This paper presents a fast and easy-to-implement acceleration-based parameter identification method for designing limited-angle servo motor drives. First, the servo motor model and position control scheme are introduced. Since controller gains are closely related to mechanical parameters, the impact of parameter mismatch on controller design is analyzed, showing that accurate parameters lead to better control performance. Conventional methods require designing the position or speed controller first and then estimating the actual parameters, which makes it hard to determine the gain of the controller before knowing the parameter. Therefore, this paper proposes an acceleration-based parameter identification method that directly identifies the parameters, which are then used to design the controller. To address cost and space limitations, an acceleration estimation technique is employed instead of installing an accelerometer. Finally, the accuracy of the parameter identification and the improved position control performance are experimentally validated.

This study aims to develop an easy-impement method for bearing health condition monitoring and predicting. Unlike traditional diagnostic methods with vibration sensor signals, this study utilizes motor current signature analysis (CSA) as a condition monitoring method to avoid cost and installation issues. Furthermore, the proposed anomaly detection approach can diagnose with just healthy samples, addressing the issue of insufficient fault samples in fault model training and classifying. This method introduced the complex autoregressive (CAR) model to identify the inherent pattern of health current. It can separate the nonsteady part in motor current caused by irregular vibration of damaged bearing. In addition, to enhance the robustness and accuracy of prognosis, this study uses the projected recursive least squares (P-RLS) for real-time prediction. It is shown to have better performance compared with traditional Bayesian-based methods by limiting the boundary of estimation. To verify the effectiveness of the proposed method, two different data sets are used for evaluation.

This paper examines the management of motor torque in electric bikes (E-bikes) while accounting for various external resistances. In the case of assist electric bikes, the electromagnetic torque generated by the permanent magnet motor can be manipulated to lessen the amount of pedaling torque required from cyclists. However, the overall torque required during riding is influenced by external resistances such as the cyclist's weight, air resistance, friction from the road, and the incline of the road. It's important to regulate the motor torque based on these riding conditions. The primary motion characteristics of an electric bike are analyzed in this paper to determine motor assistance torque. To address the dynamic effects of both pedaling torque and wheel acceleration, four torque control methods are proposed. By aligning the motor torque in the opposite phase to the pedaling torque, fluctuations in acceleration can be minimized. These torque control methods are evaluated through simulations of electric bikes across various resistances. Additionally, an experimental setup is constructed to validate the findings from these cycling simulations.

This paper proposes a high-precision permanent magnet (PM) motor primary used for the small satellite attitude control. A radial-flux type motor is designed to achieve the high demanded smooth torque production for satellite applications. On the basis, a slotless stator is used to improve flux harmonics by removing the stator steel component. Several design methods are proposed to increase the torque output while minimizing the ripples under the slotless topology. First, leakage fluxes caused by slotless stator are reduced by designing the radial-flux distribution with dual rotors. In this structure, two rotors can be used respectively for flux transmitter and flux receiver to concentrate the flux linkage across air gap. Second, rotor magnets are designed with Halbach array for the sinusoidal magnet flux distribution. Halbach array results in more torque production at low ripples. In this paper, finite element analysis (FEA) is used as the simulation tool for the motor design. A prototype is fabricated for the experimental evaluation.

A three-degree-of-freedom (3-DOF) dynamic model-based intelligent nonsingular terminal sliding mode control (INTSMC) system is proposed in this study for the precision contours tracking of a gantry position stage. A Lagrangian equation-based 3-DOF dynamic model for the gantry position stage is derived first. Then, to minimize the synchronous error and tracking error in the precision contours tracking, the 3-DOF dynamic model-based INTSMC system is proposed. In this approach, a nonsingular terminal sliding mode control is designed for the gantry position stage to achieve finite time tracking control. Moreover, to increase the robustness and to improve the control performance, an interval type-2 recurrent fuzzy neural network, and asymmetric membership function, which combines the advantages of interval type-2 fuzzy logic system, recurrent neural network, and asymmetric membership function, is developed as an estimator to approximate a lumped uncertainty. Finally, some experimental results of the gantry position stage for optical inspection application are obtained to show the validity of the proposed control approach.

A digital signal processor (DSP)-based cross-coupled functional link radial basis function network (FLRBFN) control is proposed in this study for the synchronous control of a dual linear motors servo system which is installed in a gantry position stage. The dual linear motors servo system comprises two parallel permanent magnet linear synchronous motors (PMLSMs). First, the dynamics of the field-oriented control PMLSM servo drive with a lumped uncertainty, which contains parameter variations, external disturbance and friction force, is introduced. Then, to achieve accurate trajectory tracking performance with robustness, an intelligent control approach using FLRBFN is proposed for the field-oriented control PMLSM servo drive system. The proposed FLRBFN is a radial basis function network (RBFN) embedded with a functional link neural network (FLNN). The network structure and its on-line learning algorithms for connective weights, means and standard derivations are described in detail. Moreover, since a cross-coupled technology is incorporated into the proposed intelligent control scheme for the gantry position stage, both of the position tracking errors and synchronous errors of dual linear motors will converge to zero, simultaneously. Finally, some experimental results are illustrated to depict the validity of the proposed control approach.

Abstract-In this paper, we propose a T-S fuzzy model based controller for the H-type gantry stage. The synchronized error, which is caused by mismatch quality and friction, is an important control problem for the H-type gantry stage. Utilizing a Lagrangian equation, all states can be formulated as a state equation. Furthermore, the T-S fuzzy model based controller is designed to reduce velocity error, and the position error can also be obtained through internal transformation. Moreover, by utilizing additional transformations, the stabilization problem can be transformed into a linear matrix inequalities (LMIs) problem. Finally, numerical simulation and experimental results are illustrated to demonstrate that the control scheme can effectively reduce synchronized error and tracking error of a gantry stage.

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.

A robust adaptive fuzzy neural network (RAFNN) backstepping control system is proposed to control the position of an X-Y-Theta motion control stage using linear ultrasonic motors (LUSMs) to track various contours in this study. First, an X-Y-Theta motion control stage is introduced. Then, the single-axis dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which includes cross-coupled interference and friction force, is derived. Moreover, a conventional backstepping approach is proposed to compensate the uncertainties occurred in the motion control system. Furthermore, to improve the control performance in the tracking of the reference contours, an RAFNN backstepping control system is proposed to remove the chattering phenomena caused by the sign function in the backstepping control law. In the proposed RAFNN backstepping control system, a Sugeno-type adaptive fuzzy neural network (SAFNN) is employed to estimate the lumped uncertainty directly and a compensator is utilized to confront the reconstructed error of the SAFNN. In addition, the motions at the X axis, Y axis, and Theta axis are controlled separately. The experimental results show that the contour tracking performance is significantly improved and the robustness to parameter variations, external disturbances, cross-coupled interference, and friction force can be obtained, as well using the proposed RAFNN backstepping control system.