Syuan Yi Chen

Robust Nonsingular Terminal Sliding-Mode Control for Nonlinear Magnetic Bearing System

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.

Recurrent Functional-Link-Based Fuzzy-Neural-Network-Controlled Induction-Generator System Using Improved Particle Swarm Optimization

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.

Robust Dynamic Sliding-Mode Control Using Adaptive RENN for Magnetic Levitation System

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.

FPGA-Based Computed Force Control System Using Elman Neural Network for Linear Ultrasonic Motor

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.

Robust Control of an LUSM-Based

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.

X\hbox{--}Y\hbox{--}\theta

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.

Motion Control Stage Using an Adaptive Interval Type-2 Fuzzy Neural Network

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.

Recurrent Functional-Link-Based Fuzzy Neural Network Controller With Improved Particle Swarm Optimization for a Linear Synchronous Motor Drive

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed to control the position of an X-Y-Theta (X-Y-@q) 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-@q 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.

Decentralized PID neural network control for five degree-of-freedom active magneticbearing

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.

Interval type-2 fuzzy neural network control for X-Y-Theta motion control stage using linear ultrasonic motors

A new recurrent wavelet fuzzy neural network (RWFNN) controller is proposed.RWFNN is adopted to control the rotor position of a thrust magnetic bearing (TMB).The online learning algorithm of RWFNN is derived using back-propagation method.The adaptive learning rates are performed via improved particle swarm optimization.Numerical simulations show the validity of TMB using the RWFNN controller. A new recurrent wavelet fuzzy neural network (RWFNN) with adaptive learning rates is proposed to control the rotor position on the axial direction of a thrust magnetic bearing (TMB) mechanism in this study. First, the dynamic analysis of the TMB with differential driving mode (DDM) is derived. Because the dynamic characteristics and system parameters of the TMB mechanism are high nonlinear and time-varying, the RWFNN, which integrates wavelet transforms with fuzzy rules, is proposed to achieve precise positioning control of the TMB. For the designed RWFNN controller, the online learning algorithm is derived using back-propagation method. Moreover, since the improper selection of learning rates for the RWFNN will deteriorate the control performance, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the RWFNN on-line. Numerical simulations show the validity of TMB system using the proposed RWFNN controller with IPSO under the occurrence of uncertainties. Display Omitted