Design and implementation of industrial neural network controller using backstepping

In this paper, a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closed-loop system. The NN backstepping controller is implemented on an actual motor drive system using a two-PC control system developed at The University of Texas at Arlington. The implementation results show that the NN backstepping controller is highly effective in controlling the industrial motor drive system. It is also shown that the NN controller gives better results on actual systems than a standard backstepping controller developed assuming full knowledge of the dynamics. Moreover, the NN controller does not require the linear-in-the-parameters assumption or the computation of regression matrices required by standard backstepping.     

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     

High-Speed Control of IPMSM Drives Using Improved Fuzzy Logic Algorithms

This paper presents an improved fuzzy logic controller (FLC) for an interior permanent magnet synchronous motor (IPMSM) for high-performance industrial drive applications. In the proposed control scheme for high-speed operations above the rated speed, the operating limits of IPMSM are expanded by incorporating the maximum torque per ampere operation in constant torque region and the flux-weakening operation in constant power region. The power ratings of the motor and the inverter are considered in developing the control algorithm. A new and simple FLC is utilized as a speed controller. The FLC is developed to have less computational burden, which makes it suitable for real-time implementation, particularly at high-speed operating conditions. The complete drive is implemented in real-time using digital signal processor (DSP) controller board DS 1102 on a laboratory 1-hp IPM motor. The efficiency of the proposed control scheme is evaluated through both experimental and computer simulation results. The proposed controller is found to be robust for high-speed applications     

Design and implementation of a high-performance field-orientedinduction motor drive

The design and implementation of a high-performance controller for a field-oriented induction motor drive is presented. Dynamic modeling based on the stochastic technique is performed. Based on the estimated drive model, a two-degree-of-freedom controller is proposed so good dynamic responses in both the speed tracking and regulation characteristics can be achieved. The parameters of the controller are found using a proposed systematic design procedure according to the prescribed specifications. Having designed and tested the performance of the controller by simulation, the hardware implementation is successfully made, and some experimental results are given to demonstrate the effectiveness of the proposed controller     

Speed-sensorless vector control of an induction motor using neuralnetwork speed estimation

In this paper, a novel speed estimation method of an induction motor using neural networks (NNs) is presented. The NN speed estimator is trained online by using the error backpropagation algorithm, and the training starts simultaneously with the induction motor working. The estimated speed is then fed back in the speed control loop, and the speed-sensorless vector drive is realized. The proposed NN speed estimator has shown good performance in the transient and steady states, and also at either variable-speed operation or load variation. The validity and the usefulness of the proposed algorithm are thoroughly verified with experiments on fully digitalized 2.2 kW induction motor drive systems     

基于FPGA的无刷直流电机数字控制方法研究

为了实现简单且高效的无刷直流电机（BLDC）驱动系统,本文提出了一种简单新型的基于FPGA的数字脉冲宽度调制（PWM）控制器的模型和匹配的控制算法,该控制器将梯形磁通分布的BLDC电机看作是一个数字系统,通过低功率和高功率的交替使用进行速度调节,非常便于设计实现。此外,提出的设计只使用直流环节的一个电流传感器,减少了成本和硬件的复杂性。并通过模拟实验对提出的控制方法进行了证实,结果显示提出方法的最大误差保持低于5%。因此,这种控制技术非常适合不需要高精度的应用。     

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     

基于FPGA的视频数据解析及回放技术

本文给出了一种远程视频会议系统节点设备的视频数据捕获及回放的FPGA实现。本地捕获数据在进行压缩之前,需要对每一帧中色度信号U、V、亮度信号Y以及同步信号进行分离。通过DSP EM IF接口与FPGA之间的通信,实现了捕获和回放的单片FPGA设计,给出了基于FPGA的视频数据解析方法,同时也给出了DSP EM IF控制器和SDRAM控制器的FPGA实现。工程实践表明,本文的设计减少了DSP中算法软件的复杂度,提高了节点设备处理的速度,且稳定可靠。     

FPGA-Based Adaptive Backstepping Sliding-Mode Control for Linear Induction Motor Drive

A field-programmable gate array (FPGA)-based adaptive backstepping sliding-mode controller is proposed to control the mover position of a linear induction motor (LIM) drive to compensate for the uncertainties including the friction force. First, the dynamic model of an indirect field-oriented LIM drive is derived. Next, a backstepping sliding-mode approach is designed to compensate the uncertainties occurring in the motion control system. Moreover, the uncertainties are lumped and the upper bound of the lumped uncertainty is necessary in the design of the backstepping sliding-mode controller. However, the upper bound of the lumped uncertainty is difficult to obtain in advance of practical applications. Therefore, an adaptive law is derived to adapt the value of the lumped uncertainty in real time, and an adaptive backstepping sliding-mode control law is the result. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results. With the adaptive backstepping sliding-mode controller, the mover position of the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.     

Energy-Efficient 8-Point DCT Approximations: Theory and Hardware Architectures

Due to its remarkable energy compaction properties, the discrete cosine transform (DCT) is employed in a multitude of compression standards, such as JPEG and H.265/HEVC. Several low-complexity integer approximations for the DCT have been proposed for both 1D and 2D signal analyses. The increasing demand for low-complexity, energy-efficient methods requires algorithms with even lower computational costs. In this paper, new 8-point DCT approximations with very low arithmetic complexity are presented. The new transforms are proposed based on pruning state-of-the-art DCT approximations. The proposed algorithms were assessed in terms of arithmetic complexity, energy retention capability, and image compression performance. In addition, a metric combining performance and computational complexity measures was proposed. Results showed good performance and extremely low computational complexity. Introduced algorithms were mapped into systolic-array digital architectures and physically realized as digital prototype circuits using FPGA technology and mapped to 45 nm CMOS technology. All hardware-related metrics showed low resource consumption of the proposed pruned approximate transforms. The best proposed transform according to the introduced metric presents a reduction in power consumption of 21–25 %.     

A discrete adaptive field-oriented induction motor drive

A discrete model reference adaptive controller (MRAC) is designed and implemented. This MRAC makes the performance of the field-oriented induction motor drives insensitive to parameter changes. Only the information of the reference model and the plant output are required. Hence, the proposed controller is easy to implement practically. For designing the proposed adaptive controller, the dynamic model of the drive system is estimated from the sampled input-output data using the stochastic modeling technique. The theoretical basis of the adaptive control is derived and simulation is made. The hardware of the drive system and the microprocessor-based adaptive controller are discussed. Some experimental results are given to demonstrate the effectiveness of the proposed controller     

A simple direct-torque neuro-fuzzy control of PWM-inverter-fedinduction motor drive

In this paper, the concept and implementation of a new simple direct-torque neuro-fuzzy control (DTNFC) scheme for pulsewidth-modulation-inverter-fed induction motor drive are presented. An adaptive neuro-fuzzy inference system is applied to achieve high-performance decoupled flux and torque control. The theoretical principle and tuning procedure of this method are discussed. A 3 kW induction motor experimental system with digital signal processor TMS 320C31-based controller has been built to verify this approach. The simulation and laboratory experimental results, which illustrate the performance of the proposed scheme, are presented. Also, nomograms for controller design are given. It has been shown that the simple DTNFC is characterized by very fast torque and flux response, very-low-speed operation, and simple tuning capability     

Microcomputer implementation of optimal algorithms for closed-loop control of hybrid stepper motor drives

This paper discusses optimal algorithms for closed-loop control of hybrid stepper motor drives and their microprocessor implementation. The torque characteristics and the optimal control angle of hybrid stepper motor drives with added series resistance and reluctant stepper motor drives have been described in detail in the literature. The specific contribution of the paper to this field of research consists of the analysis of the torque characteristics and the optimal control angle of hybrid stepper motor drives with a chopper amplifier and current controller. Analytical expressions for the average torque and the optimal control angle of a two-phase hybrid stepper motor with chopper amplifier and current controller have been developed. An actual hybrid stepper motor drive and the microcontroller-based implementation of the suboptimal and exact optimal control algorithms have been described. The experimental results obtained by positioning a two-phase hybrid stepper motor drive with chopper amplifier, phase current controller, and incremental encoder suggested that the derived optimal control algorithm provided maximum acceleration and minimum positioning time of the hybrid stepper motor drive.     

Evaluation of motor characteristics for hybrid electric vehicles using the hardware-in-the-loop concept

If the concept of Hardware-in-the-Loop (HIL) is applied to component testing, characteristic of component of hybrid electric vehicle in real vehicle environment can be evaluated without actually installing that component in real vehicle. In this paper, when commercially available test motor is adopted as a drivetrain of hybrid vehicle, we need to figure out which drive train configuration would be best for specific purpose. The characteristic of the motor when it is installed in the vehicle at different drive train and driving mode can be simulated and actual characteristic can be measured. Also both results can be compared. For the hardware characteristic measurement, test facility which consists of vehicle simulator and dynamometer is required. In this case, vehicle controller in the vehicle simulator is used as a vehicle controller and dynamometer is used to simulate vehicle dynamics. Two drive train types, 4-motor series, and 2-motor parallel type are proposed. Vehicle speed tracks driving cycle speed command well in both simulation and HIL implementation.     

Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms

A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance     

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.     

任意长离散余弦变换的快速递归算法

该文基于Clenshaw递归公式以及离散余弦自身的对称性提出任意长离散余弦变换(DCT)的一种并行递归快速算法,给出了该算法的滤波器实现结构;与现有的其它递归算法以及基于算术傅里叶变换的余弦变换算法进行了计算复杂度的比较分析,结果表明该文算法运算量大大减少。该递归计算的滤波器结构使算法非常适合大规模集成电路(VLSI)实现。     

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.     

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     

A neuro-fuzzy-based on-line efficiency optimization control of astator flux-oriented direct vector-controlled induction motor drive

Fuzzy logic-based online efficiency optimization control has been described previously for an indirect vector-controlled induction motor drive. The purpose of this paper is to extend the same control to a stator flux-oriented electric vehicle induction motor drive and then implement the fuzzy controller by a dynamic back propagation neural network-based controller. The principal advantage of fuzzy control, i.e., fast convergence with adaptive step size of the control variable, is retained. The neural network adds the advantage of fast control implementation, either by a dedicated hardware chip or by digital signal processor (DSP)-based software