Recurrent-fuzzy-neural-network sliding-mode controlled motor-toggleservomechanism

The dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode controlled motor-toggle servomechanism are described. The servomechanism is a toggle mechanism actuated by a permanent magnet (PM) synchronous servo motor. First, a total sliding-mode control system, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process, is introduced. In the baseline model design a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system the controlled system has a total sliding motion without a reaching phase. Then, to overcome the two main problems with sliding-mode control, a RFNN sliding-mode control system is investigated to control the motor-toggle servomechanism. In the RFNN sliding-mode control system a RFNN bound observer is utilized to adjust the uncertainty bounds in real time. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Simulated and experimental results due to periodic sinusoidal command show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties     

Adaptive fuzzy-neural-network control for induction spindle motor drive

An induction spindle motor drive using synchronous pulse-width modulation (PWM) and dead-time compensatory techniques with an adaptive fuzzy-neural-network controller (AFNNC) is proposed in this study for advanced spindle motor applications. First, the operating principles of a new synchronous PWM technique and the circuit of dead-time compensator are described in detail. Then, since the control characteristics and motor parameters for high-speed-operated induction spindle motor drive are time varying, an AFNNC is proposed to control the rotor speed of the induction spindle motor. In the proposed controller, the induction spindle motor-drive system is identified by a fuzzy-neural-network identifier (FNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the FNNI online. Moreover, the effectiveness of the proposed induction spindle motor-drive system is demonstrated using some simulated and experimental results.     

A high-performance induction motor drive with on-line rotortime-constant estimation

A high-performance induction motor (IM) speed drive with online adaptive rotor time-constant estimation and a proposed recursive least square (RLS) estimator is introduced in this paper. The estimation of the rotor time-constant is on the basis of the model reference adaptive system (MRAS) theory; and the rotor inertia constant, the damping constant and the disturbed load torque of the IM are estimated by the proposed RLS estimator, which is composed of an RLS estimator and a torque observer. Moreover, an integral proportional (IP) speed controller is designed online according to the estimated rotor parameters; and the observed disturbance torque is fed forward to increase the robustness of the induction motor speed drive     

Wavelet neural network control for linear ultrasonic motor drive via adaptive sliding-mode technique

A wavelet neural network (WNN) control system is proposed to control the moving table of a linear ultrasonic motor (LUSM) drive system to track periodic reference trajectories in this study. The design of the WNN control system is based on an adaptive sliding-mode control technique. The structure and operating principle of the LUSM are introduced, and the driving circuit of the LUSM, which is a voltage source inverter using two-inductance two capacitance (LLCC) resonant technique, is introduced. Because the dynamic characteristics and motor parameters of the LUSM are nonlinear and time varying, a WNN control system is designed based on adaptive sliding-mode control technique to achieve precision position control. In the WNN control system, a WNN is used to learn the ideal equivalent control law, and a robust controller is designed to meet the sliding condition. Moreover, the adaptive learning algorithms of the WNN and the bound estimation algorithm of the robust controller are derived from the sense of Lyapunov stability analysis. The effectiveness of the proposed WNN control system is verified by some experimental results in the presence of uncertainties.     

A robust speed controller for induction motor drives

A speed controller considering the effects of parameter variations and external disturbance for indirect field-oriented induction motor drives is proposed in this paper. First a microprocessor-based indirect field-oriented induction motor drive is implemented and its dynamic model at nominal case is estimated. Based on the estimated model, an integral plus proportional (IP) controller is quantitatively designed to match the prescribed speed tracking specifications. Then a dead-time compensator and a simple robust controller are designed and augmented to reduce the effects of parameter variations and external disturbances. The desired speed tracking control performance of the drive can be preserved under wide operating range, and good speed load regulating performance can also be obtained. Theoretic basis and implementation of the proposed controller are detailedly described. Some simulated and experimental results are provided to demonstrate the effectiveness of the proposed controller     

Robust control using neural network uncertainty observer for linearinduction motor servo drive

A robust controller, that combines the merits of integral-proportional (IP) position control and neural network (NN) observed technique, is designed for a linear induction motor (LIM) servo drive in this study. First, the secondary flux of the LIM is estimated using a sliding-mode flux observer on the stationary reference frame and the feedback linearization theory is used to decouple the thrust and the flux amplitude of the LIM. Then, the IP position controller is designed according to the estimated mover parameters to match the time-domain command tracking specifications. Moreover, a robust controller is formulated using the NN uncertainty observer, which is implemented to estimate the lumped uncertainty of the controlled plant, as an inner-loop force controller to increase the robustness of the LIM servo drive system. Furthermore, in the derivation of the online training algorithm of the NN, an error function is used in the Lyapunov function to avoid the real-time identification of the system Jacobian. In addition, to increase the speed and accuracy of the estimated flux, the sliding-mode flux observer is implemented using a 32 bit floating-point digital signal processor (DSP) with a high sampling rate. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results     

Direct decentralized neural control for nonlinear MIMO magnetic levitation system

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.     

RFNN controlled sensorless induction spindle motor drive

A sensorless induction spindle motor drive using synchronous PWM (SPWM) and dead-time compensator with recurrent fuzzy-neural network (RFNN) speed controller is proposed in this study for advanced spindle motor applications. First, the operating principles of a new type SPWM technique and the circuit of dead-time compensator using field-programmable gate arrays (FPGA) are described. Then, a speed observer based on a modified Luenberger observer is adopted to estimate the rotor speed. Moreover, since the control characteristics and motor parameters for a high-speed induction spindle motor drive are time-varying, an RFNN speed controller is developed to reduce the influence of parameter uncertainties and external disturbances. In addition, the RFNN is trained on-line using a delta adaptation law. Finally, the performance of the proposed sensorless induction spindle motor drive system is demonstrated using some simulated and experimental results.     

Self-constructing fuzzy neural network speed controller forpermanent-magnet synchronous motor drive

A self-constructing fuzzy neural network (SCFNN) which is suitable for practical implementation is proposed. The structure and the parameter learning phases are performed concurrently and online in the SCFNN. The structure learning is based on the partition of input space and the parameter learning is based on the supervised gradient decent method using a delta adaptation law. Several simulation and experimental results are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem with the implementation of a permanent-magnet synchronous motor speed drive. Moreover, the simulation results of time varying and nonlinear disturbances are given to show the dynamic characteristics of the proposed controller over a broad range of operating conditions     

An ultrasonic motor drive using a current-source parallel-resonantinverter with energy feedback

An ultrasonic motor (USM) drive using a two-phase current-source parallel-resonant inverter is proposed in this study. A single-phase equivalent model of the USM is first described. Then, a detailed theory for the newly designed driving circuit for the USM, in which the inherent parasitic capacitances formed by the polarized piezoelectric ceramic of the USM are parts of the two parallel-resonant tanks, is introduced. Since the dynamic characteristics of the USM are greatly influenced by the variation in the quality factors of the parallel-resonant tanks, two transformers are added to feed the stored energy in the resonant tanks back to the DC source to reduce the quality factors. Detailed experimental results are provided to demonstrate the effectiveness of the proposed driving circuit