基于Hamilton理论的可逆冷带轧机速度张力系统无张力计控制

基于反馈耗散Hamilton理论研究了可逆冷带轧机速度张力系统的无张力计控制问题. 首先,对系统速度张力外环(主轧机速度环和左、右卷取机张力控制环)进行预反馈控制, 并采用反馈耗散Hamilton理论完成了速度张力外环控制器的设计. 其次, 为了实现系统的无张力计控制及对摄动参数的自适应估计, 基于"扩张系统+反馈"方法完成了系统速度张力外环自适应状态观测器的设计. 再次, 为了实现可逆冷带轧机主轧机速度和左、右卷取机张力间的协调控制及对外扰不确定项的干扰抑制, 基于backstepping方法完成了系统电流内环鲁棒控制器的设计. 理论分析表明, 所提出的控制方法能够保证闭环系统的鲁棒稳定性. 最后, 基于某1422mm可逆冷带轧机速度张力系统的实际数据进行仿真, 并同串级PI控制方法相比较, 结果验证了本文所提方法的有效性.     

基于SoPC的神经网络速度控制器的实现

一种基于 SoPC 的神经网络速度控制器的设计方案。速度控制器采用神经网络参数辨识自适应控制,以现场可编程门阵列(FPGA)为硬件平台,用 Nios Ⅱ软核处理器作为上位机,实现一个完整的速度控制器的片上可编程系统(SoPC)。实验结果表明,该控制系统能够满足现代速度控制系统高速度、高精度的要求。     

键合铝硅丝连续退火复绕机的控制系统

本文介绍了以8031单片机为核心的键合铝硅丝连续退火复绕机的工作原理、电控系统及其单片机实现,在此基础上着重介绍了张力控制系统和收线子系统,使整机表现出了良好性能,从而保证了复绕成品的质量。     

Decoupling control based on terminal sliding mode and wavelet network for the speed and tension system of reversible cold strip rolling mill

To weaken the nonlinear coupling influences among the variables in the speed and tension system of reversible cold strip rolling mill, a novel dynamic decoupling control strategy is proposed based on nonsingular fast terminal sliding mode (NFTSM) and wavelet neural network (WNN). First, nonlinear disturbance observers are developed to counteract the mismatched uncertainties, and then input/output dynamic decoupling and linearisation for the speed and tension nonlinear coupling system are realised by utilising the inverse system theory. Second, nonsingular fast terminal sliding mode controller (NFTSMC) for each pseudo linear subsystem is presented based on backstepping and two-power reaching law, so as to improve the global convergence speed and robust stability of the system. Third, adaptive WNNs are used to approximate the uncertain items of the system, so as to improve the control precision of the speed and tension of reversible cold strip rolling mill. Theoretical analyses show that the NFTSMs satisfy reachability condition, the system error variables can converge to equilibrium point in finite time, and the resulting closed-loop system is globally asymptotically stable. Finally, simulation research is carried out on the speed and tension system of a 1422 mm reversible cold strip rolling mill by using the actual data, and results show the superiority of the proposed control strategy in comparison with the strategies of cascade PI, linear sliding mode control and internal model control.     

轻工行业中的变频器系统设计与应用 第2讲 变频器在压光机和复卷机中的设计与应用

国际上先进的轻工机械都朝高效率、低消耗方向发展,这是市场竞争的需要。而目前有很多正在运行的轻工设备在这方面则处于劣势,有必要进行技术改造进行升级,并从设计的源头上进行性能提升。本文通过对压光机和复卷机进行详细的变频配置和控制原理介绍。     

A practical framework for tuning DTC-SVM drive of three-phase induction motors

This paper presents a framework for tuning the proportional-integrative (PI) controllers of a direct torque control with space vector modulation (DTC-SVM), applied to a three-phase induction motor. For flux and torque loops, the PIs are designed considering frequency response methods in terms of phase margin and 0dB crossover frequency. In special for the speed loop, the Symmetric Optimum Criterion (SOC) is considered for adjusting the PI gains. Simulation and experimental results are obtained aiming at evidencing the effectiveness of the considered methodology.     

基于模糊PI混合控制的自主水下航行器推进系统

对转永磁无刷直流电机驱动对转螺旋桨主要应用在自主水下航行器推进系统中,通过调节对转电机转速就可以实现自主水下航行器速度的无级调节.在研究对转永磁无刷直流电机数学模型的基础上,提出一种外环转速环采用模糊PI混合控制,内环电流环采用PI控制的双闭环调速方法.仿真结果表明,与外环、内环均采用PI控制的双闭环调速方法相比,该方...     

Improved PI neural network-based tension control for stranded wire helical springs manufacturing

During the winding process of stranded wire helical springs (SWHSs), uneven wire tension always results in high rejection rate and non-compliance service life of SWHSs. Combining the proportion integral neural network (PINN) with a simplified actuator model, this paper presents a new control scheme for the SWHS CNC machine to keep the wire tension uniform. The PINN is improved by introducing an error variance ratio, accounting for the interaction between wires, as a modifying factor in the second hidden layer. The actuator model is simplified based on the analysis of the dynamic characteristics of the actuator. The output value of the improved PINN is transferred into control voltage value by the simplified model. The tension of each wire is controlled by an improved PINN. In order to enhance the control performance, the network parameters are updated using the gradient-based back-propagation method. The validity and consistency of the improved PINN are verified by experiments. The results indicate that (1) the computation load is slight; (2) the rising time of the step response is within 1 s; (3) 89%-96% of tension deviation values of the wire 1 and wire 3 under different process parameters are within 10% of the reference tension value; (4) the standard deviation of the wire 2 with large disturbance is 8.24 N. Compared with other algorithms (incremental PI, multiple PIDNN, PI based particle swarm optimization), the control scheme based on the improved PINN has less computation load, faster response speed and better performance in the time-varying and nonlinear system with larger disturbance.     

权值自调整模糊PI在无刷直流电机中的应用

无刷直流电机是一种多变量和非线性的控制系统,模糊控制器在该控制中得到广泛的使用。针对无刷直流电机构成的速度反馈控制采用了一种新的控制方案——权值自调整模糊PI预测控制。控制器由模糊控制环节、PI控制器、权值调整环节构成,整个系统使用电流和转速双闭环控制。PI控制器和模糊控制器的权值是通过误差、误差变化率设计的模糊调整器来在线调整,速度反馈采用灰色算法来预测。通过仿真实验结果表明上述控制方法较传统的PI控制,具有更强的鲁棒性,易于实现,通用性强。     

两变频调速电机系统的神经网络逆同步控制

针对以恒压频比工作方式的两台变频器+感应电机系统的特点,导出了两变频调速电机系统的统一数学模型,并证明该系统可逆.进一步采用静态神经网络加积分器构成的动态神经网络来构造该逆系统,并将神经网络逆系统与两变频调速电机系统相串联复合成由速度和张力子系统组成的伪线性系统,实现速度和张力的解耦.然后分别对速度和张力子系统设计线性闭环控制器从而实现对两变频调速电机系统的高性能控制.实验结果表明系统具有较好的动、静态性能和较强的抗负载扰动的能力,提出的神经网络逆同步控制方法为解决交流多电机系统解耦控制的难题提供了新思路.     

连续退火机组张紧辊带钢张力的建模及解耦控制

在带钢连续退火机组中,张紧辊区域的带钢张力直接影响着机组的运行速度以及带钢质量,因此,张紧辊区域的带钢张力是连续退火机组中的关键被控变量.本文首先建立了张紧辊区域带钢张力的动态机理模型和状态空间模型,并针对张紧辊带钢张力系统的多变量、强耦合等特性,采用极点配置和动态解耦算法,提出了带钢张力的多变量解耦控制器.仿真实验结果表明,所提出的解耦控制器实现了多个带钢张力控制回路之间的动态解耦,并可获得良好的控制效果.     

Hybrid kinematic and dynamic simulation of running machines

Dynamic simulation requires the computationally expensive calculation of joint accelerations, while in kinematic simulation these accelerations are known based on a given trajectory. This paper describes a hybrid kinematic and dynamic simulation method that can be applied to the simulation of running machines to speed up the computations over that of a dynamic simulation. This is possible because much of the time the legs of a running machine are in the air and their trajectories are directly specified and tightly controlled. The method is more flexible than dynamic simulation alone because it allows joints to be either motion-controlled or force-controlled. It is general to all robotic systems with tree structures, and fully motion-controlled or force-controlled kinematic loops. It should work best for machines with appendages that are motion-controlled, such as those encountered in underwater and space manipulation.     

面向SIMD机器的全局自动数据分割

提出了一种面向ＳＩＭＤ机器的全局数据自动分割算法,该算法能处理多个非紧嵌折循环嵌套,并且数组下标存取为循环变量的线性式,首先通过数据与迭代映射抽象了计算中的通信方式,然事提出识别规则模式通信模式的形式比条件,接着建立包含对准信息和相应通信开销的数据迭代图,并在数据迭代图的基础上提出了一个启发式算法来计算较优的数据分布和迭代分布,以优化处理单元之间的通信开销,通过发析多个循环嵌套所涉及的多个数组映和     

Design of low cost universal artificial neuron controller for chopper fed embedded DC drives

Artificial neural network (ANN) has become very popular in many control applications due to their high computation rate and ability to handle nonlinear functions. This paper proposes an artificial neuron controller for closed loop speed control of DC drive fed by DC chopper. Neuron control is used to reduce the steady state error, overshoot and settling time. The signal corresponding to the motor speed error and change in speed error are used as inputs to ANN Controller. The controller outputs the required change in duty cycle of pulse width modulated gating signal applied to DC chopper. Thus the voltage fed to the armature of the DC motor is adjusted for achieving the desired speed response. The training patterns for the neuron controller are obtained from the conventional PI controller and the effectiveness of the proposed neuron controller is studied using simulation studies.The designed controller was implemented in a low cost 8051-based embedded system and the results are documented. Two-loop control system was implemented with an inner ON/OFF current controller and an outer ANN speed controller.A conventional controller has heavy computation burden whereas a trained neural network requires less computation time. The artificial neural network has the ability to generalize and can interpolate in between the training data. This advantage of ANN makes the ANN controller universal. The ANN controller designed was tested on two different motors and found to work effectively on driving both of them.     

Multiobjective evolution based fuzzy PI controller design for nonlinear systems

This work proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms for nonlinear plants. A fuzzy PI controller is developed, which is a discrete time version of a conventional one. Its data base as well as the constant PI control gains are optimally designed by using a genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. A neural gain scheduler is designed, by the backpropagation algorithm, to tune the optimal parameters of the fuzzy PI controller at some operating points. Simulation results are shown to demonstrate the efficiency of the proposed structure for a DC servomotor adaptive speed control system used as an actuator of robotic manipulators.     

A novel IMC controller based on bacterial foraging optimization algorithm applied to a high speed range PMSM drive

This paper is a proposal of a modified internal model control based on an intelligent technique. The indirect field oriented control strategy (IFOC) is used as a permanent magnet synchronous motor (PMSM) drive platform. Neural network controller and estimator are respectively added to replace the conventional speed regulator and the speed encoder in the global drive scheme. A wide speed working range is considered and high speed mode is incorporated in the study testes. In the IFOC inner control loops, the commonly used synchronous frame conventional proportional plus integral (PI) controllers are replaced by two modified internal model control (IMC) regulators. Therefore, a method based on the bacterial foraging optimization (BFO) algorithm is performed to optimize and adjust the IMC low pass filter parameters. The robustness of the proposed PMSM sensorless drive scheme is confirmed by simulation tests in the MATLAB/SIMULINK. Moreover, a comparative evaluation results are illustrated to prove the effectiveness of the proposed control algorithm according to different controllers combinations.     

Neural networks implementation for modeling and control design of manufacturing systems

This paper presents a dynamic neural network implementation for the modeling and control design of a class of manufacturing systems. The evolution of the considered systems is supposed to be continuous and non-stochastic. A separate implementation of the system elements is detailed. These elements are then connected together in order to obtain a global net that simulates the behavior of the real system. The obtained model is modular and can be adapted easily for any modification of the system. Permanent correction rules are developed to control the speed of the machines according to a desired profile and to take into consideration the buffers limited capacities. The convergence of the control design is proved. The proposed approach is applied on an exhaust valves assembly workshop.     

Comparison of single loop and QDMC-based control of crude oil distillation towers

Significant economic benefits can result from improved control of crude towers because of their large throughput. In this paper, both conventional PI control, with and without decoupling, and model based QDMC control are applied to crude tower product quality variables. The conventional methods treated involve BLT tuning, detuned Ziegler-Nichols control, and detuned Ziegler-Nichols control plus decoupling. The various control methods are tested on a detailed non-linear simulation of a crude tower. It is shown that significant dynamic two-way interaction exists, particularly in the upper crude tower product quality loops, even though interaction is essentially one-way throughout the tower near steady state. Because of the dynamic interaction, PI controllers must be substantially detuned over what would be required for a one-way interacting system. Decoupling is shown to help improve the performance of the PI quality loops. QDMC is shown to give good transient performance, and it is considered easier to apply than traditional PI methods.     

The intelligent integrated speed controller of DTC for induction motor

The parameters selection of proportional coefficient and integral coefficient (PI) for speed controller is important for direct torque control system. However, it is difficult to adjust these parameters. In this paper, firstly, we use particle swarm optimization to search the appropriate PI values of the speed controller. Secondly, based on the optimized PI parameters, the fuzzy-PI speed control strategy is presented to solve the poor self-adaptability problem. Thus, the proportional coefficient k _p and integral coefficient k _i can be adjusted dynamically to adapt to the speed variations. And finally, to obtain the high-speed parallel processing ability, the well-trained RBF neural network replaces the fuzzy-PI speed controller. The comparison with conventional PI speed controller shows that the proposed intelligent integrated speed controller brings good benefits of fast speed response and good stability and reduces the torque ripple. The validity of the proposed intelligent integrated speed controller is verified by the simulation results.     

Identification and predictive control of a multistage evaporator

A recurrent neural network-based nonlinear model predictive control (NMPC) scheme in parallel with PI control loops is developed for a simulation model of an industrial-scale five-stage evaporator. Input–output data from system identification experiments are used in training the network using the Levenberg–Marquardt algorithm with automatic differentiation. The same optimization algorithm is used in predictive control of the plant. The scheme is tested with set-point tracking and disturbance rejection problems on the plant while control performance is compared with that of PI controllers, a simplified mechanistic model-based NMPC developed in previous work and a linear model predictive controller (LMPC). Results show significant improvements in control performance by the new parallel NMPC–PI control scheme.