广义稳态的工业过程及其优化控制

阐述了工业过程中出现的拟周期和混沌等动态行为，并分析了形成的原因和条件．介绍了与之密切相关的非线性科学中的混沌、微分动力学系统以及遍历论等理论，综述了工业过程的广义稳态优化控制的研究成果、理论意义和实用价值及其发展方向．     

混沌与分形在化工过程控制中的应用

针对复杂工业过程控制的研究对象普遍存在非线性、复杂性和不确定性的特点。总结了国内外一些将混沌与分形应用于化工过程控制的情况,指出认识化工过程中的混沌现象进而进行控制是解决化工过程中某些复杂控制问题的有效途径。最后对非线性科学与控制理论相结合的前景进行了展望。     

混沌与混沌应用

混沌理论是非线性动力学系统的重要组成部分,它揭示了非线性科学的共同属性─有序与无序的统一,确定性与随机性的统一。混沌控制及其应用是非线性科学应用新的研究领域,其研究受到了非常广泛的重视。该文介绍了混沌控制的基本方法,阐述了混沌理论的普遍应用,并对今后的发展方向和困难提出了一些见解。     

混沌控制综述

混沌和混沌控制是非线性动力学的新理论和新的研究领域.混沌运动是非线性动力学系统所产生的复杂的不规则行为,它普遍存在于自然界的各个领域中.该文介绍了混沌的产生、特点及混沌控制的发展以及研究思想,对混沌控制的不同策略进行综述.重点阐述了OGY方法的思想、原理和特点,对自适应控制方法、连续反馈控制法、神经网络法等作了介绍,对今后可能存在的困难提出了一些见解,指出混沌控制应用前景和研究方向.     

地震波阻抗参数混沌控制反演

分析了地震非线性反演的混沌现象, 并且从测井资料出发, 以非线性动力学不动点理论及混沌控制理论为基础, 提出了混沌控制反演方法, 数值实验和应用实例表明, 该方法能有效地控制非线性反演系统在反演选代过程中出现的混沌现象, 得到较真实可靠的反演结果.     

基于混沌优化的非线性预测控制器

针对非线性系统的控制问题,本文将神经网络辨识、混沌优化和预测控制思想有机结合,提出了一种新型非线性预测控制器.该控制器以神经网络作为预测模型,混沌优化算法作为滚动优化策略,避免了非线性预测控制中复杂的梯度计算和矩阵求逆问题.另外在训练神经网络过程中,采用了带混沌机制的自适应学习率的BP算法,以提高神经网络的收敛能力和收敛速度.仿真研究说明了该非线性预测控制器的有效性及实时性.     

基于跟踪的混沌反控制

为了实现连续系统的混沌反控制,研究了基于跟踪已知混沌系统的混沌反控制方法,设计了非线性控制器使连续系统跟踪混沌输入信号,从而使非混沌系统产生与混沌输入信号拓扑共轭的混沌现象。采用的方法中控制器的参数选择比较灵活,为控制器的设计带来很大方便。同时,该法无需计算被控系统的Lyapunov指数,大大减少了混沌反控制的复杂计算。通过跟踪Lorenz混沌系统和超混沌Chen系统说明了该方法的设计过程,仿真结果表明了该方法的有效性和快速性。     

基于DSP的混沌信号源设计与实现

采用数字信号处理器DSP设计实现了各种已知非线性微分方程的混沌信号源.文中还对采用DSP产生的混沌信号提出了一种基于CCS与MATLAB结合的简单有效的信号混沌性检测方法.     

一种结合混沌映射的Enigma加密方案

近年来,混沌作为非线性系统中一种新的存在形式被广泛和深入地研究.随着计算机科学和信息技术的发展,密码技术的发展非常迅速.混沌系统对初始条件和系统参数的敏感依赖性、非周期性和遍历特性等,使其具有优良的密码学特性.本文在分析Enigma加密机制的基础上,将非线性混沌过程融入到Enigma加密过程中,设计了一种结合混沌映射的Enigma加密方案.该加密方案有效地利用了Enigma原有的近似非线性的加密机制,利用混沌过程,加强了置换的随机性,提高了加密方案的安全性.对图像及文字进行加密实验结果表明,本加密方案在密钥敏感性分析、相邻像素相关性以及差分攻击分析等方面,都有很好的表现.     

R（o）ssler系统的混沌控制

提出一种用输入－状态线性化来控制Ｒｏｅｓｓｌｅｒ混沌系统的有效方法,使该混沌系统实现了全局稳定（稳化）并能实现对原系统不稳定不平衡点和周期信号的稳态跟踪。该方法的特点是通过状态变换将非线性动态特性全部变成线性动态性,从而可以应用熟知的线性控方法。仿真结果证实了该方法的有效性。     

工业过程广义稳态优化控制研究

基于广义稳态和稳态集合的中心名义值概念,给出了工业过程广义稳态优化控制问 题的数学描述,并就该问题的存在性给予了充分性证明;此外.对处理工业过程广义稳态优化 控制问题的一种等效方法展开了深入讨论,给出了具体算法步骤,并对一个化工过程的广义 稳态优化算例进行了仿真计算;结果表明,文中关于广义稳态优化控制问题的定义是正确的, 算法是有效的.     

Approach to the analysis of the dynamics of industrial FCC units

Although it is known that FCC units are operated at a controllable steady state, it is commonly assumed that this state is pseudostable. An ignited steady state, theoretically stable, outside the operable domain has also been reported for the same set of operating parameters. The aim of this work is to introduce a theoretical method for the preliminary analysis of the controllability of an industrial FCC unit, at any set of conditions, using a nonlinear model directly. An approach for the analysis of the stability of the system zero dynamics is used to investigate the dynamic behaviour of two common operating policies when the unit is operating either in the standard or in the ignited steady state. It is used to predict some particular characteristics of the process, such as the presence of inverse response after the change in catalyst circulation rate, a common control action. The results derived from the analysis developed are discussed in terms of physical responses and verified by performing open loop dynamic simulations of an industrial FCC unit. The operation of the unit is simulated for both the standard and the ignited steady states.     

Nonlinear prediction of manufacturing systems through explicit and implicit data mining

Many processes in the industrial realm exhibit stochastic and nonlinear behavior. Consequently, an intelligent system must be able to ndapt to nonlinear production processes as well as probabilistic phenomena. To this end, an intelligent manufacturing system may draw on techniques from disparate fields, involving knowledge in both explicit and implicit form.In order for a knowledge based system to control a manufacturing process, an important capability is that of prediction: forecasting the future trajectory of a process as well as the consequences of the control action. This paper presents a comparative study of explicitaand implicit methods to predict nonlinear chaotic behavior. The evaluated models include statistica; procedures as well as neural networks and case based reasoning. The concepts are crystallized through a case study in the prediction of chaotic processes adulterated by various patterns of noise.     

Adaptive feedback linearization control of chaotic systems via recurrent high-order neural networks

In the realm of nonlinear control, feedback linearization via differential geometric techniques has been a concept of paramount importance. However, the applicability of this approach is quite limited, in the sense that a detailed knowledge of the system nonlinearities is required. In practice, most physical chaotic systems have inherent unknown nonlinearities, making real-time control of such chaotic systems still a very challenging area of research. In this paper, we propose using the recurrent high-order neural network for both identifying and controlling unknown chaotic systems, in which the feedback linearization technique is used in an adaptive manner. The global uniform boundedness of parameter estimation errors and the asymptotic stability of tracking errors are proved by the Lyapunov stability theory and the LaSalle-Yoshizawa theorem. In a systematic way, this method enables stabilization of chaotic motion to either a steady state or a desired trajectory. The effectiveness of the proposed adaptive control method is illustrated with computer simulations of a complex chaotic system.     

Industrial application of a nonlinear model predictive control to polymerization reactors

A nonlinear model predictive control has been developed and applied to an industrial polypropylene semi-batch reactor and a high density polyethylene continuous stirred tank reactor. The control system consists of two-tier algorithms: at the first tier, an LQI is formulated based on a successively linearized nonlinear first principles process model. At the second tier, actual control actions are determined in consideration of process constraints by solving a QP problem, which is formulated by linearizing the nonlinear model around the LQI trajectory. A simple state estimator, which is capable of providing offset-free estimates of the outputs at steady states, has been designed for each application. In the semi-batch reactor process, the controller was able to maximize the monomer feed while satisfying the heat removal constraint. In the high density polyethylene process, the performance of the grade transition control was greatly improved.     

一种新的稳态监测算法及其应用研究

本文基于滤波法的思想,引进了一种新的数据稳态监测算法启发式算法,介绍了该算法的实现过程,它不需要CST和MTE的区间稳态假设,判断方法不需要限制时域位置,可以沿着时间轴移动时间窗口来判断时间窗内过程是否处于稳态,与滤波法有类似之处。它拓展了滤波法的适用范围,其优点在于该算法不但可以用于判断历史时间窗内过程是否处于稳态,同时也可持续地监测最新的实时测量数据是否处于稳态。该算法在自主开发的工业数据平台中得到应用,应用结果表明,该算法简单可靠,对实时过程数据的稳态监测能给出满意的结果。     

A chaotic communication system of improved performance based on the Derivative-free nonlinear Kalman filter

The Derivative-free nonlinear Kalman Filter is used for developing a communication system that is based on a chaotic modulator such as the Duffing system. In the transmitter’s side, the source of information undergoes modulation (encryption) in which a chaotic signal generated by the Duffing system is the carrier. The modulated signal is transmitted through a communication channel and at the receiver’s side demodulation takes place, after exploiting the estimation provided about the state vector of the chaotic oscillator by the Derivative-free nonlinear Kalman Filter. Evaluation tests confirm that the proposed filtering method has improved performance over the Extended Kalman Filter and reduces significantly the rate of transmission errors. Moreover, it is shown that the proposed Derivative-free nonlinear Kalman Filter can work within a dual Kalman Filtering scheme, for performing simultaneously transmitter–receiver synchronisation and estimation of unknown coefficients of the communication channel.     

Application of stochastic control techniques to chaotic nonlinearsystems

A control algorithm based on stochastic control techniques is devised for chaotic nonlinear systems. The algorithm uses a state estimator based on the Kalman filter, and yields performance improvements in at least some regions of state space with respect to that obtainable by use of a controller utilizing only the conditional mean of the system state vector. The method is applied to two typical chaotic nonlinear systems (the Henon-Heiles system and the Lorenz system), and their behavior with control is explored numerically     

Construction of S-box based on chaotic Boolean functions and its application in image encryption

In numerous encryption frameworks, the first information is changed into encoded form by applying nonlinear substitutions and affecting diffusion. The goal of the nonlinear change is to accomplish high level of randomness in the image content. The choice of the source of randomness is critical because the success in cryptanalysis is demarked by the characteristics identified in the encrypted data. The chaotic frameworks show random conduct that is suitable for encryption applications where nonlinear transformations are needed in the middle of plaintext and the scrambled information. The application of nonlinear functional chaos-based system with embedded chaotic systems and binary chaotic sequences can prompt randomness and diffusion in the information. In addition to the high state of randomness, the requirement for various round keys is needed in a run of the mill substitution–permutation process. The proposed strategy kills the requirement for different round keys, which is suitable for high-speed communication frameworks. The measurable analyses performed on the proposed nonlinear algorithm which show improvement in encryption quality and safety against numerous brute-force and statistical attacks. Also, the proposed framework demonstrates high safety against differential and linear cryptanalysis.     

Implementation of regularization for separable nonlinear least squares problems

This paper considers a class of separable nonlinear least squares problems in which a model can be represented as a linear combination of nonlinear functions. A regularized nonlinear parameter optimization approach is presented for coping with the potential ill-conditioned problem of parameter divergence. Together with a regularization parameter detection technique, Tikhonov regularization and truncated singular value decomposition are utilized in the estimation of the linear parameters if the nonlinear parameters are changed during the parameter optimization process, which centers on a nonlinear parameter search using the Levenberg-Marquardt algorithm. Benefiting from the regularization in parameter optimization, the potential ill-conditioned issue can be avoided, and the multi-step-ahead forecasting accuracy of the estimated model may be largely improved. The usefulness of this approach is illustrated by means of a chaotic time-series prediction and nonlinear industrial process modeling.