Syuan-Yi Chen

Industrial Technology Research Institute, Hsin-chu-hsien, Taiwan, Taiwan

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Publications (13)27.35 Total impact

  • Syuan-Yi Chen, Faa-Jeng Lin
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    ABSTRACT: A decentralized proportional–integral–derivative neural network (PIDNN) control scheme is proposed to regulate and stabilize a fully suspended five degree-of-freedom (DOF) active magnetic bearing (AMB) system which is composed of two radial AMBs (RAMBs) and one thrust AMB (TAMB). First, the structure and operating principles of the five-DOF AMB system are introduced. Then, the adopted differential driving mode (DDM) for the drive system of the AMB is analyzed. Moreover, due to the exact dynamic model of the five-DOF AMB system is vague, a decentralized PIDNN controller is proposed to control the five-axes of the rotor simultaneously in order to confront the uncertainties including inherent nonlinearities and external disturbances effectively. Furthermore, the connective weights of the PIDNN are trained on-line and the convergence analysis of the regulating error is provided using a discrete-type Lyapunov function. Based on the decentralized concepts, the computational burden is reduced and the controller design is simplified. Finally, the experimental results show that the proposed control scheme provides good control performances and robustness for controlling the fully suspended five-DOF AMB system in different operating conditions.
    Engineering Applications of Artificial Intelligence 03/2013; 26(3):962–973. · 1.96 Impact Factor
  • Faa-Jeng Lin, Syuan-Yi Chen, Ming-Shi Huang
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    ABSTRACT: An adaptive complementary sliding-mode control (ACSMC) system with a multi-input-multi-output (MIMO) recurrent Hermite neural network (RHNN) estimator is proposed to control the position of the rotor in the axial direction of a thrust active magnetic bearing (TAMB) system for the tracking of various reference trajectories in this study. First, the operating principles and dynamic model of the TAMB system using a linearized electromagnetic force model is derived. Then, a conventional sliding-mode control (SMC) system is designed for the tracking of various reference trajectories. Moreover, a complementary sliding-mode control (CSMC) system is adopted to reduce the guaranteed ultimate bound of the tracking error by half while using the saturation function as compared with the SMC. Furthermore, since the system parameters and the external disturbance are highly nonlinear and time-varying, the ACSMC is proposed to further improve the control performance and increase the robustness of the TAMB system. In the ACSMC, the MIMO RHNN estimator with estimation laws is proposed to estimate two complicated dynamic functions of the system on-line. In addition, a robust compensator is proposed to confront the minimum approximated errors and achieve the robustness. Finally, some experimental results for the tracking of various reference trajectories show that the control performance of the ACSMC is significantly improved comparing with the SMC and CSMC.
    Control Engineering Practice 07/2011; 19(7):711-722. · 1.67 Impact Factor
  • Syuan-Yi Chen, Faa-Jeng Lin
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    ABSTRACT: This study presents a robust nonsingular terminal sliding-mode control (RNTSMC) system to achieve finite time tracking control (FTTC) for the rotor position in the axial direction of a nonlinear thrust active magnetic bearing (TAMB) system. Compared with conventional sliding-mode control (SMC) with linear sliding surface, terminal sliding-mode control (TSMC) with nonlinear terminal sliding surface provides faster, finite time convergence, and higher control precision. In this study, first, the operating principles and dynamic model of the TAMB system using a linearized electromagnetic force model are introduced. Then, the TSMC system is designed for the TAMB to achieve FTTC. Moreover, in order to overcome the singularity problem of the TSMC, a nonsingular terminal sliding-mode control (NTSMC) system is proposed. Furthermore, since the control characteristics of the TAMB are highly nonlinear and time-varying, the RNTSMC system with a recurrent Hermite neural network (RHNN) uncertainty estimator is proposed to improve the control performance and increase the robustness of the TAMB control system. Using the proposed RNTSMC system, the bound of the lumped uncertainty of the TAMB is not required to be known in advance. Finally, some experimental results for the tracking of various reference trajectories demonstrate the validity of the proposed RNTSMC for practical TAMB applications.
    IEEE Transactions on Control Systems Technology 06/2011; · 2.52 Impact Factor
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    ABSTRACT: An intelligent complementary sliding-mode control (ICSMC) system using a recurrent wavelet-based Elman neural network (RWENN) estimator is proposed in this study to control the mover position of a linear ultrasonic motors (LUSMs)-based X-Y-θ motion control stage for the tracking of various contours. By the addition of a complementary generalized error transformation, the complementary sliding-mode control (CSMC) can efficiently reduce the guaranteed ultimate bound of the tracking error by half compared with the slidingmode control (SMC) while using the saturation function. To estimate a lumped uncertainty on-line and replace the hitting control of the CSMC directly, the RWENN estimator is adopted in the proposed ICSMC system. In the RWENN, each hidden neuron employs a different wavelet function as an activation function to improve both the convergent precision and the convergent time compared with the conventional Elman neural network (ENN). The estimation laws of the RWENN are derived using the Lyapunov stability theorem to train the network parameters on-line. A robust compensator is also proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher-order terms in Taylor series. Finally, some experimental results of various contours tracking show that the tracking performance of the ICSMC system is significantly improved compared with the SMC and CSMC systems.
    IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control 08/2010; · 1.82 Impact Factor
  • Faa-Jeng Lin, Syuan-Yi Chen
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    ABSTRACT: An intelligent integral backstepping sliding mode control (IIBSMC) system using a multi-input multi-output (MIMO) recurrent neural network (RNN) is proposed to control the position of a levitated object of a magnetic levitation system considering the uncertainties in this study. First, the dynamic model of the magnetic levitation system is derived. Then, an integral backstepping sliding mode control (IBSMC) system with an integral action is proposed for the tracking of the reference trajectory. Moreover, to relax the requirements of the needed bounds and discard the switching function in IBSMC, an IIBSMC system using a MIMO RNN estimator is proposed to improve the control performance and further increase the robustness of the magnetic levitation system. The adaptive learning algorithms are derived using Lyapunov stability theorem to train the parameters of the RNN online. Finally, some experimental results of the tracking of periodic sinusoidal trajectory demonstrate the validity of the proposed IIBSMC system for practical applications.
    International Joint Conference on Neural Networks, IJCNN 2010, Barcelona, Spain, 18-23 July, 2010; 01/2010
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    Faa-Jeng Lin, Syuan-Yi Chen, Li-Tao Teng, Hen Chu
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    ABSTRACT: A recurrent functional link (FL)-based fuzzy neural network (FNN) controller is proposed in this study to control the mover of a permanent-magnet linear synchronous motor (PMLSM) servo drive to track periodic reference trajectories. First, the dynamic model of the PMLSM drive system is derived. Next, a recurrent FL-based FNN controller is proposed in this study to control the PMLSM. Moreover, the online learning algorithms of the connective weights, means, and standard deviations of the recurrent FL-based FNN are derived using the back-propagation (BP) method. However, divergence or degenerated responses will result from the inappropriate selection of large or small learning rates. Therefore, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the recurrent FL-based FNN online. Finally, the control performance of the proposed recurrent FL-based FNN controller with IPSO is verified by some simulated and experimental results.
    IEEE Transactions on Magnetics 09/2009; · 1.42 Impact Factor
  • Syuan-Yi Chen, Faa-Jeng Lin, Kuo-Kai Shyu
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    ABSTRACT: A direct modified Elman neural networks (MENNs)-based decentralized controller is proposed to control the magnets of a nonlinear and unstable multi-input multi-output (MIMO) levitation system for the tracking of reference trajectories. First, the operating principles of a magnetic levitation system with two moving magnets are introduced. Then, due to the exact dynamic model of the MIMO magnetic levitation system is not clear, two MENNs are combined to be a direct MENN-based decentralized controller to deal with the highly nonlinear and unstable MIMO magnetic levitation system. Moreover, the connective weights of the MENNs are trained online by back-propagation (BP) methodology and the convergence analysis of the tracking error using discrete-type Lyapunov function is provided. Based on the direct and decentralized concepts, the computational burden is reduced and the controller design is simplified. Furthermore, the experimental results show that the proposed control scheme can control the magnets to track various periodic reference trajectories simultaneously in different operating conditions effectively.
    Neurocomputing. 07/2009;
  • Faa-Jeng Lin, Syuan-Yi Chen, Kuo-Kai Shyu
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    ABSTRACT: In this paper, a robust dynamic sliding mode control system (RDSMC) using a recurrent Elman neural network (RENN) is proposed to control the position of a levitated object of a magnetic levitation system considering the uncertainties. First, a dynamic model of the magnetic levitation system is derived. Then, a proportional-integral-derivative (PID)-type sliding-mode control system (SMC) is adopted for tracking of the reference trajectories. Moreover, a new PID-type dynamic sliding-mode control system (DSMC) is proposed to reduce the chattering phenomenon. However, due to the hardware being limited and the uncertainty bound being unknown of the switching function for the DSMC, an RDSMC is proposed to improve the control performance and further increase the robustness of the magnetic levitation system. In the RDSMC, an RENN estimator is used to estimate an unknown nonlinear function of lumped uncertainty online and replace the switching function in the hitting control of the DSMC directly. The adaptive learning algorithms that trained the parameters of the RENN online are derived using Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher order terms in Taylor series. Finally, some experimental results of tracking the various periodic trajectories demonstrate the validity of the proposed RDSMC for practical applications.
    IEEE Transactions on Neural Networks 06/2009; 20(6):938-51. · 2.95 Impact Factor
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    ABSTRACT: A recurrent functional-link (FL)-based fuzzy-neural-network (FNN) controller with improved particle swarm optimization (IPSO) is proposed in this paper to control a three-phase induction-generator (IG) system for stand-alone power application. First, an indirect field-oriented mechanism is implemented for the control of the IG. Then, an AC/DC power converter and a DC/AC power inverter are developed to convert the electric power generated by a three-phase IG from variable frequency and variable voltage to constant frequency and constant voltage, respectively. Moreover, two online-trained recurrent FL-based FNNs are introduced as the regulating controllers for both the DC-link voltage of the AC/DC power converter and the AC line voltage of the DC/AC power inverter. Furthermore, IPSO is adopted to adjust the learning rates to improve the online learning capability of the recurrent FL-based FNNs. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed recurrent FL-based FNN-controlled IG system.
    IEEE Transactions on Industrial Electronics 06/2009; · 6.50 Impact Factor
  • Faa-Jeng Lin, Ying-Chih Hung, Syuan-Yi Chen
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    ABSTRACT: A field-programmable gate array (FPGA) based computed force control system using an Elman neural network (ENN) is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this paper. First, the structure and operating principle of the LUSM are introduced. Then, the dynamics of the LUSM mechanism with the introduction of a lumped uncertainty, which include the friction force, are derived. Since the dynamic characteristics and motor parameters of the LUSM are nonlinear and time varying, a computed force control system using ENN is designed to improve the control performance for the tracking of various reference trajectories. The ENN with both online learning and excellent approximation capabilities is employed to estimate a nonlinear function including the lumped uncertainty of the moving table mechanism. Moreover, the Lyapunov stability theorem and the projection algorithm are adopted to ensure the stability of the control system and the convergence of the ENN. Furthermore, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. The experimental results show that excellent positioning and tracking performance are achieved, and the robustness to parameter variations and friction force can be obtained as well using the proposed control system.
    IEEE Transactions on Industrial Electronics 05/2009; · 6.50 Impact Factor
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    ABSTRACT: An interval type-2 fuzzy neural network (IT2FNN) control system is proposed to control the position of an X–Y–Theta (X–Y–θ) motion control stage using linear ultrasonic motors (LUSMs) to track various contours. The IT2FNN, which combines the merits of interval type-2 fuzzy logic system (FLS) and neural network, is developed to simplify the computation and to confront the uncertainties of the X–Y–θ motion control stage. Moreover, the parameter learning of the IT2FNN based on the supervised gradient descent method is performed on line. The experimental results show that the tracking performance of the IT2FNN is significantly improved compared to type-1 FNN.
    Neurocomputing 01/2009; · 2.01 Impact Factor
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    ABSTRACT: The robust control of a linear ultrasonic motor based X-Y-thetas motion control stage to track various contours is achieved by using an adaptive interval type-2 fuzzy neural network (AIT2FNN) control system in this study. In the proposed AIT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms are derived using the Lyapunov stability theorem to train the parameters of the IT2FNN online. Furthermore, a robust compensator is proposed to confront the uncertainties including the approximation error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of lumped uncertainty in the robust compensator, an adaptive lumped uncertainty estimation law is also investigated. In addition, the circle and butterfly contours are planned using a nonuniform rational B-spline curve interpolator. The experimental results show that the contour tracking performance of the proposed AIT2FNN is significantly improved compared with the adaptive type-1 FNN. Additionally, the robustness to parameter variations, external disturbances, cross-coupled interference, and frictional force can also be obtained using the proposed AIT2FNN.
    IEEE Transactions on Fuzzy Systems - TFS. 01/2009; 17(1):24-38.
  • Faa-Jeng Lin, Syuan-Yi Chen, Yen-Hung Liu
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    ABSTRACT: A novel recurrent wavelet-based Elman neural network (RWENN) control system is proposed in this study to control the mover position of an X-Y-Ө motion control stage using linear ultrasonic motors (LUSMs) for the tracking of various contours. In the proposed RWENN, each hidden neuron employs a different wavelet function as an activation function. Moreover, the recurrent connective weights are added in the RWENN. Therefore, compared with the conventional Elman neural network (ENN), both the convergence precision and convergence time are improved. Furthermore, the on-line learning algorithm of the REWNN based on the supervised gradient descent method is developed. Finally, some experimental results were carried out using the circle contour for the X-Y axes and the sinusoidal commend for the Ө-axis to test the effectiveness of the proposed RWENN control system.