An adaptive fuzzy-neural-network controller for ultrasonic motor drive using the LLCC resonant technique

In this study an adaptive fuzzy-neural-network controller (AFNNC) is proposed to control a rotary traveling wave-type ultrasonic motor (USM) drive system. The USM is derived by a newly designed, high frequency, two-phase voltage source inverter using two inductances and two capacitances (LLCC) resonant technique. Then, because the dynamic characteristics of the USM are complicated and the motor parameters are time varying, an AFNNC is proposed to control the rotor position of the USM. In the proposed controller, the USM 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 on line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNNI and the optimal learning rate of the adaptive controller. In addition, the effectiveness of the adaptive fuzzy-neural-network (AFNN) controlled USM drive system is demonstrated by some experimental results.     

Hybrid controller with recurrent neural network for magnetic levitation system

We propose a hybrid controller using a recurrent neural network (RNN) to control a levitated object in a magnetic levitation system. We describe a nonlinear dynamic model of the system and propose a computed force controller, based on feedback linearization, to control the position of the levitated object. To relax the requirement of the lumped uncertainty in the design of the computed force controller, an RNN functions as an uncertainty observer to adapt the lumped uncertainty on line. The computed force controller, the RNN uncertainty observer, and a compensated controller are embodied in a hybrid controller, which is based on Lyapunov stability. The computed force controller, with the RNN uncertainty observer, is the main tracking controller, and the compensated controller compensates the minimum approximation error of the RNN uncertainty observer. To ensure the convergence of the RNN, the adaptation law of the RNN is modified by using a projection algorithm. Experimental results illustrate the validity of the proposed control design for the magnetic levitation system.     

Robust control of linear ceramic motor drive with LLCC resonant technique

This study presents a robust control system for a linear ceramic motor (LCM) that is driven by a high-frequency voltage source inverter using two-inductance two-capacitance (LLCC) resonant technique. The structure and driving principle of the LCM are introduced. Because the dynamic characteristics and motor parameters of the LCM are nonlinear and time varying, a robust control system is designed based on the hypothetical dynamic model to achieve high-precision position control. The presentation of robust control for the LCM drive system is divided into three parts, which comprise state feedback controller, feed-forward controller, and uncertainty controller. The adaptation laws of control gains in the robust control system are derived in the sense of Lyapunov stability theorem such that the stability of the control system can be guaranteed. It not only has the learning ability similar to intelligent control, but also its control framework is more simple than intelligent control. With the proposed robust control system, the controlled LCM drive possesses the advantages of good tracking control performance and robustness to uncertainties. The effectiveness of the proposed robust control system is verified by experimental results in the presence of uncertainties. In addition, the advantages of the proposed control system are indicated in comparison with the traditional integral-proportional (IP) position control system.     

Controller design and FPGA implementation of a speed‐sensorless vector‐controlled induction motor drive based on stability consideration

Abstract

The purpose of this paper is to determine the controller parameter tuning range for a speed sensorless vector‐ controlled induction motor drive from the system stability point of view. The tuning rules for conventional PI controllers are mostly based on experience. Trial‐and‐error procedures are used to tune the values of the controller parameters. The relationship between the tuned controller parameters and the stable operating range of the control system is generally not known. This paper starts from establishing complete dynamic models for a sensorless vector‐controlled induction motor drive. The nonlinear dynamic models are linearized around a chosen operating point. The characteristic equation is then derived, which is used to determine the values of the controller parameters corresponding to the marginal system stability. Based on these critical values, the tuning ranges of the controller parameters are obtained, which assures stable operation of the drive in the entire operating region and provides a reference for controller parameter tuning. The proposed method is further extended to include the effect of parameter sensitivity due to motor parameter variation. An experimental setup based on a DSP‐FPGA system is implemented. The simulation and experiments confirm the validity of the proposed approach.     

Recurrent fuzzy neural network control for piezoelectric ceramic linear ultrasonic motor drive

In this study, a recurrent fuzzy neural network (RFNN) controller is proposed to control a piezoelectric ceramic linear ultrasonic motor (LUSM) drive system to track periodic reference trajectories with robust control performance. First, the structure and operating principle of the LUSM are described in detail. Second, because the dynamic characteristics of the LUSM are nonlinear and the precise dynamic model is difficult to obtain, a RFNN is proposed to control the position of the moving table of the LUSM to achieve high precision position control with robustness. The back propagation algorithm is used to train the RFNN on-line. Moreover, to guarantee the convergence of tracking error for periodic commands tracking, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Then, the RFNN is implemented in a PC-based computer control system, and the LUSM is driven by a unipolar switching full bridge voltage source inverter using LC resonant technique. Finally, the effectiveness of the RFNN-controlled LUSM drive system is demonstrated by some experimental results. Accurate tracking response and superior dynamic performance can be obtained because of the powerful on-line learning capability of the RFNN controller. Furthermore, the RFNN control system is robust with regard to parameter variations and external disturbances     

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

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.      

超声波电机模糊微步控制在指示表

提出了一种基于行波超声波电动机(USM)和FPGA的新型指示表自动检定仪的设计方法。该系统采用行波超声波电机伺服系统作为检定仪的微位移控制系统,并运用行波超声波电机模糊微步控制的新方法,实现了对指示表测杆的微量匀速进给,具有很高的精度。该指示类仪表智能检定仪还集成了EDA技术、USB通讯技术、传感器及数字图像处理等技术,可以实现各类指示型仪表的全自动检定和误差分析。提出的行波超声波电机模糊微步控制方法可在位移传感器分辨率较低情况下实现高精度、低速、稳定运行。提出的基于USM的检定仪精度高、效率高,易于实现自动测量,具有很好的应用前景。     

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.     

Modified Elman neural network controller with improved particle swarm optimisation for linear synchronous motor drive

A modified Elman neural network controller is proposed 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 modified Elman neural network is proposed to control the PMLSM. Moreover, the connective weights of the modified Elman neural network are trained online by back-propagation (BP) methodology. However, the learning rates of the online-training weights are usually selected by trial-and- error method, which is time-consuming. Therefore an improved particle swarm optimisation (IPSO) is adopted in this study to adapt the learning rates in the BP process of the modified Elman neural network to improve the learning capability. Finally, the control performance of the proposed modified Elman neural network controller with IPSO is verified by the simulated and experimental results.     

Characteristic analysis and design of a B14 rotary ultrasonic motor for a robot arm taking the contact mechanism into consideration

The ultrasonic motor (USM) has many merits for use in a robot arm application. Therefore, the disk-type traveling wave B₁₄ rotary ultrasonic motor (RUSM) is proposed in this paper for that application. Up to the present time, the analysis and design of the USM have been almost always performed using rough analytic methods or using commercial analysis tools. As a result, it was impossible to achieve an exact analysis and design of the USM. In order to address this problem, this paper proposes the analysis and design methodology of the B₁₄ RUSM using a numerical method (3-D FEM) combined with an analytic method taking the contact mechanism into consideration in a linear operation. This methodology is applicable to many other kinds of USMs that use similar mechanisms. In addition, the mechanical system and the driving circuit of the B₁₄ RUSM are designed and prototyped. Finally, the proposed analysis and design methodology is validated by comparing its outcomes with the experimental data. Also, the appropriateness of the suggested RUSM for the application of a robot arm was verified     

Characteristic analysis and design of a B14 rotary ultrasonic motor for a robot arm taking the contact mechanism into consideration.

The ultrasonic motor (USM) has many merits for use in a robot arm application. Therefore, the disk-type traveling wave B14 rotary ultrasonic motor (RUSM) is proposed in this paper for that application. Up to the present time, the analysis and design of the USM have been almost always performed using rough analytic methods or using commercial analysis tools. As a result, it was impossible to achieve an exact analysis and design of the USM. In order to address this problem, this paper proposes the analysis and design methodology of the B14 RUSM using a numerical method (3-D FEM) combined with an analytic method taking the contact mechanism into consideration in a linear operation. This methodology is applicable to many other kinds of USMs that use similar mechanisms. In addition, the mechanical system and the driving circuit of the B14 RUSM are designed and prototyped. Finally, the proposed analysis and design methodology is validated by comparing its outcomes with the experimental data. Also, the appropriateness of the suggested RUSM for the application of a robot arm was verified.     

Development of a novel ultrasonic motor resonator using topology optimization

The use of topology optimization in the design of a novel stator for an ultrasonic motor (USM) is investigated. The design challenge is to produce a stator, with two resonant modes whose frequencies are in a ratio of 1:2. When driven together, these modes result in a contact point trajectory in a figure of eight shape. As a result, only one electronic amplifier is required to drive the proposed device. In contrast traditional travelling wave USM, with elliptical contact point trajectories, require two modes with equal resonant frequencies to be driven 90° out of phase, and therefore require two amplifiers, one for each mode. To achieve a suitable stator design, a slightly unconventional topology optimization problem formulation is proposed, in which the objective function is to minimize the amount of material with intermediate density, while satisfying a constraint related to the frequency ratio of selected resonant modes. The planar design produced using the optimization procedure was refined using a detailed three dimensional finite element analysis. A prototype of the proposed stator design was manufactured and experimentally characterized. Scanning laser vibrometry measurements from two positions were used to measure the figure of-eight motion. Finally, the stator was fitted with a preloaded slider to form a simple linear motor demonstrator which was characterized experimentally. The prototype motor produced a slider speed of 14 mm/s reversibly and a maximum force of 50 mN.     

基于模糊逻辑的异步电动机转速控制

本文设计了基于模糊逻辑控制的速度控制器,以提高异步电动机矢量控制系统对参数变化和负载扰动的鲁棒性,并通过MABLAB/SIMULIINK仿真将其与PI控制的系统速度响应进行比较,仿真结果表明模糊控制能使系统取得较好的控制性能并具有较强的鲁棒性.     

Experimental determination of mechanical parameters in sensorless vector-controlled induction motor drive

High-performance industrial drives widely employ induction motors with position sensorless vector control (SLVC). The state-of-the-art SLVC is first reviewed in this paper. An improved design procedure for current and flux controllers is proposed for SLVC drives when the inverter delay is significant. The speed controller design in such a drive is highly sensitive to the mechanical parameters of the induction motor. These mechanical parameters change with the load coupled. This paper proposes a method to experimentally determine the moment of inertia and mechanical time constant of the induction motor drive along with the load driven. The proposed method is based on acceleration and deceleration of the motor under constant torque, which is achieved using a sensorless vector-controlled drive itself. Experimental results from a 5-hp induction motor drive are presented.     

A novel driver with adjustable frequency and phase for traveling‐wave type ultrasonic motor

Abstract

This paper describes the design of a traveling‐wave ultrasonic motor (TWUSM) drive circuit, intended to simultaneously employ both driving frequency and phase modulation control. The operating principles and a detailed analysis of the proposed driving circuit, consisting of voltage‐controlled oscillator (VCO), voltage‐controlled phase‐shifter circuit and non‐resonant power amplifier converter, are introduced. To drive the USM effectively, a two‐phase power amplifier converter using non‐resonant output was designed to provide a balanced two‐phase voltage source. Two‐phase output driving voltages could be maintained at the same peak voltage value as the driving frequency under varying phase‐modulation processes. Detailed experimental results are provided to demonstrate the effectiveness of the proposed driving circuit.     

Adaptive controller design for a synchronous reluctance motor drive system with direct torque control

The design and implementation of adaptive controllers for a sensorless synchronous reluctance drive system with direct torque control is proposed. Two adaptive control algorithms, which include adaptive backstepping control and model-reference adaptive control, are proposed to improve the performance of a sensorless direct torque control synchronous reluctance motor drive system. A digital signal processor, TMS320-C30, is used to execute the rotor position estimating technique and the adaptive control algorithms. The system shows good transient responses, good load disturbance responses and good tracking responses. Several experimental results validate the theoretical analysis. The advanced controller design for a sensorless synchronous reluctance motor drive with direct torque control is proposed.     

Input filter state feed-forward stabilising controller for constant power load systems

An input filter state feed-forward stabilising controller is presented to stabilise a constant power load and is validated using a brushless DC motor drive system. The strategy is to feed-forward a stabilising signal which is a function of the DC-link filter variables, capacitor voltage and the inductor current, into the current control loop of the motor drive to modify the magnitude and phase of the system input admittance around the input filter natural frequency and thereby damp the input filter. The controller design and parameter selection are described. The impact of the stabilising controller is examined on the motor controller performance and finally the effectiveness of the controller is verified by simulation and experimentally.     

Intelligent Adaptive Backstepping Control System for Magnetic Levitation Apparatus

We propose an intelligent adaptive backstepping control system using a recurrent neural network (RNN) to control the mover position of a magnetic levitation apparatus to compensate for uncertainties, including friction force. First, we derive a dynamic model of the magnetic levitation apparatus. Then, we suggest an adaptive backstepping approach to compensate disturbances, including the friction force, occurring in the motion control system. To further increase the robustness of the magnetic levitation apparatus, we propose an RNN estimator for the required lumped uncertainty in the adaptive backstepping control system. We further propose an online parameter training methodology, derived by the gradient descent method, to increase the learning capability of the RNN. The effectiveness of the proposed control scheme has been verified by experiment. With the proposed adaptive backstepping control system using RNN, the mover position of the magnetic levitation apparatus possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic trajectories     

A Sliding Mode Flux-Linkage Controller With Integral Compensation for Switched Reluctance Motor

A novel flux-linkage controller using sliding mode technique with integral compensation (SM-I) is proposed for torque ripple minimization of a switched reluctance motor (SRM). The proposed SM-I controller inherits the advantages of proportion–integration (PI) and conventional SM controller. These make it feasible for the flux-linkage controller to reduce torque ripple by correctly selecting the flux ramps in the limit of available dc-link voltage. Moreover, since the controller is not model-based, it avoids the complexity of mathematical modeling and is easily implemented. Experimental results demonstrate that the proposed controller performs better and can be used as an alternative for nonlinear SRM drive systems.      

Recurrent Radial Basis Function Network-Based Fuzzy Neural Network Control for Permanent-Magnet Linear Synchronous Motor Servo Drive

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