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In this paper, the implementation of a Model Based Fault Detection and Diagnosis System, that uses fuzzy logic to determinate the nature of the detected faults in rolling mill equipments is presented. The system is built with 4 components which work independently. An Identification module estimates the parameters of a continuous domain second order transfer function model for the process by analyzing...
In multivariable control the study of loop interactions is of prime importance. The P or V-canonical form structures, where loop interactions are dealt with as feed-forward couplings, are popular transfer function representations used to describe multivariable processes. Another alternative design consists of a bank of several Single Input Single Output (SISO) controllers linked by feed-forward terms...
The optimal pole conditions for Laguerre models are available in the literature for the || • ||2 norms (for continuous-time or discrete-time systems, with or without an impulsive input signal, and in the time or frequency domains). Recently, the author was able to extend the available results i) to other || • ||p norms, and ii) to models whose responses to known input signals satisfy, in the time...
This paper describes a method by which a set of piece-wise constant autoregressive parameters could be obtained when the source signal is subject to a multistage analysis. It also illustrates an FIR prediction scheme with application to a speech process. It describes a method where the prediction is carried out on a sample by sample basis. This prediction scheme demonstrates the possibility of having...
The objective of the paper is to define a control framework for freeway traffic in which several vehicle classes are explicitly taken into account. The Model Predictive Control approach has been chosen for defining the proposed regulation scheme, in which both ramp metering and speed limitations are adopted as control actions. In the overall control scheme, the two types of control actions (ramp metering...
This paper reports a complete formulation of a model predictive control strategy having guaranteed nominal asymptotic stability. The formulation includes a successive linearisation procedure to obtain a linear model from a non-linear plant model. It gives a complete state-space derivation including long-range prediction, trajectory tracking and modelling of both measured feedforward disturbances and...
This article presents an application of a control strategy developed by the authors to a nonlinear model. The control strategy is based on a sliding mode controller that uses a generalized predictive controller for the reaching mode part. The proposed Predictive Sliding Mode Controller is developed for a First-Order-Plus-Deadtime model that represents a good approximation to many processes. The Predictive...
We present an identification method for multivariable linear parameter-varying (LPV) state space systems that is based on a local parameterization of the system and a gradient search in the resulting parameter space. Both the output error and prediction error identification problems are discussed. Because the method involves solving a nonlinear optimization problem, it is of paramount importance to...
The ever-increasing clock speeds of printed circuit board (PCB) have imposed many challenges on the circuit designers, one of which is to pass the electromagnetic compatibility compliance testing for radiated emissions. It is essential to predict PCB radiated emissions prior to a compliance test with the aim to save cost and time. For high-speed PCBs, their traces are electrically long and thus making...
This paper presents an investigative work about the application of State Space Model-Based Predictive Control in a three-phase Permanent Magnet Synchronous Motor with trapezoidal back-electromotive force, for speed control. Such motor is utilized in the white goods appliances industry and also in automotive and medical applications, among others, especially due to its high effiency and long life cycle...
This paper presents a new methodology to assist in the tuning of Multivariable Model Predictive Controllers. It is based on the definition of several performance indexes which are averaged over long intervals using recursive filters. The computational load to obtain the indexes is very low. The indexes analysis can guide the engineer to achieve the control objective. A case study is presented to show...
In the consideration of constraints, repetitive model predictive control is an effective method to track a periodic signal as well as reject a periodic disturbance. However, in practical systems, it is difficult to determine the period of the disturbance; meanwhile, too much information is needed for the design of repetitive controller, thus will make it difficult for the design of controller. In...
Ensemble forecasting is a well-known numerical prediction technique for modeling the evolution of nonlinear dynamic systems. The ensemble member forecasts are generated from multiple runs of a computer model, where each run is obtained by perturbing the starting condition or using a different model representation of the dynamic system. The ensemble mean or median is typically chosen as the consensus...
The widespread adoption of ubiquitous devices does not only facilitate the connection of billions of people, but has also fuelled a culture of sharing rich, high resolution locations through check-ins. Despite the profusion of GPS and WiFi driven location prediction techniques, the sparse and random nature of check-in data generation have ushered diverse problems, which have prompted the prediction...
Response model is one of the most frequently used predictive model. So I make a comprehensive introduction about basic concepts, key functions and main contents of response model and evaluated the model. Through design for utility function and attribute weights to get market value function. In this progress, positive cases and negative cases must be considered. Calculating market value can help enterprises...
Simulation of various manufacturing processes such as heat treatments is rapidly gaining importance in the industry for process optimization, enhancing efficiency and improving product quality. Case carburization followed by quenching is one such significant heat treatment process commonly used in the automotive industry. The equations to be solved for simulation of these processes are non-linear...
Credit rating prediction using clustering algorithms has become more and more important in the financial literature. Expanding the ideas of [4] and [5], we propose an approach to generate models for automated credit rating prediction based on support vector domain description (SVDD) and linear regression (LR). The models include the prediction for sovereign and corporate bonds. Another advantage is,...
In this paper, Health Information Systems (HIS) is presented to discuss factors that enhance physician use of the application. The study adopts the Unified Theory of Acceptance and Use of Technology (UTAUT), which has been widely used in Information Systems (IS) research to study the behaviour of individuals that uses information technology. The theory is then extended with Self Efficacy theory in...
The paper compares Artificial Neural Network (ANN) model against traditional models in the modeling of population and external migration for Fiji population components during the years from 1986 to 2012. The performance of the various models used are based on the values of the various error functions such as the R-squared (R2), Root Square Mean Error (RSME), Mean Absolute Error (MAE), Standard Error...
This paper proposes a method for predicting aircraft flying qualities according to Cooper-Harper rating scale, where neural networks model is used to describe the nonlinear characteristics of pilot control based on experimental data. The relationship between the Cooper-Harper pilot rating and characteristics of a closed-loop aircraft-pilot system is investigated according to the parameters of pilot...
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