Application of Smith Predictor Based on Single Neural Network in Cold Rolling Shape Control

Flatness is one of the most important criterion factors to evaluate the quality of the steel strip. To improve the strip＇ s flatness quality, the most frequently used methodology is to employ the closed-loop automatic shape control system. However, in the shape control system, the shape-meter is always installed at the down way of the exit of the cold rolling mill and can not sense the changes of the strip flatness in the rolling gap directly. This kind of installation results in the delay of the feedback in the control system. Therefore, the stability and response performance of the system are strongly affected by the delay. At present, there is still no mature way to design controllers for systems with time delay. Although the conventional PID controller used in most practical applications has the capability to compensate the delay, the effect of the compensation is limited, especially for the systems with long time delay. Smith predictor, as a compensator for solving this problem, is now widely used in industry systems. However, the request of highly precise model of the system and the poor adaptive performance to the changes of related parameters limit the application of the Smith predictor in practice. In order to overcome the drawbacks of the Smith predictor, a new Smith predictor based on single neural network PID （SNN-PID） is proposed. Because the single neural network is employed into the Smith predictor to improve the controller＇s self-adaptability, the adaptive capability to the varying parameters of the system is improved. Meanwhile, for the purpose of solving the problems such as time-consuming and complicated calculation of the neural networks in real time, the learning coefficient of neural network is divided into several stages as usually done in expert control system. Therefore, the control system can obtain fast response due to the improved calculation speed of the neural networks. In order to validate the performance of the proposed controller, the experiment is conducted on the shape control system in a 300 mm four-high reversing cold rolling mill. The experimental results show that the SNN-PID with Smith predictor controller can effectively compensate the delay effects and achieve better control performance than the conventional PID controller.

An Adaptive Single Neural Control for Variable Step-Size P&O MPPT of Marine Current Turbine System

Marine current energy has been increasingly used because of its predictable higher power potential.Owing to the external disturbances of various flow velocity and the high nonlinear effects on the marine current turbine(MCT)system,the nonlinear controllers which rely on precise mathematical models show poor performance under a high level of parameters’uncertainties.This paper proposes an adaptive single neural control(ASNC)strategy for variable step-size perturb and observe(P&O)maximum power point tracking(MPPT)control.Firstly,to automatically update the neuron weights of SNC for the nonlinear systems,an adaptive mechanism is proposed to adaptively adjust the weighting and learning coefficients.Secondly,aiming to generate the exact reference speed for ASNC to extract the maximum power,a variable step-size law based on speed increment is designed to strike a balance between tracking speed and accuracy of P&O MPPT.The robust stability of the MCT control system is guaranteed by the Lyapunov theorem.Comparative simulation results show that this strategy has favorable adaptive performance under variable velocity conditions,and the MCT system operates at maximum power point steadily.

Single Phase Induction Motor Drive with Restrained Speed and Torque Ripples Using Neural Network Predictive Controller

In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of DC drives. Precise control of drives is the main attribute in industries to optimize the performance and to increase its production rate. In motion control, the major considerations are the torque and speed ripples. Design of controllers has become increasingly complex to such systems for better management of energy and raw materials to attain optimal performance. Meager parameter appraisal results are unsuitable, leading to unstable operation. The rapid intensification of digital computer revolutionizes to practice precise control and allows implementation of advanced control strategy to extremely multifaceted systems. To solve complex control problems, model predictive control is an authoritative scheme, which exploits an explicit model of the process to be controlled. This paper presents a predictive control strategy by a neural network predictive controller based single phase induction motor drive to minimize the speed and torque ripples. The proposed method exhibits better performance than the conventional controller and validity of the proposed method is verified by the simulation results using MATLAB software.

Expert control strategy using neural networks for electrolytic zinc process

The most important parameters which control the electrolytic process are the concentrations of zinc and sulfuric acid in the electrolyte. An expert control strategy for determining and tracking the optimal concentrations was proposed, which uses neural networks, rule models and a single loop control scheme. First, the process was described and the strategy that features an expert controller and three single loop controllers was explained. Next, neural networks and rule models were constructed based on statistical data and empirical knowledge on the process. Then, the expert controller for determining the optimal concentrations was designed through a combination of the neural networks and rule models. The three single loop controllers used the PI algorithm to track the optimal concentrations. Finally, the implementation of the proposed strategy were presented. The run results show that the strategy provides not only high purity metallic zinc, but also significant economic benefits.

基于虚拟仿真技术的高精度数控机床自动控制系统

为实现高精度数控加工,设计基于虚拟仿真技术的高精度数控机床自动控制系统。该系统依托SolidWorks、Deep Exploration等软件,建立数控机床虚拟仿真三维模型,设计数控机床控制约束关系,获取数控机床运行数据,并将该数据内的跟随误差、误差积分、误差微分等作为控制输入变量,设计由神经网络和PID组成的混合控制器,利用该混合控制器输出数控机床自动控制量,实现高精度数控机床自动控制。实验表明:该系统在各种运行环境下的软件移植完整程度均超过95%,在控制数控机床运动速度时,跟随误差始终保持在-50~50 mm/min,应用效果较为显著。

足式机器人腿部关节改进单神经网络PID控制算法研究

为了满足液压足式机器人在复杂环境中实现精确、快速的腿部关节控制需求,把单神经网络PID能够实时调节参数的优点运用到足式机器人液压机械腿关节的控制中,在单神经网络PID的基础上增加机械腿关节的位置和速度控制算法,形成改进单神经网络PID,实现了对神经元比例参数自调整、PID参数的自整定,能够较好地适应内、外参数的变化,增强了腿部关节的快速性、精确性。在Simulink中进行建模仿真以及在设计的以STM32为中央处理芯片的控制平台上进行实验测试,结果表明:改进单神经网络PID在足式液压机器人的腿部关节控制中具有响应速度快、超调量小、控制精度高、鲁棒性强等优点。

虚假数据注入式攻击下无人水面船舶自适应神经输出反馈轨迹跟踪控制

本文主要研究网络环境下无人水面船舶(Unmanned surface vessels,USVs)遭受虚假数据注入式(False-data-injection,FDI)攻击的跟踪控制问题.其中,内部和外部不确定以及输入饱和约束等实际因素均考虑在设计中.在控制设计过程中,为避免将船舶速度的攻击信号引入闭环系统,采用分类重构思想,构造一种新的神经网络(Neural network,NN)状态观测器,同时重构船舶速度和攻击信号.进一步,在backstepping设计框架下,利用重构的攻击信号补偿USVs运动学通道因虚假数据注入式攻击引起的非匹配不确定项.在动力学设计通道中,利用自适应神经技术和单参数学习法,重构由内部和外部不确定组成的复合不确定部分,进而提出自适应神经输出反馈控制方案.理论分析表明,即便在FDI攻击、内外不确定以及执行器饱和约束的情况下,所提控制方案仍能迫使USVs跟踪给定的参考轨迹.同时,仿真和比较结果证实了所提控制方案的有效性和优越性.

基于递归径向基神经网络滑模的多功能柔性多状态开关控制方法

近年来,新能源和电动汽车的渗透比例逐渐增高,给配电网的潮流优化和电能质量治理带来严峻挑战。针对分布式电源的随机性和间歇性问题,设计一种基于递归径向基神经网络(RRBFNN)滑模的多功能柔性多状态开关(FMS)控制方法,在实现功率交互和多端单相接地故障柔性消弧的同时,增强FMS的抗扰能力。首先考虑扰动的影响,设计一种改进RRBFNN滑模控制方法,以克服传统滑模控制固有的抖振现象和对系统精确数学模型的依赖,并减小并网暂态冲击;柔性消弧控制采用微积分型滑模面,理论推导出0轴电压控制律,提高故障电流抑制率;进一步通过李雅普诺夫定理证明所设计方法的稳定性和收敛性。最后,在Matlab/Simulink中搭建三端口FMS及其控制系统的仿真模型,通过对比仿真验证了所提策略的可行性和有效性。

NEW HYDRAULIC ACTUATOR'S POSITION SERVOCONTROL STRATEGY

A new hydraulic actuator-hydraulic muscle （HM） is described, and the actuator＇s features and applications are analyzed, then a position servocontrol system in which HM is main actuator is set up. The mathematical model of the system is built up and several control strategies are discussed. Based on the mathematical model, simulation research and experimental investigation with subsection PID control, neural network self-adaptive PID control and single neuron self-adaptive PID control adopted respectively are carried out, and the results indicate that compared with PID control, neural network self-adaptive PID control and single neuron self-adaptive PID control don＇t need controlled system＇s accurate model and have fast response, high control accuracy and strong robustness, they are very suitable for HM position servo control system.

Hybrid intelligent PID control design for PEMFC anode system

Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal levels during steady operation. In view of characteristics and requirements of the system, a hybrid intelligent PID controller is designed specifically based on dynamic simulation. A single neuron PI controller is used for anode humidity by adjusting the water injection to the hydrogen cell. Another incremental PID controller, based on the diagonal recurrent neural network (DRNN) dynamic identification, is used to control anode pressure to be more stable and exact by adjusting the hydrogen flow rate. This control strategy can avoid the coupling problem of the PEMFC and achieve a more adaptive ability. Simulation results showed that the control strategy can maintain both anode humidity and pressure at ideal levels regardless of variable load, nonlinear dynamic and coupling characteristics of the system. This work will give some guides for further control design and applications of the total PEMFC generator.

Association between PPARG genetic polymorphisms and ischemic stroke risk in a northern Chinese Han population: a case-control study

Two common polymorphisms of the peroxisome proliferator-activated receptor gamma(PPARG) gene, rs1801282 and rs3856806, may be important candidate gene loci affecting the susceptibility to ischemic stroke. This case-control study sought to identify the relationship between these two single-nucleotide polymorphisms and ischemic stroke risk in a northern Chinese Han population. A total of 910 ischemic stroke participants were recruited from the First Hospital of China Medical University, Shenyang, China as a case group, of whom 895 completed the study. The 883 healthy controls were recruited from the Health Check Center of the First Hospital of China Medical University, Shenyang, China. All participants or family members provided informed consent. The study protocol was approved by the Ethics Committee of the First Hospital of China Medical University, China on February 20, 2012(approval No. 2012-38-1). The protocol was registered with the Chinese Clinical Trial Registry(registration number: ChiCTR-COC-17013559). Plasma genomic DNA was extracted from all participants and analyzed for rs1801282 and rs3856806 single nucleotide polymorphisms using a SNaPshot Multiplex sequencing assay. Odds ratios(ORs) and 95% confidence intervals(CIs) were calculated using unconditional logistic regression to estimate the association between ischemic stroke and a particular genotype. Results demonstrated that the G allele frequency of the PPARG gene rs1801282 locus was significantly higher in the case group than in the control group(P < 0.001). Individuals carrying the G allele had a 1.844 fold increased risk of ischemic stroke(OR = 1.844, 95% CI: 1.286–2.645, P < 0.001). Individuals carrying the rs3856806 T allele had a 1.366 fold increased risk of ischemic stroke(OR = 1.366, 95% CI: 1.077–1.733, P = 0.010). The distribution frequencies of the PPARG gene haplotypes rs1801282-rs3856806 in the control and case groups were determined. The frequency of distribution in the G-T haplotype case group was significantly higher than that in the control group. The risk of ischemic stroke increased to 2.953 times in individuals carrying the G-T haplotype(OR = 2.953, 95% CI: 2.082–4.190, P < 0.001). The rs1801282 G allele and rs3856806 T allele had a multiplicative interaction(OR = 3.404, 95% CI: 1.631–7.102, P < 0.001) and additive interaction(RERI = 41.705, 95% CI: 14.586–68.824, AP = 0.860;95% CI: 0.779–0.940;S = 8.170, 95% CI: 3.772–17.697) on ischemic stroke risk, showing a synergistic effect. Of all ischemic stroke cases, 86% were attributed to the interaction of the G allele of rs1801282 and the T allele of rs3856806. The effect of the PPARG rs1801282 G allele on ischemic stroke risk was enhanced in the presence of the rs3856806 T allele(OR = 8.001 vs. 1.844). The effect of the rs3856806 T allele on ischemic stroke risk was also enhanced in the presence of the rs1801282 G allele(OR = 2.546 vs. 1.366). Our results confirmed that the G allele of the PPARG gene rs1801282 locus and the T allele of the rs3856806 locus may be independent risk factors for ischemic stroke in the Han population of northern China, with a synergistic effect between the two alleles.

The PID control based on CHAOS-RBF in stove application

Introduces the ceramic stove controlling system controlled by MSP430 single-chip computer. The system ameliorate the PID control method, adopts CHAOS-RBF, improves the accuracy of temperature control largely. There are two parts in this system, the lower machine measures the data and the upper machine with responsibility for data processing, displaying data and so on. Mean-while, using the serial communication RS-485 to realize the control of principal and subordinate station.

基于神经网络PID的疏浚管道泥浆流速控制

疏浚作业中,泥浆管道内物料的组成、粒径、浓度等随水下地形土质等变化很大,易造成流速波动甚至堵管、爆管等故障,因此泥浆流速稳定控制对泥浆输送的效率和安全具有重要意义;疏浚管道输送系统具有非线性、大时滞和参数时变等特征,传统PID控制方法效果不佳,故此将BP神经网络和传统PID控制算法相结合,并将其应用于泥浆流速控制中;以河海大学管道输送实验平台为对象,采用受控自回归CAR模型描述泥泵变频器频率与管道泥浆流速之间的关系,通过实验和数值处理对模型进行离线辨识;在此基础上通过仿真对比传统PID、单神经元PID和BP-PID的流速控制性能,发现BP-PID控制器的超调量仅为3.8%,响应时间为11 s,控制性能较好;最后通过在体积浓度-10%到-30%泥浆范围内,泥浆浓度小幅度和大幅度增减实验,对流速控制方法进行了验证,结果表明在浓度平缓或剧烈波动时,采用BP-PID控制算法的流速控制系统,均能够在保证输送安全的前提下,快速、稳定地达到目标流速,具有较好的自适应控制性能。

基于单神经元PID水平炮控系统稳定控制

为保证坦克行进间炮塔始终处于稳定位置,针对系统齿隙进行非线性补偿。利用径向基函数(radical basis function,RBF)神经网络对炮塔水平方向调速系统进行系统辨识,同时采用齿隙死区模型代表齿隙环节,建立水平向的模拟炮控系统;提出一种单神经元PID控制器;模拟外界扰动,利用计算机对控制过程完成仿真。仿真结果表明:在给定条件下,考虑齿隙因素,炮塔水平向位置与稳定位置的误差始终足够小。

采煤机摇臂智能调高控制系统设计与功能实现

针对采煤机在实际生产中需要根据煤层条件对其摇臂的截割高度自适应的调节控制的要求,结合采煤机当前自动调高系统的控制需求,完成了采煤机关键器件的选型设计;并结合采煤机摇臂的智能化调高要求提出了PID控制策略,对比传统PID控制器和单神经元PID控制器的控制效果,为今后采煤机摇臂的智能化调高奠定基础。

一种无人机姿态智能PID控制研究

姿态控制是无人机自主飞行控制的基础 ,其控制律设计结果对无人机飞行特性的影响至关重要 ,它决定了无人机是否能够满足自主飞行要求。为了解决在整个飞行包线中都能获得好的控制效果 ,本文引入了仿人智能比例 ,积分和单神经元控制等智能控制方法 ,设计出了一种用于无人机姿态控制的智能 PID控制器。控制品质主要表现为响应快、精度高、超调量小 ,能进行稳定的大范围调适 ,鲁棒性强。仿真研究表明 ,这种控制器算法简单 ,易于实现 ,且比常规

船舶航向离散非线性系统自适应神经网络控制

针对考虑舵机特性的船舶航向离散非线性控制系统,提出了一种基于神经网络的自适应控制设计方法。为了消除离散系统后推设计中存在"因果矛盾"的问题,原船舶航向离散系统通过变换得到等价的能够预测变量的前向预测系统。通过使用单一神经网络逼近系统的所有未知部分,该控制设计方法可以有效地减轻控制系统存在的"计算量膨胀"问题,并具有控制器结构简单,控制参数少,易于工程实现等优点。同时,稳定性分析证明闭环系统的所有信号一致最终有界,并能使得航向跟踪误差任意小。最后,运用"育鲲"轮进行仿真研究以证明所提方法的有效性。

基于RBF神经网络在线辨识的永磁无刷直流电机单神经元PID模型参考自适应控制

永磁无刷直流电机控制系统是多变量和非线性的。针对传统PID控制方法的不足,提出一种基于径向基函数神经网络在线辨识的单神经元PID模型参考自适应控制方法,并用于永磁无刷直流电机的控制中。该方法构造了一个径向基函数神经网络对系统进行在线辨识,建立其在线参考模型,由单神经元控制器完成控制器参数的自学习,并在数字信号处理器中实现控制参数的在线调节。系统较好地实现了给定速度参考模型的自适应跟踪,结构简单,能适应环境变化,具有较强的鲁棒性。

基于神经网络PID控制的系统非线性校正的研究

在对BP神经网络PID控制器系统研究的基础上,提出了单神经元的自适应PSD算法。该算法兼有单神经元和自适应PSD算法的特点,简单、实时性好、自适应能力强,可用于控制过程时变、有大滞后的较复杂的对象,是一种实用价值较高的自适应控制算法。文中采用BP神经网络PID控制与单神经元PSD自适应控制两种方法对压电式微位移系统进行非线性控制,并取得了良好的效果。

基于RBF在线辨识的AGV转向单神经元PID控制

针对自动引导车(AGV)转向系统的复杂、非线性和时变性,提出了基于RBF神经网络在线辨识的单神经元PID控制来改进常规的PID控制性能。在该控制系统结构中,采用RBF神经网络辨识器实现对转向系统的Jacobian矩阵信息的在线辨识,获得PID参数在线调整信息,并由单神经元PID控制器完成控制器参数的在线自整定,实现系统的智能控制。实验结果表明,与常规的PID控制方法相比,该方法具有较高的控制精度、较强的自适应性和鲁棒性,完全可适用于AGV转向系统的控制。