Adaptive Control of a Chaotic System with Delay

A model-reference adaptive control strategy is proposed for a delay chaotic system with known or unknown parameters.Theoretical analysis and numerical simulations show that the controlled system state can track an arbitrarily given reference trajectory that may be an equilibrium point,a periodic orbit or a chaotic orbit.

DESIGN OF OPTIMAL ROBUST CONTROLLER FOR SYSTEMS WITH LARGE PARAMETER PERTURBATION

This paper addresses the development of an optimal robust control algorithm for the systems with large parameter perturbation. The resulting control system may provide the satisfactory optimal robustness properties in the sense of a modified linear quadratic cost function. The related optimal gain matrix of the Controller for the systems with the defined upper bound of uncertainties could be designed to the minimal degrees via using the approaches to design of optimal robust control as developed here. Finally, the numerical examples are given to describe the applications and results of the proposed techniques.

Control synthesis for polynomial nonlinear systems and application in attitude control

A method for positive polynomial validation based on polynomial decomposition is proposed to deal with control synthesis problems. Detailed algorithms for decomposition are given which mainly consider how to convert coefficients of a polynomial to a matrix with free variables. Then, the positivity of a polynomial is checked by the decomposed matrix with semidefinite programming solvers. A nonlinear control law is presented for single input polynomial systems based on the Lyapunov stability theorem. The control synthesis method is advanced to multi-input systems further. An application in attitude control is finally presented. The proposed control law achieves effective performance as illustrated by the numerical example.

Intelligent Optimized Control System for Solvent Dewaxing Device

Methods for promoting product and reducing energy consumption in a solvent dewaxing device are putforward.Then,the authors mainly discuss about adaptive database,mathematical modeling,mathematical op-timized control algorithm,expert optimized control system and intelligent alarming system etc.At last,hard-ware and software of the intelligent optimized control system are introduced.Field test is finally chosen for thisstudy and proved that the intelligent optimized control system is effective.

Controller Design for Polynomial Nonlinear Systems with Affine Uncertain Parameters

借助于多项式分解，为多项式的一个控制计划有仿射的变化时间的不明确的参数的非线性的系统被介绍。多项式分解的想法是与免费变量把多项式的系数变换成一个矩阵，以便有甚至订单的多项式的 nonnegativity 能被线性矩阵不平等(LMI ) 检查解答者或双线性的矩阵不平等(BMI ) 解答者。为多项式的控制合成非线性的系统在这份报纸基于 Lyapunov 稳定性定理。构造 Lyapunov 功能并且发现反馈控制器被计算机编程自动地完成，算法交上这份报纸。为有相对高顺序的控制器的 multidimension 系统，与完整的单项的底构造的控制器将在众多的术语。克服这个问题，有最小的单项的术语的还原剂形式控制器被建议算法导出。然后，与获得限制瞄准最小的费用性能的非最优的控制被推进。控制计划完成由数字例子说明了的有效性能。

Fluidized bed control system based on inverse system method

The invertible of the Large Air Dense Medium Fluidized Bed (ADMFB) were studied by introducing the concept of the inverse system theory of nonlinear systems. Then the ADMFB, which was a multivariable, nonlinear and coupled strongly system, was decoupled into independent SISO pseudo-linear subsystems. Linear controllers were designed for each of subsystems based on linear systems theory. The practice output proves that this method improves the stability of the ADMFB obviously.

Nonlinear Model Predictive Control Based on Support Vector Machine with Multi-kernel

非线性的系统和它的特定的鉴定方法的 Multi-kernel-based 支持向量机器(SVM ) 模型结构被建议，它 SVM 是镇静的，线性内核功能与花键内核功能由 SVM 在系列列在后面。在这个模型的帮助下，非线性的模型预兆的控制能被转变到线性模型预兆的控制，并且因而预兆的控制是可能的发源的 multi-step-ahead 的最佳的输入的一个统一分析答案。这个算法不要求联机反复的优化以便对有更少的计算的即时控制合适。pH 中立化过程和 CSTR 反应堆的模拟结果显示出介绍算法的有效性和优点。

A Non-linear Non-ideal Model of Simulated Moving Bed Chromatography for Chiral Separations

A non-linear non-ideal model,taking into account non-linear competitive isotherms,axial disperison,film mass transfer,intraparticle diffusion,and port periodic switching,was developed to simulate the dynamics of simulated moving bed chromatography(SMBC),The model equations were solved by a new efficient numerical technique of orthogonal collocation on finite elements with periodical movement of conceantration vector,The simulated SMBC performance is in accordance with the experimental results reported in the literature for separation of 1,1''''''''-bi-2-naphtol enantiomers using SMBC,This model is useful for design,operation ,optimization and scale-up of non-linear SMBC for chiral separations with significant non-ideal effects,especially for high solute concentration and small intraparticle diffusion coefficient or large chiral stationary phase particle.

Online process monitoring for complex systems with dynamic weighted principal component analysis

Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach.The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.

Modeling and Simulation of Heterogeneous Catalyzed Propylene Polymerization

A novel mathematical model for single particle slurry propylene polymerization using heterogeneous Ziegler-Natta catalysts has been developed to describe the kinetic behavior,the molecular weight distribution,the monomer concentration,the degree of polymerization,the polydispersity index(PDI),etc.This model provides a more valid mathematical description by accounting for the monomer diffusion phenomena at two levels as multi- grain model counts,and obtains results that are more applicable to the conditions existing in most polymerizations of industrial interest.Considering that some models on the mesoscale phenomena are so complex that some existing modeling aspects have to be simplified or even neglected to make the model convenient for use in interesting engi- neering studies,it is very important to put some effort into determining what sort of numerical analysis works best for these problems.For this reason,special attention is paid to these studies to explore an efficient algorithm using adaptive grid-point spacing in a finite-difference technique to figure out more practical mass transport models and convection-diffusion models efficiently.The reasonable outcomes,as well as the significant computation time sav- ing,have been achieved,thereby displaying the advantage of this calculation method.

A closed-loop particle swarm optimizer for multivariable process controller design

Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity. After the effectiveness, efficiency and robustness are tested by benchmark functions, CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.

VPLS Based Quality and Cost Control for Tennessee Eastman Process

Product quality and operation cost control obtain increasing emphases in modern chemical system engineering. To improve the fault detection power of the partial least square (PLS) method for quality control, a new QRPV statistic is proposed in terms of the VP (variable importance in projection) indices of monitored process variables, which is significantly advanced over and different from the conventional Q statistic. QRPV is calculated only by the residuals of the remarkable process variables (RPVs). Therefore, it is the dominant relation between quality and RPV not all process variables (as in the case of the conventional PLS) that is monitored by this new VP-PLS (VPLS) method. The combination of QRPV and T2 statistics is applied to the quality and cost control of the Tennessee Eastman (TE) process, and weak faults can be detected as quickly as possible. Consequently, the product quality of TE process is guaranteed and operation costs are reduced.

STEADY-STATE OPERATION ANALYSIS OF AN IDEAL HEAT INTEGRATED DISTILLATION COLUMN

A systematic approach for the steady-state operation analysis of chemical processes is pro-posed.The method affords the possibility of taking operation resilience into consideration during thestage of process design.It may serve the designer as an efficient means for the initial screening ofalternative design schemes.An ideal heat integrated distillation column(HIDiC),without any reboileror condenser attached,is studied throughout this work.It has been found that among the various va-riables concerned with the ideal HIDiC,feed thermal condition appears to be the only factor exertingsignificant influences on the interaction between the top and the bottom control loops.Maximuminteraction is expected when the feed thermal condition approaches 0.5.Total number of stages andheat transfer rate are essential to the system ability of disturbance rejection.Therefore,more stagesand higher heat transfer rate ought to be preferred.But,too many stages and higher heat transfer ratemay increase the load of the

Numerical Simulation of Asynchronous Simulated Moving Bed Chromatography

Asynchronous simulated moving bed chromatography (ASMBC), known also as the 'VARICOL' process, is more efficient and flexible than the well-known and traditional simulated moving bed chromatography (SMBC). A detailed model of ASMBC, taking account of non-linear competitive isotherms, mass transfer parameters, and complex port switching schedule parameters, was developed to simulate the complex dynamics of ASMBC.The simulated performance is in close agreement with the experimental data of chiral separation reported in the literature. The simulation results show that ASMBC can achieve the performance similar to SMBC with fewer columns and can achieve better performance than SMBC with the same total column number. All design and operation parameters can be chosen correctly by numerical simulation. This detailed ASMBC model and the numerical technique are useful for design, operation, optimization and scale-up of ASMBC.

Optimization of Internal Thermally Coupled Distillation Columns of Nonideal Mixture

Internal thermally coupled distillation column(ITCDIC) is a frontier in energy saving distillation research. In this paper, the optimal assessment on the energy saving and the operating cost for ITCDIC of nonideal mixture is explored. An evaluating method is proposed, and the pertinent optimization model is then derived. The ethanol-water system is studied as an illustrative example. The optimization results show that the maximum energy saving in ITCDIC process is about 35% and the maximum operating cost saving in ITCDIC process is about 30%,as compared with a conventional distillation column(CDIC) under the minimum reflux ratio operating; the optimal operating pressure of the rectifying section is found to be around 0.25 MPa; the effects of the feed composition,operating pressure and the heat transfer rate on operation are also found and analyzed. It is revealed that ITCDIC process possesses high energy saving potential and promising economical prospect.

Guest Editorial for Special Issue on Cyber-Physical Systems

Ⅰ.Introduction CYBER-PHYSICAL system is a system of collaborating computational elements to control physical entities.The coordination and the tight link between computational,virtual and physical resources in cyber-physical system will have a pervasive effect on our everyday life.The development of cyber-physical system will create new opportunities for the introduction of services that will enhance the quality of life

Simulation of Diode-Pumped Q-Switched Nd:YAG Laser Generating Eye-Safe Signal in IOPO Environment

Numerical simulation of diode-pumped Q-switched Nd:YAG laser leading to the generation of eye-safe signal in singly resonant Intracavity Optical Parametric Oscillator (IOPO) is presented. Starting from rate equations, the time dependent laser equations have been solved numerically, whereas the space-dependent OPO equations analytically. Our results show that 1.4 J diode laser (810 nm) pulse with 200 msec width, delivers 30 mJ Nd:YAG laser (1064 nm) pulse with 5 n-second width. This Nd:YAG laser further generates 9 mJ eye safe signal (1570 nm) pulse with 2.5 n-second width.

基于自适应分块主元分析模型的提高故障检测与分离（英文）

In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.

A statistical information-based clustering approach in distance space

Clustering, as a powerful data mining technique for discovering interesting data distributions and patterns in the nderlying database, is used in many fields, such as statistical data analysis, pattern recognition, image processing, and other usiness applications. Density-based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996) is a good erformance clustering method for dealing with spatial data although it leaves many problems to be solved. For example, BSCAN requires a necessary user-specified threshold while its computation is extremely time-consuming by current method uch as OPTICS, etc. (Ankerst et al., 1999), and the performance of DBSCAN under different norms has yet to be examined. In his paper, we first developed a method based on statistical information of distance space in database to determine the necessary hreshold. Then our examination of the DBSCAN performance under different norms showed that there was determinable relation etween them. Finally, we used two artificial databases to verify the effectiveness and efficiency of the proposed methods. ey words: DBSCAN algorithm, Statistical information,

复杂故障判定的基于结构化残差的多层次分析法(英文)

In industrial processes,there exist faults that have complex effect on process variables.Complex and simple faults are defined according to their effect dimensions.The conventional approaches based on structured residuals cannot isolate complex faults.This paper presents a multi-level strategy for complex fault isolation.An extraction procedure is employed to reduce the complex faults to simple ones and assign them to several levels.On each level,faults are isolated by their different responses in the structured residuals.Each residual is obtained insensitive to one fault but more sensitive to others.The faults on different levels are verified to have different residual responses and will not be confused.An entire incidence matrix containing residual response characteristics of all faults is obtained,based on which faults can be isolated.The proposed method is applied in the Tennessee Eastman process example,and the effectiveness and advantage are demonstrated.