load the complete vibration signal to the unified chaotic system, applying the dynamic error to analyze the wind turbine vibration signal, and adopting extenics theory for artificial intelligent fault diagnosis of the analysis signal. Hence, a fault diagnostor has been developed for wind turbine rotating blades. This study simulates three wind turbine blade states, namely stress rupture, screw loosening and blade loss, and validates the methods. The experimental results prove that the unified chaotic system used in this paper has a significant effect on vibration signal analysis. Thus, the operating conditions of wind turbines can be quickly known from this fault diagnostic system, and the maintenance schedule can be arranged before the faults worsen, making the management and implementation of wind turbines smoother, so as to reduce many unnecessary costs.

Particle swarm optimization used with proportional–derivative control to analyze nonlinear behavior in the atomic force microscope

An investigation of nonlinear probe behavior in an atomic force microscope, caused by different excitation frequencies, was carried out as well as an analysis and subsequent regulation using particle swarm optimization in combination with proportional–derivative control. The dynamic behavior was resolved by numerical analysis using the atomic force microscope probe governing equations and the properties were examined using phase portrait, bifurcation diagrams, Poincaré maps, and the maximum Lyapunov exponent. The results show that excitation frequency ratio can actuate periodic, sub-harmonic, and chaotic behavior in the system under certain conditions. A proportional–derivative controller was employed to control the chaos, and particle swarm optimization was used to find the proportional–derivative parameters. Integral absolute error was used to evaluate the quality of the parameters for the performance indicator. The generation of nonlinear behavior and a chaotic state may be effectively suppressed to improve the stability and performance of an atomic force microscope system.

Nonlinear behavior analysis and control of the atomic force microscope and circuit implementation

In this paper an analysis of the nonlinear dynamic behavior and control of an atomic force microscope system is described. Phase plane trajectories, spectrum analysis, bifurcation analysis, the Poincare cross-section, the maximal Lyapunov exponent, and other numerical analysis methods were used to observe and verify the dynamic characteristics and the differential equations for the system. The results showed that at specific excitation frequencies, the system will exhibit nonlinear behavior that may be cyclic, multi-cyclic or acyclic. A Psim circuit simulation using the same parameters showed the same nonlinear behavior, as did laboratory circuit implementations. The results also showed than an understanding and control of the dynamic characteristics would not be an easy task. However, they could be used as a basis for the suppression and control of nonlinear behavior and vibration in atomic force microscope systems. A proportional-derivative system was also used, with particle swarm optimization, to find the control parameters Kp and KD and a fuzzy controller was used to compare the results. The controller simulation and hardware implementation both effectively inhibited the nonlinear behavior and were most helpful for the control and enhancement of measurement accuracy.

Chaotic control and circuit implementation of the Atomic Force Microscope system

In this paper an analysis of the nonlinear dynamic behavior and control of an Atomic Force Microscope system is described. Phase plane trajectories, spectrum analysis, bifurcation analysis, the Poincaré map, and the maximum Lyapunov exponent were used to observe and verify the dynamic characteristics for the system. The results show that at specific excitation frequencies, the system will exhibit non-linear behavior that may be cyclic, multi-cyclic or acyclic. A Psim circuit simulation using the same parameters showed the same nonlinear behavior, as did laboratory circuit implementations. A PD system was also used, with particle swarm optimization to find the controller parameters. The controller simulation and hardware implementation both effectively inhibit the nonlinear behavior and this is helpful in the control and enhancement of measurement accuracy in an AFM system.

Control circuit design and chaos analysis in an ultrasonic machining system

Purpose This study aims to investigate the dynamic motion of an ultrasonic machining system comprising two Duffing oscillators, each with a single degree of freedom. After derivation of the differential equations of the system using the Lagrange equations and dimensionless time, numerical analysis was used to observe changes in the system caused by differences in excitation frequency. Design/methodology/approach To suppress this effect and improve performance, proportional differential (PD) control was used. The integral absolute error was used as the fitness function, and particle swarm optimization was used to find the best value for the gain constant of the PD controller. Findings The results showed that with specific changes of excitation frequency, the dynamic motion of the system became nonlinear and chaotic behavior resulted. This made the system unstable and affected performance. Originality/value A range of methods, including fuzzy control, was used to analyze the results, and exhaustive laboratory work was carried out. Means of control were found that were effective in suppressing the chaotic behavior, and differences in response to control were investigated and verified. The findings of this study can be used as a basis for system parameter settings or control circuit design.

Influence of bearing number on high speed air rotor bearing systems

High Speed Air Rotor-Bearing (HSAR) systems have been extensively applied for a variety of mechanical engineering parts, and potential for use in high-rotational speed, high-precision and high stiffness instrumentation. The current study presents a detailed theoretical analysis of bearing performance, in which a hybrid method combing finite difference method (FDM) and differential transformation method (DTM) is used to solve the governing equations of HSAR system. In order to realize under what kind of operating condition the non-periodic motion will occur to the HSAR system, the dynamic behavior of the rotor center is examined with different operating conditions by generating the corresponding bifurcation diagrams, Poincaré maps, and power spectra etc. The results obtained for the orbits of the rotor center are in good agreement with those obtained using the traditional FDM approach. Moreover, the hybrid method increases the accuracy and obtains better numerical stability than the FDM at real operating parameters of the bearing number and computational time-step. The results presented summarize the changes which take place in the dynamic behavior of the HSAR system as the rotor bearing number are increased and therefore provide a useful guideline for the HSAR system.