Earticle

In the practical control systems, the dynamic range of actuatiors is limited(or saturated) when actuators are driven by sufficiently large signals. This gives rise to a nonlinearity as a result of actuator saturation. For example, the upper limit is imposed on productive capability by available factory space and capital equipment. Other examples of those kinds of actuator saturations are a maximum torque of the actua- ting motors and a throttle position in an aircraft speed control A saturating actuator may lead not only to a large overshoot during start-up and shut-down, but also to deterioration of the performance due to the uncertainties. That is, the speed of response is decreased and, possibly, the system output may not follow the lalrge reference inputs. The large-overshoot may be accompanied by rest wind-up(or called by integra- tor wind-up) which comes from controllers with integral action in saturation operation regions. Eventually, as the overshoot increases, the system has a limit cycle or becomes oscillatorily unstable. Due to these cir- cumstances, many studies are focused on the stability and robustness of the nonlinear systems with satu- rating actuator in the time-domain as well as in the frequency-domain.

This paper introduces the fuzzy scatter partition-based fuzzy inference system to construct the model for nonlinear process to analyze nonlinear characteristics. The fuzzy rules of fuzzy inference systems are generated by partitioning the input space in the scatter form using Fuzzy C-Means (FCM) clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the parameters of the consequence part are estimated by least square errors. The proposed model is evaluated with the performance using the data widely used in nonlinear process. Finally, this paper shows that the proposed model has the good result for high-dimension nonlinear process.

Because of the nonlinear characteristics of monitoring system in military barracks, the traditional KPCA method either have low sensitivity or unable to detect the fault quickly and accurately. In order to make use of higher-order statistics to get more useful information and meet the requirements of real-time fault diagnosis and sensitivity, a new method of fault detection and diagnosis is proposed based on multivariate statistical kernel principal component analysis (MSKPCA), which combines statistic pattern analysis framework (SPA) and kernel principal component analysis (KPCA). First, the transformation of multivariate statistics and kernel function are conducted in which technology of moving time window is used. Then, PCA is executed to analysis the kernel function obtained from the first step. Moreover, the statistics of T^2 and SPE and the control limits of them are calculated. Finally, simulations on a typical nonlinear numerical example show that the proposed MSKPCA method is more effective than PCA and KPCA in terms of fault detection and diagnosis.

We show that the minimum EOP (eye-opening penalty) obtained by tunable dispersion compensation is a function of a figure of merit for a nonlinear process, $I_0L_{eff}$, where $I_0$ is the optical intensity and $L_{eff}$ is the effective length of the interaction region. Using this rule, we do not need to conduct nonlinear simulations in all the cases of signal power and transmission length to obtain the signal distortion in dispersion-managed optical transmission. Instead, we need to conduct a simulation in only one case of a signal power and find the functional relation, and then we can obtain the values of the signal distortion in other cases using the discovered functional relation. This technique can reduce the number of nonlinear simulations to less than 10%.

We propose a new instrumental variable estimator of the complex parameter of a class of univariate complex-valued diffusion processes defined by the possibly non-stationary and/or nonlinear stochastic differential equations. On the basis of the exact finite sample distribution of the pivotal quantity, we construct the exact confidence intervals and the exact tests for the parameter. Monte-Carlo simulation suggests that the new estimator seems to provide a viable alternative to the maximum likelihood estimator (MLE) for nonlinear and/or non-stationary processes.

This paper proposed a modified observer based on Busawon's high gain observer using an appropriate time depended function, which can be chosen to make each estimated state converge faster to its real value. The stability of the modified observer is proved by using Lyapunov function. The modified nonlinear observer is applied to estimate the states in stirred tank bioreactor: out-put substrate concentration, output biomass concentration and the specific growth rate of the process. The convergences of the modified observer and Busawon's observer are compared trough simulation results. As the results, the modified observer converges faster to its real value than the well-known Busawon's observer.

Euler method is generalized to solve the system of nonlinear differential equations. The generalization is carried out by taking a special constant matrix S so that exp(tS) can be exactly computed. Such a matrix S is extracted from the Jacobian matrix of the given problem. Stability of the generalized Euler process is discussed. It is shown that the generalized Euler process is comparable to the fourth order Runge-Kutta method. We also exemplify that the important qualitative and geometric features of the underlying dynamical system can be recovered by the generalized Euler process.

This paper proposes a new direct numerical integration algorithm for solving equation of motion in structural dynamics problems with nonlinear stiffness. The new implicit method's degree of accuracy is higher than that of existing methods due to the higher order of the acceleration. Two parameters are defined, leading to a new family of unconditionally stable methods, which helps to take greater time steps in integration and eliminate concerns about the duration of solving. The method developed can be utilized for a number of solid plane finite elements, examples of which are given to compare the proposed method with existing ones. The results indicate the superiority of the proposed method.

As part of continuing efforts to investigate nonlinear dynamics and stability of film casting process, our earlier results obtained by Lee et al. (2001b) have been extended in the present study to cover the film casting of both extension thickening and extension thinning fluids. The same instability mechanism and draw resonance criterion previously derived have been found valid here, and a rather complex dynamic behavior of film width in contrast to that of film thickness has also been confirmed. The effect of fluid viscoelasticity on draw resonance, however, exhibits opposite results depending on whether the fluid is extension thickening or thinning, i.e., it stabilizes film casting in the former while destabilizing in the latter. The encapsulation extrusion method which recently has been successfully employed to stabilize industrially important paper coating process, has been theoretically explained in the present study as to why such stabilization is possible.

In the industrial film casting process with the two-stage cascade (velocity and tension controlled) loop, various aspects of nonlinear dynamics and chaotic motion have been investigated solving simple 1-D viscoelastic model. Most of the extensional deformation processes exhibit sustained-periodic oscillations or stable limit cycles of state variables (called draw resonance instability) beyond the critical onset, even if they are conventionally controlled systems. Besides such well-known instability, some intriguing nonlinear dynamic phenomena such as period-doubling and eventual chaotic motion of film thickness and width could be observed when a disturbance is imposed in the second tension-controlled system. In other words, the bifurcation mode is dramatically changed by a sinusoidal disturbance under the combined velocity-tension control condition in contrast to only constant take-up velocity condition inducing a supercritical Hopf bifurcation of draw resonance.

This paper provides an M/G/1 queueing model for the operations management problem at the toll plaza. This queueing process is incorporated with two non-linear integer programming models - the user cost minimization model during the peak times and the operating cost minimization model during the off-peak hours.

100MW급 유류 연소 드럼형 열병합발전소의 주증기온도와 드럼수위 제어를 위한 비선형 PMBC를 개발하고 그 성능을 PID 알고리즘과 비교하였다. "first principle" approach를 사용하여 개발된 프로세스 모델은 정상상태를 과도상태에서 그 정확도를 현장의 데이터와 비교 검증한 것으로 제어기 성능 평가와 설계에 적합한 정교한 것이다. PMBC와 PID 제어의 성능을 부하증가와 연료의 열량변화 등의 외란에 비교한 결과, PMBC가 PID에 비하여 속응성, 절대오차 적분차, 제어노력 등에서 월등하게 우수한 것을 확인하였다. 것을 확인하였다.

In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.

In this paper, we construct the on-line model structure for the nonlinear process systems using the adaptive fuzzy-neural network. Adaptive fuzzy-neural network usually consists of two distinct modifiable structure, with both, the premise and the consequent part. These two parts can be adapted by different optimization methods, which are the hybrid learning procedure combining gradient descent method and least square method. To achieve the on-line model structure, we use the recursive least square method for the consequent parameter identification of nonlinear process. We design the interface between PLC and main computer, and construct the monitoring and control simulator for the nonlinear process. The proposed on-line modeling to real process is carried out to obtain the effective and accurate results.

In this paper, We proposed a parallel structure of the Neural Network model to nonlinear complex system. Neural Network was used as basic model which has learning ability and high tolerence level. This paper, we used Neural Network which has BP(Error Back Propagation Algorithm) model. But it sometimes has difficulty to append characteristic of input data to nonlinear system. So that, I used HCM(hard c-Means) method of clustering technique to append property of input data. Clustering Algorithms are used extensively not only to organized categorize data, but are also useful for data compression and model construction. Gas furance, a sewage treatment process are used to evaluate the performance of the proposed model and then obtained higher accuracy than other previous medels.

In this paper, we proposed an optimazation method using Genetic Algorithm for nonlinear system modeling. Fuzzy Neural Network(FNNs) was used as basic model of nonlinear system. FNNs was fused of Fuzzy Inference which has linguistic property and Neural Network which has learning ability and high tolerence level. This paper, We used FNNs which was proposed by Yamakawa. The FNNs was composed Simple Inference and Error Back Propagation Algorithm. To obtain optimal model, parameter of membership function, learning rate and momentum coefficient of FNNs are tuned using genetic algorithm. And we used simplex algorithm additionaly to overcome limit of genetic algorithm. For the purpose of evaluation of proposed method, we applied proposed method to traffic choice process and waste water treatment process, and then obtained more precise model than other previous optimization methods and objective model.

Under the condition of stable inverse a billinear model predictive control method for SISO and MIMO system with time delay is derived. For processes subject to a bounded disturbance the proposed control method with a classical recursive adaptation algorithm was shown to be stable in the sense of the convergence of parameter estimates and the boundedness of the control error. Several simulation results demonstrate the characteristics of the proposed bilinear model predictive control method.

This paper considers the problem of operation sequencing in nonlinear process planning, which is the problem of selecting and sequencing operations required to produce a part with the objective of minimizing the sum of operation processing costs and machine, setup and tool change costs. Main constraints are the precedence relations among operations. The problem can be decomposed into two subproblems: operation selection and operation sequencing. We suggest four simulated annealing algorithms, which solve the two subproblems iteratively until a good solution is obtained. Here, the operation selection problem can be solved using a shortest path algorithm. Application of the algorithms is illustrated using an example. Also, to show the performances of the suggested algorithms, computational experiments were done on randomly generated test problems and the results are reported. In particular, one of the suggested algorithms outperforms an existing simulated annealing algorithm.

In this paper, we propose the Optimal Polynomial Neural Networks(PNN) for nonlinear process. The PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and modified quadratic, and is connected as various kinds of multi-variable inputs. The conventional PNN depends on experience of a designer that select No. of input variable, input variable and polynomial type. Therefore it is very difficult a organizing of optimized network. The proposed algorithm identified and selected No. of input variable, input variable and polynomial type by using Genetic Algorithms(GAs). In the sequel the proposed model shows not only superior results to the existing models, but also pliability in organizing of optimal network. Medical Imaging System(MIS) data is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

본 논문은 최적 탐색 알고리즘인 유전자 알고리즘을 이용하여 다항식 뉴럴네트워크(Polynomial Neural Networks : PNN)의 최적 설계가 그 목적이다. 기존의 다항식 뉴럴네트워크는 확장된 GMDH(Group Method of Data Handling) 방법에 기반을 두며, 네트워크의 성장과정을 통하여 각 층의 다항식뉴런(혹은 노드)에서 고정된 (설계자에 의해 미리 선택된) 노드 입력들의 수뿐만 아니라 다항식 차수(1차, 2차, 그리고 수정된 2차식)를 이용하였다. 더구나, 그 방법은 학습을 통해 생성된 PNN이 최적 네트워크 구조를 가진다는 것을 보증하지 못한다. 그러나, 제안된 GA-based PW 모델은 다음의 파라미터들- 즉 입력변수의 수, 입력변수, 및 다항식 차수-을 유전자 알고리즘을 이용하여 선택 동조함으로써 그 구조를 구조적으로 더 최적화된 네트워크가 되도록 하고, 기존의 PNN보다 훨씬 더 유연하고, 선호된 뉴럴 네트워크가 되도록 한다. 하중계수를 가진 합성성능지수가 그 모델의 근사화 및 일반화(예측) 능력 사이의 상호 균형을 얻기 위해 제안된다. GA-based PNN의 성능을 평가하기 위해 그 모델은 가스 터빈발전소의 NOx 배출 공정 데이터로 실험된다. 비교해석은 제안된 GA-based PNN이 앞서 나타난 다른 지능모델보다 더 우수한 예측능력뿐만 아니라 높은 정확성을 가진 모델임을 보인다.