Fangzhou Xia

@article{2024IEEETNNLS,

title = {SymPO:One-Pass Fault Prediction For Non-Stationary Dynamics},

author = {Yip Fun Yeung and Fangzhou Xia* and Mikio Furokawa and Takayuki Hirano and Kamal Youcef-Toumi},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {IEEE Transactions on Neural Network and Learning Systems (under review)},

keywords = {Automation, Intelligence, Mechatronics, Method, Modeling & Simulation, Signal Processing, Theory},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{2024IEEETPEL,

title = {Time Delay based Neural Network Control of Permanent Magnet Synchronous Motors},

author = {Abhishek Patkar and Qinghui Meng* and Hanrui Wang and Fangzhou Xia* and Kamal Youcef-Toumi},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {IEEE Transactions on Power Electronics (under review)},

keywords = {Automation, Design, Experimentation, Instrumentation, Intelligence, Mechatronics, Modeling & Simulation, Motion Control, Theory},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{2024TMech,

title = {Flexure-based Multi-actauted Vibration Suppression System for OHT Wafer Transfer Vehicles},

author = {Jiajie Qiu^ and Hongjin Kim^ and Fangzhou Xia* and Kamal Youcef-Toumi},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {IEEE/ASME Transactions on Mechatronics (in preparation)},

publisher = {IEEE},

keywords = {Automation, Experimentation, Instrumentation, Mechatronics, Modeling & Simulation, Motion Control},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{2023IEEESensors,

title = {A Doppler Radar with a Sweeping Lock-in Demodulator for Machine Vibration Sensing},

author = {Lois Wampler^ and Fangzhou Xia^* and Yip Fun Yeung and Takayuki Hirano and Ali Alshehri and Mikio Furokawa and Kamal Youcef-Toumi},

doi = {10.1109/JSEN.2023.3325820},

year  = {2023},

date = {2023-10-31},

urldate = {2023-10-31},

journal = {IEEE Sensors Journal},

abstract = {Data-driven predictive maintenance of modern machinery has the potential to increase equipment lifespan and decrease manufacturing costs. Vibration analysis can effectively diagnose potential problems in machines. A Doppler radar can be used as a sensor that provides non-contact, inexpensive real-time machine vibration data collection without necessitating line-down time. Implementation with Software Defined Radio (SDR) allow easy adjustment of excitation signals based on application needs. Conventional Fast Fourier Transformation based vibration analysis requires large amounts of data to achieve high spectral resolution needed for fault detection, which can be inefficient and computationally too expensive. In this work, we propose to use a sweeping lock-in amplifier to achieve high frequency resolution with small amounts of data by processing windowed sections of Doppler-shifted radio signals. This algorithm can reliably measure the Doppler shift frequency corresponding to the travelling speed of a moving object and identify the frequency of oscillating target with small amplitude, with the latter widely present in machine vibration. The distinguishing condition of the two cases is mathematically derived. The proposed algorithm is studied in simulation with triangular displacement waveform for simplicity of analysis and sinusoidal waveform for generic applications. For experimental verification, speaker vibration at a known frequency is analyzed to achieve an accuracy of 0.025 Hz within the known vibration frequency. This method is robust to the presence of noise frequencies and capable of detecting multiple frequencies.},

keywords = {Automation, Experimentation, Instrumentation, Mechatronics, Modeling & Simulation, Sensor, Signal Processing, Theory},

pubstate = {published},

tppubtype = {article}

}

@article{2023IEEETM,

title = {AFM SMILER: A Scale Model Interactive Learning Extended Reality Toolkit for Atomic Force Microscopy based on Digital Twin Technology},

author = {Fangzhou Xia* and Shane Lovet and Eyan Forsythe and Malek Ibrahim and Kamal Youcef-Toumi},

url = {https://ieeexplore.ieee.org/abstract/document/10136835},

doi = {10.1109/TMECH.2023.3274695},

year  = {2023},

date = {2023-06-01},

urldate = {2023-01-01},

journal = {IEEE Transactions on Mechatronics (in preparation)},

abstract = {Atomic force microscope (AFM) is a precision mechatronic system for nanoscale imaging of surfaces. Due to limited instrument access and lack of visualization techniques, understanding its principles can be challenging. Digital twin technology allows the creation of virtual representations of physical systems, which can be particularly useful to address challenges in AFM education. To realistically simulate nanoscale physics, we first developed new efficient algorithms for four virtual scale models, including cantilever mechanics, probe transducers, controller tuning, and contact mechanics. Second, three simulated experiment interactive learning modules are developed for instrument operation, including virtual imaging, system overview, and imaging modalities. In the end, three hardware systems are integrated for an extended reality experience, including a macroscopic AFM scale model, a haptic device for probe-sample interaction force feedback and an upgraded low-cost educational AFM for nanoscale imaging and instrumentation. This completes the eight total modules for the AFM SMILER: A Scale Model Interactive Learning Extended Reality toolkit. Preliminary studies shows the toolkit being helpful for AFM education. In addition to mechatronics and nanotechnology education, techniques developed in this work can be generally applied to computationally efficient realistic digital twin creation.},

keywords = {Atomic Force Microscopy, Design, Education, Experimentation, Instrumentation, Intelligence, Mechatronics, MEMS, Modeling & Simulation, Nanorobotics, Sensor, Theory},

pubstate = {published},

tppubtype = {article}

}

@article{2023Machines,

title = {Disturbance Rejection Control for Active Vibration Suppression of OHT Wafer Transfer Vehicles},

author = {Jiajie Qiu^ and Hongjin Kim^ and Fangzhou Xia* and Kamal Youcef-Toumi},

url = {https://www.mdpi.com/2075-1702/11/2/125},

doi = {10.3390/machines11020125},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Machines},

volume = {11},

number = {2},

pages = {125},

abstract = {In modern semiconductor fabrication plants, automated Overhead Hoist Transport (OHT) vehicles transport wafers in Front Opening Unified Pods (FOUPs). Even in a cleanroom environment, small particles excited by the mechanical vibration of the FOUP can still damage the chips if such particles land on the critical area of the wafers. To minimize the vibration excitation force transferred to the FOUP, this research focuses on controlling the vibration displacement level of an OHT Hand Unit—interface between the OHT vehicle and the FOUP. However, since the OHT vehicle and the FOUP keep traveling, the target system is floating and there exists no external anchoring point for a controlling force source. In addition, no sensor attachments are permitted on mass-production FOUPs, which makes this vibration level suppression problem more challenging. In this research, a custom testbed is designed to replicate the acceleration profile of the OHT vehicle under its travel motion. Then, system modeling and identification is conducted using simulation and experiment to verify the fabricated testbed design. Finally, a Disturbance Observer-Based Controller (DOBC) is developed and implemented on a custom active vibration suppression actuator with inertia force-based counterbalancing to reduce peak vibration amplitude from 870 µm to 230 µm.},

keywords = {Actuator, Automation, Design, Experimentation, Instrumentation, Mechatronics, Modeling & Simulation, Motion Control, Theory},

pubstate = {published},

tppubtype = {article}

}

@article{2021_MSSP,

title = {Comprehensive study of charge-based motion control for piezoelectric nanopositioners: Modeling, instrumentation and controller design},

author = {Chen Yang* and Fangzhou Xia and Yi Wang and Kamal Youcef-Toumi},

url = {https://www.sciencedirect.com/science/article/pii/S0888327021008219},

doi = {https://doi.org/10.1016/j.ymssp.2021.108477},

issn = {0888-3270},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Mechanical Systems and Signal Processing},

volume = {166},

pages = {108477},

abstract = {High performance motion control of piezoelectric nanopositioners is crucial for a wide range of applications. The primary challenges stem from several aspects, including hysteresis and creep effects, along with the lightly damped mechanical resonance. In view of these issues, this paper proposes a comprehensive charge-based motion control solution, including a generalized electromechanical model, a charge controller with non-resistive DC stabilization and a robust charge control (RCC) strategy. The advantages of charge-based control in system identification and controller design are clearly indicated. Towards this solution, a generalized model of piezoelectric nanopositioner is first proposed, showing the effectiveness of charge control approach in the presence of arbitrarily complicated mechanical dynamics. Furthermore, we present a charge controller with a simple configuration, which eliminates the frequency-dependent performance of classical controller design. This guarantees consistently high control performance over the full operating bandwidth. Finally, to deal with the mechanical resonance and remaining nonlinearities and uncertainties, we propose a control strategy that combines charge control and robust feedback control into a single framework. Superior tracking and damping control performance of the proposed solution is confirmed by extensive experimental validations.},

keywords = {Actuator, Experimentation, Instrumentation, Mechatronics, Modeling & Simulation, Motion Control, Piezoelectricity, Theory},

pubstate = {published},

tppubtype = {article}

}

@article{2020IEEETIE,

title = {Modeling and Control of Piezoelectric Hysteresis: A Polynomial-Based Fractional Order Disturbance Compensation Approach},

author = {Chen Yang* and Nicolas Verbeek and Fangzhou Xia and Yi Wang and Kamal Youcef-Toumi},

url = {https://ieeexplore.ieee.org/document/9027124},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Transactions on Industrial Electronics},

volume = {68},

number = {4},

pages = {3348-3358},

publisher = {IEEE},

abstract = {Piezoelectric hysteresis is a critical issue that significantly degrades the motion accuracy of piezo-actuated nanopositioners. Such an issue is difficult to be precisely modeled and compensated for, primarily due to its asymmetric, rate, and input amplitude-dependent characteristics. This article proposes a novel method to deal with this challenge. Specifically, a polynomial-based fractional order disturbance model is proposed to accommodate and characterize the complex hysteresis effect. In this model, the rate dependence is captured by a general method of implementing curve fitting in Bode magnitude plot. The inverse model for control purposes is immediately available from the original one. The proposed method does not require expensive computational resources. In fact, this article shows that this controller can be easily implemented in an analog manner, which brings the advantages of high bandwidth and low cost. Extensive modeling and tracking experiments are carried out to demonstrate the effectiveness of the proposed method. It is shown that the piezoelectric hysteresis nonlinearity can be significantly suppressed over a wide bandwidth.},

keywords = {Actuator, Experimentation, Instrumentation, Mechatronics, Modeling & Simulation, Motion Control, Piezoelectricity, Theory},

pubstate = {published},

tppubtype = {article}

}

@article{2020IEEETIE_2,

title = {Statically Stable Charge Sensing Method for Precise Displacement Estimation of Piezoelectric Stack-Based Nanopositioning},

author = {Chen Yang* and Nicolas Verbeek and Fangzhou Xia and Yi Wang and Kamal Youcef-Toumi},

url = {https://ieeexplore.ieee.org/abstract/document/9177358},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Transactions on Industrial Electronics},

volume = {68},

number = {9},

pages = {8550-8560},

publisher = {IEEE},

abstract = {Piezoelectric self-sensing is a promising displacement estimation technique that has captured a broad range of attention since its origin. However, one long-standing and critical issue associated with this technique is its unstable static performance. This issue significantly undermines the sensing accuracy and the long-term reliability, which are primary concerns in industrial applications. In this article, we show that this challenging issue can be finally solved. The proposed solution consists of two steps. First, we develop a novel charge sensing circuit that provides an explicit access to observe any disturbance that might impact the static sensing. Second, by introducing cascaded observers, a self-improvement sensing scheme is developed. High accuracy displacement estimate can, thus, be reconstructed by fusing the reference signal, the observed disturbance, and the original sensing signal together. Experimental validations demonstrate the expected stable performance. The root-mean-square sensing errors are well below 1%. The developed self-sensing technique is a competitive solution for nanopositioning applications, also due to its self-contained, immunity to environmental disturbance, and plug-and-play features.},

keywords = {Actuator, Experimentation, Instrumentation, Mechatronics, Modeling & Simulation, Motion Control, Piezoelectricity, Theory},

pubstate = {published},

tppubtype = {article}

}