基于神经网络的自整定PID控制器设计

针对非线性时变系统,设计了一种基于神经网络的参数在线自整定PID控制器.该控制器采用基于最近邻聚类方法的RBF神经网络快速学习算法,通过实时在线辨识,建立被控系统的精确模型并得到准确的Jacobian信息;同时将此信息提供给BP神经网络,从而实现PID控制器参数的自动在线整定. 仿真结果表明,该方法提高了算法的精度和速度并具有较快的系统响应和良好的跟踪特性.     

基于D分解的分数阶PID控制器的图形化参数整定

针对二自由度分数阶PID控制器设计的参数多,结构复杂等复杂问题,提出了一种基于D分解法和主导极点配置的新型参数整定方法。其基本原理首先基于动态响应指标约束进行主导极点配置,在确保闭环系统的响应特性良好的条件下确定系统超调量和调节时间,由此经过转换得到未知参数之间的函数关系。其次,使用D分解法,将未知参数在不影响的控制性能的条件下由多减少,再由相关参数取得系统性能稳定的参数域中优化,最后以差分进化算法为导向,以两种方法取得的相关约束条件为指标取得最优控制器参数,在确保所选极点的优势下使所设计的控制器达到理想的控制性能。最终,将所设计的控制器通过应用在整数阶和分数阶被控对象上,使用仿真验证新方法的鲁棒性和快速性,同时也表现了新方法的有效性和实用性。     

基于模糊推理的PI控制器参数整定方法

针对常用的PID控制器整定方法应用于高阶、时延、非线性工业对象时,许多整定公式不能获得满意的性能的问题,提出了一种对PI控制器参数进行整定的新方法。通过模糊推理将控制过程分为暂态和稳态,然后调用相应的模糊控制器来求取PI控制器的比例系数和积分系数。该方法既利用了模糊控制对被控对象的数学模型要求弱和抗干扰能力强、响应速度快等优点,也保留了传统PI控制器良好的稳态调节作用,达到了比较理想的控制效果。     

PID控制器参数自整定方法综述

由于PID控制器具有简单而固定的形式，而且具有良好的鲁棒性，在工业生产中得到广泛的应用。本文综述了各种PID控制器参数自整定方法及其最近的研究成果。     

基于可视化的PI控制器参数整定方法

在控制器优化设计问题的研究中,PI控制器的参数通常采用归一参数法和试凑法来整定,很难得到最优的整定参数.为解决上述问题,提出了一种快速且有效的四维数据可视化算法的PI控制器参数整定方法.采用可视化方法将伯德图图形簇体现在四维可视化幅频特性图和相频特性图里,可直接观察kp,ki与幅频特性和相频特性的关系,并可间接地将幅值裕度和相角裕度表现在频域特性里,通过观察频域特性的变化来达到整定的目的.仿真实例表明,提出的方法适用于低阶或高阶被控对象,得到了满意的效果,为控制器优化设计提供了依据.     

基于RBF神经网络整定的高速公路匝道PID控制器

研究RBF神经网络整定PID控制器的参数,并应用到高速公路入口匝道控制中。首先阐述了入口匝道控制原理,然后建立了高速公路交通流模型,并设计了RBF神经网络整定的高速公路匝道PID控制器,RBF神经网络通过对被控对象Jacobian信息的辨识来动态调节PID控制器的参数,最后用MATLAB软件进行系统仿真。仿真结果表明,该控制器具有优越的动态和稳态性能,用于高速公路入口匝道控制中效果良好。     

基于参数自整定Fuzzy-PID控制器的气体温控系统

本文设计建立了快响应气体温度控制系统，通过对系统控制策略的理论和实验研究，实现了快响应气体温度控制系统的高精度快响应控制。首先，结合运用传热学、热力学和气动伺服控制技术相关理论，建立了快响应气体温度控制系统的数学模型;其次，对快响应气体温度控制系统进行了相应控制策略的研究。本文设计出参数自整定FUZZY-PID控制器，同PID控制相比，本文所提出的控制策略能明显改善系统的动静态性能。     

基于BP神经网络的PID参数自整定的船舶操纵控制器研究

船舶操纵的模型参数具有非线性、慢时变特性。船舶操纵的传统控制方法的操纵性能不能令人满意。本文讨论一种应用ＢＰ神经网络实现ＰＩＤ参数自整定的控制方法。此法能根据船舶动态特性的变化,自动重新整定ＰＩＤ参数,从而改善了操纵性能和鲁棒性。     

闭环极点在指定区域内的ι1优化反馈控制器*

本文提出了一种闭环极点在指定区域内的l~1优化反馈控制器的设计方法。证明了对闭环极点的位置加以限制后,指标函数的下确界和无限制时的指标函数下确界相同。这就保证了在满足瞬态特性要求后,不会明显降低系统的抗扰性或鲁棒性。给出了任意逼近指标函数下确界的计算步骤和计算实例。     

On closed-loop system identification using polyspectral analysis given noisy input–output time-domain data

The problem of closed-loop system identification given noisy input–output measurements is considered. It is assumed that the closed-loop system operates under an external non-Gaussian input which is not measured. If the external input has non-vanishing integrated bispectrum (IB) and data IB is used for identification, then the various disturbances/noise processes affecting the system are assumed to be zero-mean stationary with vanishing IB. If the external input has non-vanishing integrated trispectrum (IT) and data IT is used for identification, then the various disturbances/noise processes affecting the system are assumed to be zero-mean stationary Gaussian. Noisy measurements of the (direct) input and output of the plant are assumed to be available. The closed-loop system must be stable but it is allowed to be unstable in open loop. Parametric modeling of the various noise sequences affecting the system is not needed. First the open-loop transfer function is estimated using the integrated polyspectrum and cross-polyspectrum of the time-domain input–output measurements. Then two existing techniques for parametric system identification given consistent estimates of the underlying transfer function, are exploited. The parameter estimators are strongly consistent. Asymptotic performance analysis is also carried out. A computer simulation example using an unstable open-loop system is presented to illustrate the proposed approach.     

基于神经网络的热处理炉温度自校正控制系统

热处理加热炉是一种具有纯滞后的大惯性不确定系统,常规的控制方法很难达到控制要求。本文提出了一种基于神经网络的自校正调节系统,可使加热炉按照预定的温度曲线升温,以获得良好的跟随性能。运行结果表明,这种系统可获得满意的控制效果。     

数控机床进给伺服系统神经网络自整定PID控制

在介绍BP神经网络结构和学习算法的基础上,给出了一种数控机床进给伺服系统基于BP神经网络的自整定PID控制算法,并设计了基于BP神经网络自整定PID控制器的结构.在Matlab仿真中证实,该算法减小了系统的调节时间,提高了系统的响应速度、抗干扰能力和对被控对象参数变化的适应能力.     

基于支持向量机的参数自整定PID非线性系统控制

对非线性系统提出了一种基于支持向量机的自整定PID控制新方法.用支持向量机辨识系统的非线性关系,并对之进行线性化,提取出瞬时线性模型,采用最小方差的准则获取PID控制器的最优参数.为改善控制器的性能,提出了一些改进措施,包括使用一阶滤波器、控制器参数更新标准及惩罚系数的调整等.通过对典型非线性系统的仿真,验证了该方法的有效性和可行性.     

Production-inventory system controller design and supply chain dynamics

The paper deals with the modelling and control of aggregated production-inventory systems as described by differential equations. Hitherto, research in the area has been characterized by the approximation of production delays by first-order lags rather than more realistic pure delays. We demonstrate the substantial qualitative differences between these two approaches and thus generate the motivation for the rest of the paper, which tackles pure delay systems. The application of some relatively new design methodologies for delay systems yields four design choices that are tested for their performance over a range of criteria including stability robustness. The investigation is then extended to the model of a supply chain comprising many such productioninventory systems. The mechanism by which disturbances can be transmitted along the supply chain causing disruption and incurring costs to other supply chain echelons is elucidated. A heuristic feedback policy designed adaptively to tune the individual system designs in response to such disturbances is presented.     

基于极点配置算法的温度自校正控制器

温度控制是工业生产过程中的常见问题,由于温度系统是一种慢时变的非线性系统,这就给温度的实时控制带来了一定的难度.为了解决这个问题,本论文设计了一种自校正控制方案,它把模型参数的在线辩识与控制器的在线设计有机结合在一起,参数辨识采用渐消记忆的递推最小二乘算法,而控制器采用极点配置算法.这就使得该控制系统不但能在线实时调整参数,并保证实际的闭环系统的零极点收敛于期望的零极点,达到较好控制效果.整个控制过程的算法比较简单,便于工程实现.该系统目前已经在软件和硬件上得以实现,无论是从仿真结果还是在实验室的试运行情况来看,该方案都取得了满意的效果.     

A two-degree-of-freedom PI controller based on events

In this paper, a completely event-based two-degree-of-freedom proportional-integral controller is presented. The architecture of the controller is based on event-based decoupled solutions for the set-point following and the load disturbance rejection tasks. For the first task, the solution is a design procedure that, by considering a first-order-plus-dead-time model of the process and a predesigned open-loop control action, produces an event-based feedforward controller that provides the required process variable transition with just two events. The second task is solved by applying two separate event-based proportional and integral control actions. Because the two tasks are initially solved independently, two solutions for coupling them are described. Illustrative examples of the performance of the controller are included as well as experimental results.     

基于全调节RBF神经网络的远程网络控制器设计

针对一类具有参数不确定项的非线性网络控制系统,提出一种基于全调节RBF神经网络的反馈线性化与远程状态反馈控制方法相结合的控制策略.该控制策略首先通过设计全调节RBF神经网络的权值W,中心值φ和影响范围σ的调节律,在线补偿系统的非线性及参数不确定项;然后利用状态反馈控制解决时延条件下的网络控制问题.通过Lyapunov稳定性理论给出了系统的稳定性定理,并通过仿真实验验证了该方法的有效性.     

Simultaneous closed-loop tuning of cascade controllers based directly on set-point step-response data

This study presents a novel closed-loop tuning method for cascade control systems, in which both primary and secondary controllers are tuned simultaneously by directly using set-point step-response data without resorting to process models. The tuning method can be applied on-line to improve the performance of existing underperforming cascade controllers by retuning controller parameters, using routine operating data. The goal of the proposed design is to obtain the parameters of two proportional-integral-derivative (PID)-type controllers, so that the resulting inner and outer loops behave as similarly as possible to the appropriately specified reference models. The tuning rule and optimization problem related to the proposed design are derived. Based on the rationale behind cascade control, the secondary controller is designed based on disturbance rejection to quickly attenuate disturbances. The primary controller is designed to accurately account for the inner-loop dynamics, without requiring an additional test. In addition, robustness considerations are included in the proposed tuning method, which enable the designer to explicitly address the trade-off between performance and robustness for inner and outer loops independently. Simulation examples show that the proposed method exhibits superior control performance compared with the previous (model-based) tuning methods, confirming the effectiveness of this novel tuning method for cascade control systems.     

基于神经网络理论的现场总线系统安全性评价的研究

本文在介绍神经网络基本原理的基础上，提出了BP神经网络的优化算法：BP-1算法。进而分析了神经网络应用于现场总线系统安全性评价的优点，提出了基于神经网络理论的现场总线系统安全性评价模型和实现方法。最后，以一个原型系统证明了该方法的有效性。