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This paper proposes a high-precision permanent magnet (PM) motor primarily used for the satellite attitude control. Considering aerospace applications, the dynamic response, weight and torque ripple are primarily concerns. To achieve the fast response and low ripple, a stator with slotless windings is designed to achieve the ripple free torque production. However, slotless windings contain visible leakage fluxes which might decrease the torque production. In this paper, several design methods are proposed to decrease leakage fluxes by concentrating the flux linkage under slotless topology. First, leakage fluxes caused by slotless windings are minimized through the radial-flux dual-rotor topology. This topology results in the flux linkage concentration in the air gap because two rotors are used separately for flux transmitter and receiver. It is concluded that the dual-rotor is well suited for a slotless windings motor to maximize the air gap flux linkage. Second, Halbach array magnets are chosen to realize the sinusoidal flux linkage distribution. Different array angles are analyzed to minimize the torque ripple. It is shown that the 22.5deg array angle results in the lowest torque ripple among three different Halbach magnets. More importantly, the average torque output is higher comparing to conventional radial magnet magnetization. Although several optimization methods have been developed on the motor geometric design, very few researches focus on the design of dual-rotor slotless windings and air gap length. This design approach further decreases leakage fluxes. In this paper, finite element analysis (FEA) is used for the satellite motor design. In addition, a motor prototype is built for experimental tests.

This paper proposes a high-performance disturbance rejection for the Galvano motor system through sensorless acceleration estimation and acceleration feedback control. The Galvano motor system is wildly used in the laser processing area because of its fast response and high accuracy. To meet the accuracy and response requirements, the Galvano motor is implemented by a permanent magnet (PM) motor with a sine/cosine encoder. Besides, a laser mirror is coupled with the motor for the motion control of the laser beam. Considering the application on high-speed laser drilling, the disturbance torque caused by the unbalanced mirror vibration will affect the drilling accuracy. To minimize the impact of undesired torque vibration, a separated observer is firstly proposed for the instantaneous acceleration estimation. Besides, the acceleration feedback control is proposed for the disturbance torque rejection. According to experimental results, the proposed sensorless acceleration estimation and control improve the disturbance rejection performance on a Galvano motor system.

This paper analyzes the influence of stator windings turn-to-turn fault on inverter-fed permanent magnet (PM) machines. By adding a secondary neutral point (NP) in stator windings, the turn fault detection and tolerant capability can all improve considering a small turn-to-turn short at an early stage. From the perspective on fault detection, the proposed windings with two NPs increase the equipment impedance per NP. Once a turn fault occurs, the fault-induced voltage harmonic magnitude increases. It results in a better signal-to-noise ratio for the fault detection. From the perspective on tolerant drive, the fault-induced circulating currents can flow across different phase coils in the windings with two NPs. Reduced effect on circulating currents in a faulty machine is achieved due to the current diversion. Turn fault induced additional losses are then decreased compared to conventional windings with only one NP. A 1-kW PM machine with two different windings is tested to verify the fault detection and tolerant performance.

Conventional permanent magnet (PM) machine open-phase fault detection relies on the estimation of current harmonics using available phase current sensors. Because the magnitudes of current harmonic are proportional to the machine load condition, it is still a challenge to detect a phase fault at light load when fault-induced current harmonics are too small to estimate. This paper investigates the phase fault detection specifically for the conditions under low fault harmonic magnitudes. Both fault-induced harmonics in dq currents and neutral point (NP) voltage are analyzed to find a suited signal for the fault detection. By adding a voltage sensor to measure the NP voltage in a PM machine, a better fault detection performance is achieved because the fault harmonic in NP voltage results in the reduced effect on torque loads and sensor measurement errors. A 50-W PM machine is tested to verify the phase fault detection performance under low fault harmonic magnitudes. This paper includes the experimental evaluation on the detection limitation in terms of load capability using current and voltage fault signals.

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.

This paper investigates digital implementation issues on high speed permanent magnet (PM) machine drives. The machine operating speed is designed beyond 30krpm where the ratio of controller sample frequency $\mathrm{f}_{\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{ple}}$ over rotor electrical frequency $\mathrm{f}_{\mathrm{e}}$ is less than 5. Because microcontrollers are used in variable frequency drives to realize the field oriented control (FOC), the discretized effects must be considered to maintain the controller stability under low ratios of $\mathrm{f}_{\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}/\mathrm{f}_{\mathrm{e}}$ . In this paper, the FOC based on different current controllers are compared specifically at low $\mathrm{f}_{\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}/\mathrm{f}_{\mathrm{e}}$ . It is shown that current controllers using forward difference and bilinear approximation are able to maintain the drive stability if the voltage delay and indcutance cross-coupling are compensated. However, the drive performance strongly depends on the accuracy of inductance parameter. By contrast, the controller using direct digital design achieves the better FOC performance independent to machine specifications. In this paper, different types of controllers are experimentally compared to find a suited high speed FOC drive.

In this study, an intelligent integral backstepping sliding-mode control IIBSMC system using a recurrent neural network RNN is proposed for the three-dimensional motion control of a piezo-flexural nanopositioning stage PFNS. First, the dynamic model of the PFNS is derived. Then, an integral backstepping sliding-mode control IBSMC system is proposed for the tracking of the reference contours. The steady-state response of the control system can be improved effectively due to the addition of the integrator in the IBSMC. Moreover, to relax the requirements of the bound and discard the switching function in the IBSMC, an IIBSMC system using an RNN estimator is proposed to improve the control performance and the robustness of the PFNS. The RNN estimator is proposed to estimate the lumped uncertainty, including the system parameters and external disturbance, online. Furthermore, the online tuning law for the training of the parameters of the RNN is derived using the Lyapunov stability theorem. In addition, a robust compensator is proposed to confront the minimum reconstructed error occurring in the IIBSMC system. Finally, some experimental results for the tracking of various contours are given to demonstrate the validity of the proposed IIBSMC system. From the performance measurements of the proportional-integral control, sliding mode control, IBSMC, and IIBSMC systems, the proposed IIBSMC system has the lowest maximum, average, and standard deviation of the position tracking errors for three-dimensional motion control of the PFNS.

This paper analyzes the stator windings turn-to-turn fault tolerant capability on inverter-fed permanent magnet (PM) machines. By adding a secondary neutral point (NP) in stator windings, both the fault detection and tolerant capability can be improved considering a small turn-to-turn short at early stage. From the perspective of fault detection, the proposed two NP's windings increase the equipment impedance per NP. Once a turn fault occurs, the fault induced voltage harmonic magnitude can be increased. It results in a better signal-to-noise ratio for the fault detection. From the perspective of tolerant drive, the fault induced circulating currents can flow across different phase coils in the windings with two NP's. Reduced influence of circulating currents on a machine is achieved due to the current diversion. Turn fault induced copper losses are then decreased comparing to conventional windings with only one NP. By using the proposed two NP's windings, the influence of turn fault on PM machines can be minimized. A 1-kW PM machine with two different windings is experimentally evaluated to verify the proposed fault detection and tolerant performance.

Conventional permanent magnet (PM) machine open-phase fault detection relies on the estimation of current harmonics using existing drive current sensors. Because the magnitudes of current harmonic are proportional to the machine load condition, it is still a challenge to detect a phase fault at light load when fault induced current harmonics are too small to estimate. This paper investigates the phase fault detection specifically for the conditions under low fault harmonic magnitudes. Both fault induced harmonics in dq currents and neutral point (NP) voltage are analyzed to find a suited signal for the phase fault detection. By adding a voltage sensor to measure the NP voltage in a PM machine, a better fault detection performance is achieved because the fault harmonic in NP voltage shows the reduced effect on torque loads and sensor measurement errors. A 50-W PM machine is tested to verify the phase fault detection performance under low fault harmonic magnitudes. This paper includes the experimental evaluation to analyze the detection limitation in terms of load capability based on current and voltage fault signals.

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

This paper develops an inertial-sensing-based wearable human activity classification system and its associated human activity classification algorithm for accurately recognizing human daily activity. The proposed system used two inertial sensing modules, which are worn on subjects' wrist and ankle, to collect motion signals of human activities, and utilized the nonparametric weighted feature extraction (NWFE) algorithm to reduce the feature dimensions and improve the classification rate simultaneously. First, we integrated a microcontroller, a triaxial accelerometer, a triaxial gyroscope, and a RF wireless transmission module into the inertial sensing module. Next, the two inertial sensing modules are worn on subjects' wrist and ankle to form a wearable human activity classification system, and collect motion signals generated by daily activities. Subsequently, we developed an inertial-sensing-based human activity classification algorithm composed of signal preprocessing, human motion detection, windowing, feature extraction, feature reduction, and activity classification to recognize the types of human activities. In addition, we also utilized the NWFE algorithm to reduce the feature dimensions and computational complexity, and improve the classification rate. Finally, the experimental results show that the wearable human activity classification system and its human activity classification algorithm can recognize 10 daily activities with the classification rate of 90.5%.

The objective of this study is to develop a multi-sensor data fusion technology based smart home system and its intelligent monitoring interface. The functions of this system include: 1) smart entertainment and household appliances remote control; 2) indoor positioning and intelligent energy management; and 3) home fire prevention. In addition, an intelligent monitoring interface is developed for the smart home system to provide indoor environmental temperature, CO concentration, communicative environmental alarm, household appliances situation, human motion signals, and the results of gesture classification and indoor positioning in real-time. Finally, the abovementioned functions and intelligent monitoring interface are implemented in an experimental model for validating the effectiveness of the smart home system. We have executed the following three research subjects to accomplish the aforementioned objectives: 1) Develop a wearable sensing device placed on hand and its 3D gesture recognition algorithm to implement a real-time, convenient, and low-cost household appliances remote control system. 2) Develop a wearable sensing device placed on foot and its indoor pedestrian positioning algorithm to realize an effective pedestrian navigation system for smart energy management. 3) Develop a multi-sensor circuit and its intelligent fire prevention and alarm algorithm to realize an intelligent fire prevention and alarm system for home fire prevention. The experimental results have successfully validated the effectiveness of the proposed smart home system and its intelligent monitoring interface.

This paper presents a self-designed wearable inertial sensing module and its associated drift modeling and compensation algorithm to obtain drift-removed signals for handwriting trajectory reconstruction. Users can utilize the wearable inertial sensing module to write numerals at normal speed, which consists of a microcontroller, a triaxial accelerometer, a uniaxial gyroscope, a biaxial gyroscope, and an RF wireless transmission module. The accelerations and angular velocities generated by the handwriting movements are transmitted to a computer via the RF module. In order to minimize the integral or cumulative errors caused by the intrinsic drift of the microelectro-mechanical-system (MEMS)-based gyroscopes, we have developed a drift modeling and compensation algorithm for removing the drift of the gyroscopes and increasing the accuracy of trajectory reconstruction. In the proposed drift modeling and compensation algorithm, a Wiener-type recurrent neural network (WRNN), which consists of a dynamic linear model cascaded by a static nonlinear model and can be expressed in a state-space representation, is used to model the drift of the gyroscopes. Our experimental results on handwriting trajectory reconstruction have successfully validated the effectiveness of the self-designed wearable inertial sensing module and its drift modeling and compensation algorithm.

A cross-coupled proportional-integral-derivative neural network (PIDNN) 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. 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 PIDNN is proposed for the field-oriented control PMLSM servo drive system. In the proposed approach, the on-line learning algorithms of the PIDNN are derived using back-propagation (BP) method to guarantee the convergence of the network. Finally, some experimental results are illustrated to depict the validity of the proposed control approach.

A computed force control system using functional link radial basis function network with asymmetric membership function (FLRBFN-AMF) for three-dimension motion control of a piezo-flexural nanopositioning stage (PFNS) is proposed in this study. First, the dynamics of the PFNS mechanism with the introduction of a lumped uncertainty including the equivalent hysteresis friction force are derived. Then, a computed force control system with an auxiliary control is proposed for the tracking of the reference contours with improved steady-state response. Since the dynamic characteristics of the PFNS are non-linear and time varying, a computed force control system using FLRBFN-AMF is designed to improve the control performance for the tracking of various reference trajectories, where the FLRBFN-AMF is employed to estimate a non-linear function including the lumped uncertainty of the PFNS. Moreover, by using the asymmetric membership function, the learning capability of the networks can be upgraded and the number of fuzzy rules can be optimised for the functional link radial basis function network. Furthermore, the adaptive learning algorithms for the training of the parameters of the FLRBFN-AMF online are derived using the Lyapunov stability theorem. Finally, some experimental results for the tracking of various reference contours of the PFNS are given to demonstrate the validity of the proposed control system.

A three-degree-of-freedom (3-DOF) dynamic model based interval type-2 recurrent fuzzy neural network (IT2RFNN) control system is proposed in this study for a gantry position stage. To consider the effect of inter-axis mechanical coupling, a Lagrangian equation based 3-DOF dynamic model for gantry position stage is derived first. Then, to minimize the synchronous error and tracking error of the gantry position stage, the 3-DOF dynamic model based IT2RFNN control system is proposed. In this approach, the adaptive learning algorithms of the IT2RFNN on-line are derived from the Lyapunov stability theorem. Finally, some experimental results of optical inspection application are illustrated to show the validity of the proposed control approach.

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-based cross-coupled functional link (FL) radial basis function network (FLRBFN) control is proposed in this study for the synchronous control of a dual linear motors servo system that 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 embedded with a FL neural network. Moreover, to guarantee the convergence of the FLRBFN, a discrete-type Lyapunov function is provided to determine the varied learning rates of the FLRBFN. In addition, since a cross-coupled technology is incorporated into the proposed intelligent control scheme for the gantry position stage, both 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.

A field-programmable gate array (FPGA)-based intelligent-complementary sliding-mode control (ICSMC) is proposed in this paper to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo-drive system to track periodic-reference trajectories. First, the dynamics of the field-oriented control PMLSM servo drive with a lumped uncertainty, which contains parameter variations, external disturbances, and nonlinear-friction force, is derived. Then, to achieve the required high-control performance, the ICSMC is developed. In this approach, a radial-basis function-network (RBFN) estimator with accurate approximation capability is employed to estimate the lumped uncertainty directly. Moreover, the adaptive-learning algorithms for the online training of the RBFN are derived using the Lyapunov theorem to guarantee the closed-loop stability. Furthermore, the FPGA chip is adopted to implement the developed control and online learning algorithms for possible low-cost and high-performance industrial applications using PMLSM. Finally, some experimental results are illustrated to show the validity of the proposed control approach.

In this study, a filtering-type sliding-mode control with a radial basis function network (FSCRBFN) for a two-axis motion control system, which consists of two permanent magnet linear synchronous motors (PMLSMs), is proposed. First, the dynamics of the single-axis motion system with a lumped uncertainty which contains parameter variations, external disturbances, cross-coupled interference and non-linear friction force is derived. Next, a filtering-type sliding-mode control (FSC) is adopted for the two-axis motion control system to confront the lumped uncertainty. Then, to improve the control performance in contour tracking, the FSCRBFN control approach is developed. In the control approach, a radial basis function network (RBFN) is employed mainly to estimate the lumped uncertainty. Moreover, the proposed control approach is performed on a digital signal process (DSP)-based control system using TMS320C32. Finally, some experimental results are illustrated to show the validity of the proposed control approach.

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.

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.

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN 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 that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system.

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.

A robust fuzzy neural network controller with nonlinear disturbance observer (RFNNCNDO) is proposed for the precision control of a two-axis motion control system. The adopted two-axis motion control system is composed of two permanent magnet linear synchronous motors (PMLSMs). The single-axis motion dynamics with the introduction of a lumped disturbance, which includes parameter variations, external disturbances, cross-coupled interference between the two PMLSMs and fiction force, is derived. Then, a nonlinear disturbance observer is applied to estimate the lumped disturbance and a feedback linearisation controller is adopted to stabilise the control system. However, the system responses are degraded by the existed observer error. To improve the control performance in the tracking of the reference contours, a Sugeno-type adaptive fuzzy neural network (SAFNN) is employed in the proposed RFNNCNDO to estimate the observer error directly. The online learning algorithms of the SAFNN guarantee the stability of closed-loop systems on the basis of the Lyapunov theorem. Moreover, the proposed control algorithms are implemented in a TMS320C32 digital signal processor (DSP)-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved and the robustness can be obtained as well using the proposed RFNNCNDO control system.

A self-constructing Sugeno type adaptive fuzzy neural network (SAFNN) control system is proposed in this study for the contour tracking control of a two-axis motion control system. The adopted two-axis motion control system is composed of two permanent magnet linear synchronous motors (PMLSMs). The proposed SAFNN combines the merits of a self-constructing fuzzy neural network (SCFNN) and a TSK-type fuzzy inference mechanism. Moreover, the structure and the parameter learning phases are performed concurrently and on line in the SAFNN. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. Furthermore, the proposed control algorithms are implemented in a TMS320C32 DSP-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved and robustness can be obtained as well using the proposed SAFNN control system.

We propose a recurrent radial basis function network-based (RBFN-based) fuzzy neural network (FNN) to control the position of the mover of a field-oriented control permanent-magnet linear synchronous motor (PMLSM) to track periodic reference trajectories. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, it performs the structureand parameter-learning phases concurrently. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method, using a delta adaptation law. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties