基于惯量辨识的测试转台自适应内模控制

为了降低系统模型参数变化对测试转台控制系统的影响,提出了基于惯量辨识技术的自适应内模控制方案.首先建立了某型号测试转台数学模型,设计了测试转台内模控制器,在此基础上采用基于扰动转矩观测器的惯量辨识算法来辨识测试转台转动惯量的变化情况,然后通过模糊控制器根据转动惯量的变化自动调整内模控制器的参数,从而确保了控制器的控制性能.仿真和实验结果表明,该控制方案对惯量的变化有着很强的自适应性,提高了测试转台控制系统的抗干扰性能和系统的鲁棒性,取得了较好的控制效果.     

一类模型未知系统的辨识和混沌化控制

对于一类模型未知的非混沌系统采用模糊神经网络辨识其动力学特性, 将得到的模糊神经网络辨识模型应用于逆系统方法中, 实现了一类模型未知非混沌系统的混沌化控制. 该方法不依赖于被控对象的数学模型, 就可以进行有效控制. 研究了模糊神经网络辨识误差对控制精度的影响, 证明了适当设计参数可以使由辨识误差引起的控制误差小于辨识误差. 针对连续和离散两类系统的仿真研究证明了该方法的有效性.     

自适应内模控制方法在磨矿过程中的应用

针对磨矿生产过程中球磨机给矿环节存在的大滞后、大惯性、参数时变等特性,提出了一种增益自适应内模控制方法,将内模控制和对象增益参数辨识算法相结合,通过在线辨识被控对象模型的增益参数,对内模控制器和被控对象模型进行校正,以减少模型参数变化和各种干扰对系统的影响。计算机仿真和实际应用效果均表明,与常规PID控制及IMC控制效果相比,该控制方法对于阶跃响应具有超调小、响应时间快、鲁棒性能好等特点,满足磨矿生产过程对控制器性能的要求,实现了球磨机给矿环节的稳定化控制,为选矿过程综合自动化系统的实施奠定了基础。     

自适应内模控制方法在磨矿过程中的应用

针对磨矿生产过程中球磨机给矿环节存在的大滞后、大惯性、参数时变等特性,提出了一种增益自适应内模控制方法,将内模控制和对象增益参数辨识算法相结合,通过在线辨识被控对象模型的增益参数,对内模控制器和被控时象模型进行校正,以减少模型参数变化和各种干扰对系统的影响.计算机仿真和实际应用效果均表明,与常规PID控制及IMC控制效果相比,该控制方法对于阶跃响应具有超调小、响应时间快、鲁棒性能好等特点,满足磨矿生产过程对控制器性能的要求,实现了球磨机给矿环节的稳定化控制,为选矿过程综合自动化系统的实施奠定了基础.     

基于PCS7的内模控制器设计与应用

针对工业对象的高阶以及非线性特性,采用二阶加纯滞后模型作为内模控制的内部模型,提出了一种通用内模控制器的设计方法;根据内模控制的原理以及二阶加纯滞后模型的差分方程,推导出通用内模控制器的算法.通过PCS 7 V6.1系统软件中的结构化控制语言SCL将内模控制算法编译成可通用的FB功能块,在西门子过程控制系统PCS7中实现了内模控制;最后利用精度较高的最小二乘法辨识被控对象的模型,将该通用内模控制器应用在工业中普遍使用的高阶大滞后对象换热器的出口温度控制过程中,取得了很好的应用效果;应用表明,内模控制器在工业DCS应用过程中具有高性价比,能有效地拓展内模控制的应用空间.     

大时滞过程的增益自适应内模控制算法的研究与实现

针对大时滞、慢时变对象稳态增益改变对系统性能的影响，提出一种具有三级低通滤波的增益自适应内模控制算法。通过自适应辨识机构识别和计算被控对象稳态增益，自动整定控制器和对象模型增益，使大时滞系统在增益变化较大且模型参数失配的情况下，仍具有良好的跟踪性和鲁棒性。仿真结果表明：增益自适应算法投入后，能有效地克服大增益变化对系统控制性能的影响，明显改善系统的控制品质，证明了该控制算法的有效性和可行性。     

改进型内模控制系统的稳定性与鲁棒跟踪条件

为了改善系统的响应性能或解决常规内模控制不能用于不稳定对象的问题,通常在常规内模控制结构中增设一个反馈环节,但这种改进型内模控制系统的性能分析尚缺乏系统的、令人满意的结果．为此,利用基于传递函数互质分解的频域理论,系统地分析了这种改进型内模控制系统的稳定性和稳态性能,得到了保证系统内稳定和鲁棒跟踪的一般性结论,并且无论控制对象稳定与否、系统连续或离散,该结论都适用．     

非线性动态系统神经模糊建模与内模/PID双重控制系统设计

非线性动态系统的内模控制要求建立精确的对象正模型和逆模型,这对于大多数实际对象是难以做到.提出了基于一类神经模糊模型的非线性动态系统建模方法,并在此基础上研究了基于神经模糊模型的非线性系统的内模控制设计.基于输入输出数据辨识的对象正模型和逆模型存在着模型失配问题,导致神经模糊内模控制范围变窄和控制鲁棒性降低,为了改善系统的性能,提出了神经模糊内模控制与PID控制结合的双重控制策略.对CSTR的反应物浓度控制研究表明,双重控制策略能有效地拓宽系统可控范围,改善系统性能.仿真结果证明该控制策略简单而有效.     

变滤波器参数的内模控制在炉内脱硫系统中的应用

基于内模控制、模糊控制,针对炉内脱硫过程对象的大迟延、大惯性、变参数的特点,设计了变滤波器参数的内模控制系统。该控制方案对工况变化具有较好的自适应能力且实现简单。对某300MW循环流化床机组炉内脱硫系统的3种典型工况进行模型辨识及控制仿真,结果表明,该控制方案,超调小,过渡时间短,动态性能好,控制效果优于PID控制与内模控制。     

基于继电辨识的大时滞对象的内模控制

采用改进的饱和继电反馈实验辨识高精度的过程模型，将内模控制与Smith预估控制器相结合，提出了一种适用于大时滞对象的内模控制结构，并按内模控制结构设计控制器。仿真实验表明该控制结构可获得比Smith预估控制更好的动态性能、鲁棒性和抗干扰性能，为大时滞对象的控制提供了一种新的实用方法。     

A Hybrid Control System for an Unstable Non-Stationary Plant with a Predictive Model

A hybrid control system with a discrete adaptive predictive model for a nonstationary unstable third-order dynamic plant in continuous time is synthesized and modeled. An adaptive state observer, estimating a variable parameter of the plant model with respect to the a quadratic quality criterion, was synthesized. Continuous estimation of the plant parameter for a discrete sample is used in a discrete adaptive control algorithm with a predictive model. A linear model of the control plant mimics the unstable vertical motion of plasma in a tokamak with a vertical cross-section elongated along the vertical axis compared to a given equilibrium position.     

水印能量估计的一般性框架

数字图像水印技术为网络环境下多媒体数据的传输提供了一个有效的版权保护方案 ,当前大多数的水印算法均利用实验来确定嵌入的水印的最佳能量。该文提出了一个一般性的水印算法框架,可以从理论上计算需嵌入水印的最佳容量或能量,对基于酉变换域方法的数字水印算法而言,该框架可以作为一个有用的指导方法。     

State-space constrained optimal control problems with control variables appearing linearly

This paper deals with the state-space constrained optimal control problems with control variables appearing linearly by the concept of decomposition. To solve this continuous optimal control problem, we first discretize the time and replace the system of differential equations by difference equations. For this resulting discrete optimal control problem, fixing the value of state variables reduces the given problem to a finite number of independent linear programming problems which are parameterized by the value of state variables. From this point of view, after para. meterizing by the value of state variables, we outer-linearize the resulting itifimal valuo functions in the minimond and apply the relaxation strategy to the new constraints arising as a consequence of outer-linearization. An algorithm is proposed which requires baek-and-forth iteration between a master problem and a finite number of linear programming subproblems. Finite convergence of this algorithm follows directly from the finite number of constraints of the master problem.     

Continuous and Discrete Trajectory Models for Dynamic Traffic Assignment

A continuous trajectory model is presented in which transportation networks are represented as topological constructs. The general formulation enhances existing analytic dynamic traffic assignment models by incorporating continuous single-link traffic flow models in a general, coherent, and relatively intuitive manner. Specific exact formulation based on a simplified kinematic wave traffic flow model with physical queues is presented as well.A discrete trajectory model is proposed as an approximation of the continuous model. The discrete model provides wide flexibility in choosing the level of aggregation with respect to time intervals, ranging from several hours, as typical in current practice of long-term travel forecasting models, to one second or less, as in microscopic simulations. An algorithm to find discrete approximate solutions is presented as well as accuracy measures to evaluate them. The effect of time resolution on model performance is examined by a numerical example.     

Timing and liveness in continuous Petri nets

Fluidification constitutes a relaxation technique for studying discrete event systems through fluidified approximated models, thus avoiding the state explosion problem. Moreover, the class of continuous models thus obtained may be interesting in itself. In Petri nets, fluidification leads to the so-called continuous Petri nets, which are technically hybrid models. Under infinite server semantics, timing a continuous Petri net model preserves the liveness property, but the converse is not necessarily true, and if the autonomous net model is not live, the timing may transform it into a live model. In this paper, we investigate the conditions on the firing rates of timed continuous models that make a given continuous system live.     

IDFC: A new approach to control bifurcation in TCP/RED

The main objective of this research is to analyze the bifurcation phenomenon in Internet congestion model for Transmission Control Protocol (TCP) with Random Early Detection (RED). This problem can be divided to many categories considering different viewpoints. Different approaches of modeling (continuous and discrete models) and various system structures (control in end node or routers) are some of these categories. The most significant method in control of such systems is Delayed Feedback Controller (DFC). In this paper, a discrete model is considered and a new algorithm Integral DFC (IDFC) is presented that has many preferences over similar algorithms, which are illustrated by simulation results and analytical discussions.     

基于小波分析的传感器故障诊断方法仿真研究

针对装甲车辆数据采集控制系统中传感器故障难于直接检测的问题,本文提出一种基于小波变换的方法对传感器故障进行诊断。首先介绍了小波分析的基本理论,对比阐述了连续小波变换和离散小波变换的原理和优缺点;其次利用Matlab/Simulink搭建模型进行仿真试验,采用连续小波变换对传感器故障进行检测,准确定位故障发生时刻;最后本文还选取了几种典型的传感器故障进行诊断,仿真结果验证了该方法的可行性和准确性。     

一种对象具有未建模动态时的间接式混合自适应极点配置方案

本文提出了一种对象具有未建模动态时的间接式混合自适应极点配置方案。在此方案中利用规范化技术和带遗忘因子的协方差重置递推最小二乘法估计对象参数,并直接根据连续对象设计离散自适应控制律,保证了闭环系统的鲁棒稳定性。理论分析和数字仿真均表明此方案优于相类似的全连续和全离散方案。     

一种对象具有未建模动态时的间接式混合自适应极点配置方案

In this paper, we propose an indirect hybrid adaptive pole placement scheme for plant with numodeled dynamics. Normalization technique and covariance reset least square method with forgetting factor are used for parameter estimation. We design the discrete adaptive control law directly from continuous plant, which guarantees the robust stability of the closed-loop system. Theoretical analysis and digital simulation results show that this scheme is superior to similar continuous scheme and discrete scheme.     

Optimum decoding-based smoothing algorithm for dynamic systems

A new smoothing algorithm for discrete models is presented. For the disturbance noise and the observation noise, only independency is assumed. Moreover the models’ functions are not limited to continuous functions, i.e. they can be non-continuous. This algorithm estimates the states by first quantizing them and then using the Viterbi decoding algorithm. Simulation results have shown that for some non-linear models the new algorithm performs better than the extended Kalman filter algorithm, while it performs almost as well as the Kalman filter algorithm for linear models with gaussian noise.