Design of Total Sliding-Mode-Based Genetic Algorithm Control for Hybrid Resonant-Driven Linear Piezoelectric Ceramic Motor

This study presents a total sliding-mode-based genetic algorithm control (TSGAC) system for a linear piezoelectric ceramic motor (LPCM) driven by a newly designed hybrid resonant inverter. First, the motor configuration and driving circuit of an LPCM are introduced, and its hypothetical dynamic model is represented by a nonlinear function with unknown system parameters. In the hybrid resonant drive system, it has the merits of the high voltage gain from a parallel-resonant current source, and the invariant output characteristic from a two-inductance two-capacitance resonant driving circuit. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, a TSGAC system is therefore investigated based on direction-based genetic algorithm with the spirit of total sliding-mode control (TSC) and fuzzy-based evolutionary procedure to achieve high-precision position control under a wide operation range. In this control scheme, a GAC system is utilized to be the major controller, and the stability can be indirectly ensured by the concept of TSC without strict constraints and detailed system knowledge. In addition, the effectiveness of the proposed drive and control system is verified by numerical simulations and experimental results in the presence of uncertainties     

Implementation of LLCC-resonant driving circuit and adaptive CMAC neural network control for linear piezoelectric ceramic motor

In this paper, an adaptive cerebellar-model articulation computer (CMAC) neural network (NN) control system is developed for a linear piezoelectric ceramic motor (LPCM) that is driven by an LLCC-resonant inverter. The motor structure and LLCC-resonant driving circuit of an LPCM are introduced initially. The LLCC-resonant driving circuit is designed to operate at an optimal switching frequency such that the output voltage will not be influenced by the variation of quality factor. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive CMAC NN control system is designed without mathematical dynamic model to control the position of the moving table of the LPCM drive system to achieve high-precision position control with robustness. In the proposed control scheme, the dynamic backpropagation algorithm is adopted to train the CMAC NN online. Moreover, to guarantee the convergence of output tracking error for periodic commands tracking, analytical methods based on a discrete-type Lyapunov function are utilized to determine the optimal learning-rate parameters of the CMAC NN. The effectiveness of the proposed driving circuit and control system is verified by experimental results in the presence of uncertainties, and the advantages of the proposed control system are indicated in comparison with a traditional integral-proportional position control system. Accurate tracking response and superior dynamic performance can be obtained due to the powerful online learning capability of the CMAC NN with optimal learning-rate parameters.     

在几何结构设计中采用遗传算法优化横向磁场永磁电机的转矩波形

在制造横向磁场永磁电机（TFPM）电机之前，应确定最大转矩的波形。通过调整TFPM电机的各项几何参数，可以使电机达到其最大转矩。一种基于人工智能的最优化方法可以使得每个极间隔都获得理想的转矩波形。本文给出了一种用于优化TFPM几何参数的遗传算法，该算法同样适用于其它结构的电机。     

用于简易变频器设计的新型扁平输入整流器

在制造横向磁场永磁电机（TFPM）电机之前，应确定最大转矩的波形。通过调整TEPM电机的各项几何参数，可以使电机达到其最大转矩。一种基于人工智能的最优化方法可以使得每个极间隔都获得理想的转矩波形。本文给出了一种用于优化TEPM几何参数的遗传算法，该算法同样适用于其它结构的电机。     

Voltage control strategy for maximum torque operation of aninduction machine in the field-weakening region

In this paper, a novel field-weakening scheme for the induction machine is presented. The proposed algorithm, based on the voltage control strategy, ensures the maximum torque operation over the entire field-weakening region without using the machine parameters. Also, by introducing the direct field-oriented (DFO) control, which is insensitive to the variation of machine parameters in the field-weakening region, the drive system can obtain robustness to parameter variations. Moreover, the speed sensorless control can be achieved in the very-high-speed range, where the utilization of the speed sensor is limited. Experimental results for the laboratory induction motor drive system confirm the validity of the proposed control algorithm     

Optimal controller design for a matrix converter based surface mounted PMSM drive system

This paper proposes a new control algorithm for a matrix converter permanent magnet synchronous motor (PMSM) drive system. First, a new switching strategy, which applies a backpropagation neural network to adjust a pseudo DC bus voltage, is proposed to reduce the current harmonics of the permanent magnet synchronous motor. Next, a two-degree-of-freedom controller is proposed to improve the system performance. The parameters of this controller are obtained by using a frequency-domain optimization technique. The controller design algorithm can be applied in an adjustable speed control system and a position control system to obtain good transient responses and good load disturbance rejection abilities. The controller design procedures require only algebraic computation. The implementation of this kind of controller is only possible by using a high-speed digital signal processor. In this paper, all the control loops, including current-loop, speed-loop, and position-loop, are implemented by a 32-b TMS320C40 digital signal processor. The hardware, therefore, is very simple. Several experimental results are shown to validate the theoretical analysis.     

Robust control of induction motor with a neural-network load torqueestimator and a neural-network identification

This paper presents a control scheme for an induction motor drive which consists of a compensator, neural network identification (NNI), and neural network load torque estimator (NNLTE) based on the conventional proportional-integral controller. The NNI is a two-layer neural network which uses a projection algorithm to estimate the parameters of the induction motor and to regulate the gain of the compensator such that the response of the induction motor follows that of the nominal plant. The NNLTE is a two-layer neural network which uses the steepest descent algorithm to estimate the load disturbance and forward feed, resulting in equivalent control such that the speed response of the induction motor is robust against the load disturbance. Computer simulations and experimental results demonstrate that the proposed control scheme can obtain a robust speed control     

基于电机温度时滞耦合系统的自抗扰控制研究

电机过热时会造成绕组绝缘降低,严重时会烧坏电机。由于电机温度存在较大的时滞性和耦合性,无法用准确的数学控制模型进行描述。传统的双通道PID控制算法降低了对电机温度的控制精度,导致电机过热。提出一种基于自抗扰控制算法（ADRC）的电机温度控制方法,以双通道PID控制方法为基础进行了改进,对内外扰动进行综合处理,对扩张状态观测器进行估计,并在反馈中引入非线性特性。利用Matlab平台模拟实验环境和实际电机,与传统的PID控制算法进行了仿真实验对比。结果表明,自抗扰控制算法能够有效的对电机温度进行解耦控制,实现了对电机温度的精确控制。     

Evaluating generalized predictive control for a brushless DC drive

This paper proposes a new control approach for a brushless DC motor drive using the generalized predictive control (GPC) algorithm. Based on the same least-squares framework as in the controller design, we further develop the method to design an observer. The GPC algorithm uses the receding horizon approach whereby the control signals are determined by minimizing a quadratic cost function. Our study shows that the rise time and settling time of the servo system have an approximate linear relationship with the prediction horizon. Thus, it is used to tune the controller of the drive. Moreover, the control weighting factor can be used to smooth the controller output. The proposed algorithm has been implemented using a digital signal processor (DSP) and tested in real time with a prototype system. The performance and robustness of the algorithms have been evaluated both in simulation and experiment. The results show that the drive performs reasonably well despite load changes and step changes in the position setpoint. Furthermore, it is fairly robust against motor parameters change     

Sensorless field-oriented control for double-inverter-fed wound-rotor induction motor drive

A novel control technique for sensorless vector control operation of a double-inverter-fed wound-rotor induction motor is presented. Two current controllers control the stator-side currents based on a vector control algorithm. Another V/f-type flux and frequency controller controls the rotor-side frequency directly. A novel frequency command profile for the rotor-side controller is suggested to make this sensorless drive operation reliable and reduce dependence on motor parameters at any rotor speed. A complete inverter power flow analysis is presented to show that the drive can deliver full torque from 0- to 2-p.u. speed for either direction of rotation. Thus, double the rated power can be extracted from the induction motor without overloading it. The proposed algorithm allows the drive to start on-the-fly without any rotor transducer. Results from a prototype 50-hp drive are presented.     

Sensorless full-digital PMSM drive with EKF estimation of speed androtor position

This paper concerns the realization of a sensorless permanent magnet (PM) synchronous motor drive. Position and angular speed of the rotor are obtained through an extended Kalman filter. The estimation algorithm does not require either the knowledge of the mechanical parameters or the initial rotor position, overcoming two of the main drawbacks of other estimation techniques. The drive also incorporates a digital d-q current control, which can be easily tuned with locked rotor. The experimental setup includes a PM synchronous motor, a pulsewidth modulation voltage-source inverter, and floating-point digital-signal-processor-based control system     

基于动态规划的对焦步进电机控制

为了解决STM(stepper motor)镜头中步进电机低频对焦慢、高频失步跑焦的问题,通过分析对焦过程中电机的加减速动作,结合步进电机加减速曲线,设计了一种适用于STM镜头驱动的步进电机开环控制算法.算法根据步进电机速度在单位控制周期内不变,把步进电机位置控制问题转换为单位控制周期内脉冲输出问题,实现了速度控制与位...     

光纤陀螺稳定平台控制系统的设计与实现

针对重力测量中海空重力仪需要时刻保持稳定的垂直指向的问题,设计了一种双环比例、积分、微分(PID)控制方法来控制平台水平,隔离外界扰动;针对力矩电机设计中存在死区的特性,提出了一种死区补偿函数,减小电机死区对控制精度的影响;针对双环PID参数难以整定的问题,设计了一种优化的差分进化（DE）算法,减小参数整定时的工作量。仿真和实测实验结果表明,基于双环PID控制和死区补偿的系统稳定性高,控制精度优于30″;改进DE算法的参数整定性能优于遗传(GA)算法和粒子群(PSO)算法,且计算量小。     

Precise angular speed control of permanent magnet DC motors in presence of high modeling uncertainties via sliding mode observer-based model reference adaptive algorithm

The major concentration of this study is on developing a control scheme with parameter- and load-insensitive features capable of precise angular speed regulation of a permanent magnet (PM) DC motor in the presence of modeling uncertainties. Towards this objective, first, an appropriate nonlinear dynamic model of friction, the modified LuGre model, is opted and incorporated into the mathematical model of a PM DC motor. Then a sliding mode observer (SMO) is designed to estimate the state variable of the friction model. Next, a model reference adaptive control system into which estimated values of the friction state and parameters are fed is designed to track the desired speed trajectory while alleviating the adverse effects of model uncertainties and friction. Stability of the proposed SMO-based MRAC system is discussed via the Lyapunov stability theorem, and its asymptotic stability is verified. In addition to simulation studies, the algorithm is implemented on a new variable structure test-bed which gives us the ability to simulate desired parameter variations and external disturbance changes in experiment. The main contribution of the proposed scheme is the bounded estimation of the system’s friction parameter. While similar control solutions do estimate these parameters, there is no guarantee that they will estimate the correct value of friction parameters. However, in the proposed method, by properly choosing the design parameters, if certain criteria is satisfied, the estimated friction parameters will be in the bounded vicinity of their actual values. The obtained results show the effectiveness of the proposed tracking algorithm and its robustness against load and system parameters’ variations.     

Hybrid control algorithm to improve both stable impedance range and transparency in haptic devices

An ideal haptic device should transmit a wide range of stable impedances with maximum transparency. When using active actuators, transparency improvement algorithms tend to decrease the range of attainable impedances. Passive actuators can transmit high impedances stably, but are not sufficient alone for transparency. In this study, a hybrid force control algorithm employing active and passive actuators was developed to improve the stable impedance range and transparency in haptic devices. A new transparency-Z-width plot is proposed as a way to evaluate the stable impedance range and transparency together. The hybrid control algorithm uses parameters to share the torque demand between two actuators with smooth transition. These parameters were determined and an artificial neural network (ANN) was used to extend them to the entire achievable impedance range. The algorithm was tested experimentally on a 1-DOF haptic device. The transparency experiments employed an excitation motor located at the user side of the device to evaluate various algorithms in time and frequency domains. Results showed that the proposed hybrid control algorithm enables simulation of higher range of impedances with higher transparency than the conventional algorithms.     

Vehicle Stability Enhancement of Four-Wheel-Drive Hybrid Electric Vehicle Using Rear Motor Control

A vehicle stability enhancement control algorithm for a four-wheel-drive hybrid electric vehicle (HEV) is proposed using rear motor driving, regenerative braking control, and electrohydraulic brake (EHB) control. A fuzzy-rule-based control algorithm is proposed, which generates the direct yaw moment to compensate for the errors of the sideslip angle and yaw rate. Performance of the vehicle stability control algorithm is evaluated using ADAMS and MATLAB Simulink cosimulations. HEV chassis elements such as the tires, suspension system, and steering system are modeled to describe the vehicle's dynamic behavior in more detail using ADAMS, whereas HEV power train elements such as the engine, motor, battery, and transmission are modeled using MATLAB Simulink with the control algorithm. It is found from the simulation results that the driving and regenerative braking at the rear motor is able to provide improved stability. In addition, better performance can be achieved by applying the driving and regenerative braking control, as well as EHB control.     

电机故障诊断中噪声提取系统的设计

设计了一种远场多电机噪声定向提取系统。该系统通过数字信号处理器DSP28335与六阵元麦克风阵列远场采集噪声信号,并结合多重信号分类(MUSIC)算法及简化的聚焦算法确定各电机的位置参数,最后通过阵列导向矢量的广义逆矩阵及位置参数,完成对各电机噪声信号的分离与提取。仿真实验表明,该系统简易可行,电机的位置误差在5°以内,提取的电机信号与原信号的吻合度较高。     

Control of induction motors for both high dynamic performance andhigh power efficiency

The authors attempt to control induction motors with maximum power efficiency as well as high dynamic performance by means of decoupling of motor speed (or motor torque) and rotor flux. For maximum power efficiency, the squared rotor flux is adjusted according to a minimum power search algorithm until the measured power input reaches the minimum. Since the motor speed is dynamically decoupled from the rotor flux, this can be done successfully without any degradation of motor speed responses. The controller depends on rotor resistance but not on stator resistance. However, the performance of the control scheme is robust with respect to variations in rotor resistance because an identification algorithm for rotor resistance is employed. The identification algorithm for rotor resistance has some advantages over the previous methods. To demonstrate the practical significance of the results, some experimental results are presented     

Speed control of ultrasonic motors using neural network

The ultrasonic motor (USM) is a newly developed motor, and it has excellent performance and many useful features, therefore, it has been expected to be of practical use. However, the driving principle of USM is different from that of other electromagnetic-type motors, and the mathematical model is complex to apply to motor control. Furthermore, the speed characteristics of the motor have heavy nonlinearity and vary with driving conditions. Hence, the precise speed control of USM is generally difficult. This paper proposes a new speed-control scheme for USM using a neural network. The proposed controller can approximate the nonlinear input-output mappings of the motor using a neural network and can compensate the characteristic variations by on-line learning using the error backpropagation algorithm. Then, the trained network finally makes an inverse model of the motor. The usefulness and validity of the proposed control scheme are examined in experiments     

A passivity-based method for induction motor control

The control of an induction motor is a difficult problem, since the dynamics of the induction motor are nonlinear, the rotor electrical state variables (i.e., rotor fluxes or currents) are usually unavailable for measurement, and the motor parameters can vary significantly from their nominal values. The main purpose of this paper is to develop a control algorithm that forces the induction motor to track time-varying speed, position, and flux trajectories without knowledge of the rotor electrical state variables. To achieve this, a passivity-based method is developed. The key point with this method is the identification of terms, known as workless forces, which appear in the dynamic equations of the induction motor but do not have any effect on the energy balance equation of the induction motor. These terms do not influence the stability properties of the induction motor and, hence, there is no need to cancel them with feedback control. This leads to a simpler control structure and enhances the robustness of the control system. Experimental results show that the passivity-based method provides close tracking of time-varying speed, position, and flux trajectories without knowledge of the rotor electrical state variables