Recurrent neural network control for LCC-resonant ultrasonic motor drive

A newly designed driving circuit for the traveling wave-type ultrasonic motor (USM), which consists of a push-pull DC-DC power converter and a two-phase voltage source inverter using one inductance and two capacitances (LCC) resonant technique, is presented in this study. Moreover, because the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, a recurrent neural network (RNN) controller is proposed to control the USM drive system. In the proposed controller, the dynamic backpropagation algorithm is adopted to train the RNN on-line using the proposed delta adaptation law. Furthermore, to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates for the training of the RNN. Finally, the effectiveness of the RNN-controlled USM drive system is demonstrated by some experimental results.     

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.     

Recurrent RBFN-based fuzzy neural network control for X-Y-theta motion control stage using linear ultrasonic motors

A recurrent radial basis function network (RBFN) based fuzzy neural network (FNN) 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. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, the structure and the parameter learning phases of the recurrent RBFN-based FNN are performed concurrently and on line. 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. The experimental results due to various contours show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties.     

An intelligent control for linear ultrasonic motor using interval type-2 fuzzy neural network

An interval type-2 fuzzy neural network (IT2FNN) is developed for the position control of a thetas-axis motion-control stage using a linear ultrasonic motor to confront the uncertainties of the motion-control stage. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part and a three-layer interval neural network as the consequent part. A general T2FNN is computationally intensive due to the complexity of reducing type 2 to type 1. Therefore an IT2FNN is adopted to simplify the computational process. Moreover, the developed IT2FNN combines the merits of an interval type-2 fuzzy logic system and a neural network. Furthermore, the parameter-learning of the IT2FNN, which is based on the supervised gradient decent method using a delta adaptation law, is performed on line. Experimental results show that the dynamic behaviours of the proposed IT2FNN control system are more effective and robust with regard to uncertainties than the type-1 FNN control system.     

FPGA-based elman neural network control system for linear ultrasonic motor

A field-programmable gate array (FPGA)-based Elman neural network (ENN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principle of the LUSM are introduced. Because the dynamic characteristics and motor parameters of the LUSM are nonlinear and time-varying, an ENN control system is designed to achieve precision position control. The network structure and online learning algorithm using delta adaptation law of the ENN are described in detail. Then, a piecewise continuous function is adopted to replace the sigmoid function in the hidden layer of the ENN to facilitate hardware implementation. In addition, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results.     

Field-programmable gate array-based recurrent wavelet neural network control system for linear ultrasonic motor

A field-programmable gate array (FPGA)-based recurrent wavelet neural network (RWNN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM). First, the structure and operating principles of the LUSM are introduced. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an RWNN controller is designed to improve the control performance for the precision tracking of various reference trajectories. The network structure and its on-line learning algorithm using delta adaptation law of the RWNN are described in detail. Moreover, the connective weights, translations and dilations of the RWNN are trained on-line. Furthermore, to guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RWNN. In addition, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. Finally, the effectiveness of the proposed control system is verified by some experimental results.     

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.     

Analysis of shaking beam actuator for piezoelectric linear ultrasonic motor

In this paper, piezoelectric linear ultrasonic motors (PLUM) have been investigated on the elliptic trajectory of a contact point in shaking beam, which has been accomplished by two resonance vibration modes of the actuators. The actuators have generated the vibration modes, longitudinal and flexural, by two longitudinal mechanical vibrations with phase difference of pi/2. Modal and harmonic analysis of the shaking beam actuator were performed by the finite element method (FEM) to calculate a resonance frequency and a modal shape and to perform harmonic response. Experimental results proved that a contact point of the PLUM tends to move with an elliptic trajectory.     

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.     

陶瓷浆料3D打印机挤压力模糊神经网络PID稳定控制研究

针对在微流挤出陶瓷浆料3D打印机作业过程中挤压力稳定控制的需求,根据打印机挤压力控制系统非线性、时变性的特点,总结了现有挤压力稳定控制策略的优缺点,并在模糊PID (proportion-integral-derivative,比例-积分-微分)控制器中嵌入神经网络结构,提出了挤压力模糊神经网络PID稳定控制策略。该策略基于六层模糊神经网络,以挤压力偏差值e和偏差值变化率ec为输入,PID控制器控制参数为输出,完成正向模糊控制过程,并基于神经网络的自学习优势实现反向传播及在线更新神经网络权值,以实现打印过程中挤压力的精准自适应调节。挤压力控制Simulink仿真、挤压力控制实验及坯体打印实验表明：相较于传统PID控制策略,采用模糊神经网络PID控制策略可使超调量减小20.9%,挤压力提前90 s达到稳定状态,压力峰值减小12 N,压力谷值增大18 N;相较于采用模糊PID控制策略,超调量减小1.73%,挤压力提前56 s达到稳定状态,压力峰值减小4 N,压力谷值增大8 N;模糊神经网络PID控制策略具有一定的优越性,可使打印过程中挤压力的控制精度更高,稳定速度更快,超调量更小,所打印坯...     

Speed control of SR motor by self-tuning fuzzy PI controller with artificial neural network

In this work, the dynamic model, flux-current-rotor position and torque-current-rotor position values of the switched reluctance motor (SRM) are obtained in MATLAB/Simulink. Motor control speed is achieved by self-tuning fuzzy PI (Proportional Integral) controller with artificial neural network tuning (NSTFPI). Performance of NSTFPI controller is compared with performance of fuzzy logic (FL) and fuzzy logic PI (FLPI) controllers in respect of rise time, settling time, overshoot and steady state error.     

FPGA-based adaptive backstepping control system using RBFN for linear induction motor drive

A field-programmable gate array (FPGA)-based adaptive backstepping control system with radial basis function network (RBFN) observer is proposed to control the mover position of a linear induction motor (LIM). First, the indirect field-oriented mechanism is adopted for controlling the LIM. Next, a backstepping control law is designed step by step for the tracking control of periodic reference trajectories, in which the uncertainties are lumped by a conservative constant. However, the lumped uncertainty is unknown and difficult to obtain in advance in practical applications. Therefore an RBFN is derived to observe the lumped uncertainty in realtime, and an adaptive backstepping control system with RBFN observer is resulted. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost, high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some simulated and experimental results. By using the adaptive backstepping control system with RBFN observer, the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.     

New type of piezoelectric ultrasonic motor

The authors describe a newly developed motor concept which allows a bidirectional piezoelectric ultrasonic motor to be operated with only a single voltage feed and thus only one power amplifier. The motor concept is based on the superposition of a longitudinal and a flexural oscillation of a rod-shaped resonator. In a way analogous to the generation of a Lissajous figure, this superposition produces a rotary movement of the resonator end by means of which a rotor is directly driven. By selecting the relative phase of the electrical stimulations of both modes, the speed can be continuously varied in both directions. The motor can be driven in both right and left directions with speeds of 0 to 300 r/min, and a freewheeling state can be set up by means of a suitable phase between the oscillation modes. In the off state, the motor blocks the motion.     

Design and performance testing of an ultrasonic linear motor with dual piezoelectric actuators

In this work, design and performance testing of an ultrasonic linear motor with dual piezoelectric actuator patches are studied. The motor system consists of a linear stator, a pre-load weight, and two piezoelectric actuator patches. The piezoelectric actuators are bonded with the linear elastic stator at specific locations. The stator generates propagating waves when the piezoelectric actuators are subjected to harmonic excitations. Vibration characteristics of the linear stator are analyzed and compared with finite element and experimental results. The analytical, finite element, and experimental results show agreement. In the experiments, performance of the ultrasonic linear motor is tested. Relationships between velocity and pre-load weight, velocity and applied voltage, driving force and applied voltage, and velocity and driving force are reported. The design of the dual piezoelectric actuators yields a simpler structure with a smaller number of actuators and lower stator stiffness compared with a conventional design of an ultrasonic linear motor with fully laminated piezoelectric actuators.     

夹心式功率超声压电陶瓷换能器的工程设计

文中为换能器的优化设计和性能改善提供一些有用的设计指南和解决措施,并对功率超声夹心式压电陶瓷换能器工程设计中的一些重要问题(即换能器各部分的功能及选择,压电陶瓷元件的位置优化,接触界面对换能器性能的影响与预应力的选择及影响等)进行了简要的分析。结论表明,文中所述对于优化设计、改善并提高性能具有参考和使用价值。     

微量注射泵用直线超声电机的结构设计

改进了一种螺纹杆直线超声电机,并用于驱动微量注射泵。螺纹杆直线超声电机具有体积小、精度高、推力足、无磁干扰等特点。为了满足微量注射泵推力要求,针对螺纹杆直线超声电机不能施加预压力问题,提出了一种可施加预压力的螺纹杆直线超声电机。该直线超声电机包括底座、柱状定子、分体式螺纹杆动子。动子由动子主轴、套筒、预压力施加装置组成。当预压力调至33.9 N时,电机达到最大堵推力21 N,此时空载最大速度为4.5 mm/s。并设计了一款微量注射泵,该微量注射泵结构简单、抗磁干扰,分辨率达到nl级别,满足各种微量药物注射要求。     

Modeling of a piezoelectric rotary ultrasonic motor

A piezoelectric rotary ultrasonic motor is modeled for the purpose of predicting, a priori, motor performance as a function of design parameters. The Rayleigh-Ritz assumed mode energy method is used to model the distributed piezoceramics and the traveling wave dynamics of the stator. Natural frequencies and modeshapes are obtained for a generally configured motor. Nonlinear normal and tangential interface forces between the rotor and stator are incorporated into the forcing function along with the linear piezoelectric forcing. Given the applied torque, applied axial loading, and piezo drive voltages as inputs to the model, general motor performance measures are obtained-namely speed, input power, output power, and efficiency. The approach presented here provides a general framework for modeling these motors as well as a design tool for optimizing prototypes with the added flexibility of allowing for a wide variety of geometries and materials     

挖掘机器人轨迹的模糊CMAC神经网络控制

在对挖掘机器人工作装置动力学分析的基础上,为克服多变量、强耦合及负载扰动对轨迹控制系统的影响,提出了模糊CMAC神经网络与常规控制相结合的控制方法,使神经网络逼近挖掘机器人工作装置逆动力学模型,很好地解决了轨迹控制的问题.利用MATLAB进行仿真研究,结果表明该控制方法具有较高的控制精度和鲁棒性,满足挖掘机器人轨迹控制的要求.     

A single vibration mode tubular piezoelectric ultrasonic motor

A novel tubular ultrasonic motor is presented that uses only a single vibration bending mode of a piezoelectric tube to generate rotation. When the piezoelectric tube bends, the diagonal motion of points on selected areas at the ends of the tube generates forces with tangential components along the same circumferential direction, driving the rotors to rotate. Bi-directional motion is achieved by simply switching the direction of bending. Because only one vibration mode is used, the motor requires only one driving signal and no vibration mode coupling is needed, simplifying the design, fabrication, assembly, and operation of the device. Two prototypes [one with cut-in lead zirconate titanate (PZT) teeth and one with added metal teeth] were built and tested using PZT tubes available to the authors. The tubes have an outside diameter of 6.6 mm, inner diameter of 5.0 mm, and length of 25.4 mm. The working frequencies of the two motors are 27.6 and 23.5 kHz. The motors achieved a maximum no-load speed of 400 rpm and a stall torque of 300 μN·m.