基于2D-OTSU图像边缘检测的回转窑工况识别方法

回转窑作为水泥窑炉煅烧过程的核心热工设备,其正常运转率与产品产量、质量及能耗紧密相关,由于回转窑内部核心反应区温度高且装置持续旋转,接触式温度传感器无法安装在窑内核心反应区域,而筒扫系统借助红外扫描装置能够实时监测回转窑筒体表面温度并间接反映窑内热工状况.签于此,提出一种新的基于筒扫图像2D-OTSU边缘检测的回转窑异常工况识别方法.该方法首先构建基于灰度梯度和局部灰度标准差信息的融合模型,并利用相对熵概念计算模型权重系数,进而通过1D-OTSU预搜索策略提升识别算法的效率;然后给出一种2D双阈值检测阈值分割策略,以保证边缘的连续性;最后采用工业现场实际的回转窑筒扫图像对所提方法与其他典型检测方法进行比较研究.对比实验结果表明,所提出方法的检测率和单位误报次数均优于其他算法并具备一定鲁棒性,能够有效检测回转窑内的异常工况,达到提高回转窑运转率的目的.     

Identification, prediction and detection of the process fault in a cement rotary kiln by locally linear neuro-fuzzy technique

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, locally linear neuro-fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 min prediction horizon. The other two models are presented for the two faulty situations in the kiln with 7 min prediction horizon. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used in this study.     

Finite-time stability and stabilization of time-delay systems

Finite-time stability and stabilization of retarded-type functional differential equations are developed. First, a theoretical result on finite-time stability inspired by the theory of differential equations, using Lyapunov functionals, is given. As it may appear not easily usable in practice, we show how to obtain finite-time stabilization of linear systems with delays in the input by using an extension of Artstein’s model reduction to nonlinear feedback. With this approach, we give an explicit finite-time controller for scalar linear systems and for the chain of integrators with delays in the input.     

Model predictive control of a rotary cement kiln

A first principles model of a cement kiln is used to control and optimize the burning of clinker in the cement production process. The model considers heat transfer between a gas and a feed state via convection and radiation. Furthermore, it contains effects such as chemical reactions, feed transport, energy losses and energy input. A model predictive controller is used to stabilize a temperature profile along the rotary kiln, guarantee good combustion conditions and maximize production. Moving horizon estimation was used for online estimation of selected model parameters and unmeasured states. Results from the pilot site are presented.     

Recursive identification of switched ARX systems

We consider the problem of recursively identifying the parameters of a deterministic discrete-time Switched Auto-Regressive eXogenous (SARX) model, under the assumption that the number of models, the model orders and the mode sequence are unknown. The key to our approach is to view the identification of multiple ARX models as the identification of a single, though more complex, lifted dynamical model built by applying a polynomial embedding to the input/output data. We show that the dynamics of this lifted model do not depend on the value of the discrete state or the switching mechanism, and are linear on the so-called hybrid model parameters. Therefore, one can identify the parameters of the lifted model using a standard recursive identifier applied to the embedded input/output data. The estimated hybrid model parameters are then used to build a polynomial whose derivatives at a regressor give an estimate of the parameters of the ARX model generating that regressor. The estimated ARX model parameters are shown to converge exponentially to their true values under a suitable persistence of excitation condition on a projection of the embedded input/output data. Such a condition is a natural generalization of the well known result for ARX models. Although our algorithm is designed for perfect input/output data, our experiments also evaluate its performance as a function of the level of noise for different choices of the number of models and model orders. We also present an application to temporal video segmentation.     

Using Informational Measures of Dependence in Statistical Linearization

Some problems arising in identification of stochastic systems due to using nonlinear measures of dependence of random variables (processes) were analyzed. Recent publications describing approaches based on a consistent measure of dependence such as the mutual information were discussed. A constructive procedure of creating the linear input-output model that is statistically equivalent to the nonlinear dynamic stochastic system with white-noise Gaussian input process was proposed. The condition for coincidence of the mutual information of the input and output processes of the system and the mutual information of the input and output processes of the model which is used as a criterion for statistical linearization is pivotal to this procedure. This approach enables one to establish explicit relations defining the weight coefficients of the linearized model without using the unrealistic—in the context of identification—a priori condition for knowledge of the joint distribution of the output processes of system and model.     

Identification of Boolean control networks

In this paper the identification of Boolean control networks is addressed. First, necessary and sufficient conditions are obtained for the identification of state equation from input-state data. Then a necessary and sufficient condition for a controllable Boolean network to be observable is presented. Based on these two results, a necessary and sufficient condition for the identification from input–output data is achieved. To practically identify the model, a numerical algorithm is proposed. Two particular cases: (i) identification of systems with a known network graph; (ii) identification of a higher order Boolean network, are also investigated. Finally, the approximate identification for large size networks is explored.     

多输入C-ARMA系统参数和时延的梯度追踪辨识算法

考虑含有输入时延的多输入单输出受控滑动平均自回归系统模型的参数和时延估计问题.参数化后的系统模型具有稀疏特性,受递阶辨识原理和压缩感知理论的启发,结合梯度搜索原理和匹配追踪思想提出针对该模型的梯度追踪迭代辨识算法.相比正交匹配追踪迭代算法,所提出的算法计算量较小且具有较高的辨识效率.最后通过数值仿真实例验证所提出算法的有效性.     

On a Sufficient Condition for Additivity in Quantum Information Theory

In this paper we give a sufficient condition for additivity of the minimum output entropy for a pair of given channels and an analytic verification of this condition for specific quantum channels breaking a closely related multiplicativity property [1, 2]. This yields validity of the additivity conjecture for these channels, a result obtained by a different method in [3]. Our proof relies heavily upon certain concavity properties of the output entropy, which are of independent interest.__________Translated from Problemy Peredachi Informatsii, No. 2, 2005, pp. 9–25.Original Russian Text Copyright © 2005 by Datta, Holevo, Suhov.This work was initiated when the second author was an overseas visiting scholar at St John’s College, Cambridge. Afterwards he was supported by the Russian Scientific School Program, project no. 1758.2003.1. The first and third authors worked in association with the CMI, University of Cambridge - MIT.     

Adaptive fuzzy identification and predictive control for industrial processes

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T–S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T–S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm’s performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T–S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T–S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.     

Worst case identification of continuous time systems via interpolation

We consider a worst case robust control oriented identification problem recently studied by several authors. This problem is one of identification in the continuous time setting. We give a more general formulation of this problem. The available a priori information in this paper consists of a lower bound on the relative stability of the plant, a frequency dependent upper bound on a certain gain associated with the plant, and an upper bound on the noise level. The available experimental information consists of a finite number of noisy plant point frequency response samples. The objective is to identify, from the given a priori and experimental information, an uncertain model that includes a stable nominal plant model and a bound on the modeling error measured in norm. Our main contributions include both a new identification algorithm and several new ‘explicit’ lower and upper bounds on the identification error. The proposed algorithm belongs to the class of ‘interpolatory algorithms’ which are known to possess a desirable optimality property under a certain criterion. The error bounds presented improve upon the previously available ones in the aspects of both providing a more accurate estimate of the identification error as well as establishing a faster convergence rate for the proposed algorithm.     

Modeling Connectionist Neuro-Fuzzy network and Applications

The main objective of this paper is to propose a Neuro-Fuzzy network, which can model a system from input–output data by automatically dividing the input–output space and extracting fuzzy if-then rules from numerical data. The structure of the network is simple with input, rule and output layers only. The connections between input and rule layer is used to derive the membership functions of the fuzzified part. Kohonens self-organizing learning algorithm is applied to partition the pattern space. Using this algorithm, similar rules are mapped close by and extraction of if-then rules is made easy. It can also adapt to a number of rules automatically. The proposed network is verified for three benchmark applications. Experimental results show that the adaptive method discussed in this paper not only trains in a few hundred iterations but also provides better performance measures when compared to conventional methods.     

On-line identification of SISO linear time-invariant delay systems from output measurements

The paper presents a theoretical treatment of on-line identification issues and develops parameter identification algorithms for a class of single input single output (SISO) linear time delay systems (LTDS) with uncertain time-invariant parameters. The delays can also be unknown if their possible values belong to a set consisting of a finite number of known values. Since only output measurements are assumed to be available, a sliding mode-based observer of an underlying SISO LTDS with uncertain parameters is constructed and utilized to design an adaptive identifier of system parameters. The theory is supported by an application of the developed algorithms to transient fuel identification in a port fuel injected (PFI) internal combustion engine.     

PID controller synthesis for a class of unstable MIMO plants with I/O delays

Conditions are presented for closed-loop stabilizability of linear time-invariant (LTI) multi-input, multi-output (MIMO) plants with I/O delays (time delays in the input and/or output channels) using PID (Proportional+Integral+Derivative) controllers. We show that systems with at most two unstable poles can be stabilized by PID controllers provided a small gain condition is satisfied. For systems with only one unstable pole, this condition is equivalent to having sufficiently small delay-unstable pole product. Our method of synthesis of such controllers identify some free parameters that can be used to satisfy further design criteria than stability.     

MIMO Smith predictor: Global and structured robust performance analysis

The purpose of this work is to extend the analysis of the Smith predictor structure to multiple input multiple output (MIMO) systems with uncertain multiple delays. This is applied to the set of models that can be factorized into a rational MIMO model in series with left/right diagonal (multiple) delay matrices. Necessary and sufficient conditions on the plant’s model to achieve this factorization are proved. This factorized structure is instrumental for the structured robustness analysis and applies to multiple pool open flow canals. Nominal and robust performance and stability are analyzed for the case of plants with multiple uncertain delays for two different uncertainty structures: global dynamic and structured parametric. The first uncertainty structure could also accommodate the dynamic uncertainty of the plant’s rational part as well. This analysis is applied to a controller designed for a two-pool canal system.     

Adaptive predictor-free control of a plant with delayed input signal

This paper considers the adaptive control problem for plants with delays in input signals using no predictors and plant’s output measurements only. The proposed algorithm ensures a desired accuracy of plant’s output tracking with respect to a reference signal. Finally, we provide simulation results to illustrate the performance of the algorithm.     

基于双启发动态规划的预分解窑控制器设计

针对水泥预分解窑熟料煅烧过程多变量、多扰动、非线性,难以建立精确的数学模型,实际生产中对工人的生产经验依赖性较强等问题,提出采用误差反向传播(BP)神经网络建立烧成系统数学模型,设计预分解窑双启发动态规划(DHP)控制器。DHP评价网络输出代价函数J关于状态量的偏导数,获得最优或者次优的控制信号,然后由动作网络输出控制信号,使系统得到期望的控制轨迹。仿真结果表明控制器响应时间较快,各参量超调量均不大,有助于实际系统的稳定运行。     

Output transformations and separation results for feedback linearisable delay systems

The class of strict-feedback systems enjoys special properties that make it similar to linear systems. This paper proves that such a class is equivalent, under a change of coordinates, to the wider class of feedback linearisable systems with multiplicative input, when the multiplicative terms are functions of the measured variables only. We apply this result to the control problem of feedback linearisable nonlinear MIMO systems with input and/or output delays. In this way, we provide sufficient conditions under which a separation result holds for output feedback control and moreover a predictor-based controller exists. When these conditions are satisfied, we obtain that the existence of stabilising controllers for arbitrarily large delays in the input and/or the output can be proved for a wider class of systems than previously known.     

Identification of stochastic linear systems in presence of input noise

Most identification methods rely on the assumption that the input is known exactly. However, when collecting data under an identification experiment it may not be possible to avoid noise when measuring the input signal. In the paper some different ways to identify systems from noisy data are discussed. Sufficient conditions for identifiability are given. Also accuracy properties and the computational requirements are discussed. A promising approach is to treat the measured input and output signals as outputs of a multivariable stochastic system. If a prediction error method is applied using this approach the system will be identifiable under mild conditions.     

Digital redesign of uncertain systems with state and input delays using evolutionary programming

This paper presents two novel issues: (i) digital modeling of the analog uncertain system in the presence of state and input delays and unstructured large parametric uncertainties; and (ii) digital redesign of the analog robust output tracking controller for the parametric uncertain system with state and input delays. The Newton's forward formula together with the evolutionary programming (EP) is utilized to find an equivalent discrete-time time-delayed parametric uncertain model of the continuous-time parametric uncertain system with multiple delays in both the state and input. Also, based on the EP global optima-search technique, the integration of the available analog robust controller and a parameter tuning algorithm is established to find a practically implementable "best" digital controller for the analog time-delayed uncertain system. The worst-case parameter set obtained from the analog time-delayed uncertain system with respect to the implemented "best" digital controller is searched to show the capability of the proposed robust digital controller.