DSpace Angular :: Browsing by Author "Cipriano, A."

Browsing by Author "Cipriano, A."

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A new identification method for use in nonlinear prediction
(IOS PRESS, 2001) Montoya, F.; Cipriano, A.; Ramos, M.
This paper presents a new identification method for fuzzy models used in nonlinear prediction. The structure and parameters of the fuzzy model are obtained, using input-output data, by minimization of the prediction error. The predictive capacity of the fuzzy model is compared with other linear and non-linear models analyzing an illustrative example. The results show that the new method presents a better behavior.
Adaptive hybrid predictive control for a combined cycle power plant optimization
(JOHN WILEY & SONS LTD, 2008) Saez, D.; Zunigal, R.; Cipriano, A.
The design and development of an adaptive hybrid predictive controller for the optimization of a real combined cycle power plant (CCPP) are presented. The real plant is modeled as a hybrid system, i.e. logical conditions and dynamic behavior are used in one single modeling framework. Start modes, minimum up/down times and other logical features are represented using mixed integer equations, and dynamic behavior is represented using special linear models: adaptive fuzzy models. This approach allows the tackling of special non-linear characteristics, such as ambient temperature dependence on electrical power production (combined cycle) and gas exhaust temperature (gas turbine) properly to fit into a mixed integer dynamic (MLD) model. After defining the MLD model, an adaptive predictive control strategy is developed in order to economically optimize the operation of a real CCPP of the Central Interconnected System in Chile. The economic results obtained by simulation tests provide a 3% fuel consumption saving compared to conventional strategies at regulatory level. Copyright (c) 2007 John Wiley & Sons, Ltd.
Assessment of expert fuzzy controllers for conventional flotation plants
(1999) Osorio, D.; Perez Correa, Jose Ricardo; Cipriano, A.
Comparison of simple and model predictive control strategies for the holding problem in a metro train system
(INST ENGINEERING TECHNOLOGY-IET, 2010) Grube, P.; Cipriano, A.
This study presents two new strategies for real-time control of a metro (rail transit) system. Both act upon the holding times of trains at stations and attempt to minimise passenger wait times. The first strategy applies heuristic rules and requires very few computational or infrastructure resources. The second strategy is based on predictive models (MPC) and numerical optimisation of an objective function using genetic algorithms, and requires online measurement of state variables. The two strategies are compared to an open-loop control base case that imposes constant holding times. Testing is conducted by a dynamic simulator calibrated with real-world data from the Valparaiso (Chile) metro system. The simulations employ origin-destination matrices and assume finite train capacity and minimum security headways between trains. The results indicate that the simple strategy produces improvements of 32.7% in wait times and 35.5% in travel times compared to the open-loop case. The model predictive control (MPC) strategy reduces wait times by 24.0% and travel times by 5.5% compared to the simple strategy. Given the high costs of MPC infrastructure, the authors conclude that for the situation studied, an economic cost-benefit analysis must be performed before choosing one or the other approach during a real implementation.
Fault detection and isolation in cooperative mobile robots using multilayer architecture and dynamic observers
(CAMBRIDGE UNIV PRESS, 2011) Carrasco, R. A.; Nunez, F.; Cipriano, A.
Mobile robot systems are being used more often in tasks that protect human operators from dangerous environments, but these benefits can be easily lost if the robots spend much of their time being repaired. This implies that any increment in their reliability will also improve their benefits. One way to achieve this is by adding redundant elements to the robot, but this adds complexity and cost to the design. On the other hand, cooperative mobile robots formed by members with the same basic structure provide a natural redundancy of elements, which may be used for reliability improvement. This work presents an architecture that takes advantage of the analytical and sensor redundancy present in groups of cooperative mobile robots in order to increase the reliability of the whole system. First, the design of the architecture is portrayed and the faults to be detected are described. The different layers of the system are then explained and analyzed using several simulations to test their capabilities and limitations. Finally, the experimental results on a group of small mobile robots are shown, validating the results delivered by simulations. These results show that the proposed architecture is able to detect and isolate correctly most of the faults tested.
Forecasting high SO2 concentration levels with fuzzy clustering techniques
(1998) Perez Correa, Jose Ricardo; Letelier, M. V.; Cipriano, A.; Jorquera, H.; Encalada, O.; Solar, I.
One day ahead load forecasting by recurrent neural networks
(C R L PUBLISHING LTD, 1997) Prina, J.; Cipriano, A.; Cardenoso, V.; Alonso, L.; Olmedo, J.C.; Ramos, M.
In recent years, many applications of neural network methodologies to power system problems have been reported. Among them, short term load forecasting has been one of the most popular. Multilayer perceptron networks have constituted the preferred architecture, achieving successful results. However this network model generally fails to deal with the temporal characteristics of the load signal, being more suitable for static pattern recognition tasks. Dynamic or recurrent networks have shown better capabilities for time signals modeling and forecasting. This paper presents the application of a recurrent network model, which uses a very limited amount of data, to the load forecasting problem. Particularly, the Elman, recurrent model was applied to the 24 hour ahead load forecasting for the Chilean Central Interconnected System (SIC). The load values are considered as a time series, taking advantage of the temporal processing capabilities of this neural network model.
Real-time hybrid predictive modeling of the Teniente Converter
(ELSEVIER SCI LTD, 2010) Schaaf, M.; Gomez, Z.; Cipriano, A.
The Teniente Converter (TC) is air important technology for smelting and converting copper concentrates. Due to the complexities of its autogenous Operation, which combines continuous input flows with intermittent product extraction, efforts to model and control this dynamic have only beer) partially successful. This work presents a model of TC operation that combines phenomenological equations with empirical expressions and includes both continuous and discrete variables. Six different operating modes are described. To improve predictive capabilities, an adaptive compensation technique takes account of changes in concentrate mix characteristics. The model predicts five important variables: white metal copper concentration, slag magnetite concentration, white metal temperature, and white metal and slag levels. It is calibrated and validated with real operating data from Codelco's Potrerillos smelter. Once the model is implemented, it delivers online estimates of process variables that are Measured intermittently.
Robust H-infinity filter design for singular systems with time-varying uncertainties
(INST ENGINEERING TECHNOLOGY-IET, 2011) Barbosa, K. A.; Cipriano, A.
This article deals with the design of robust H-infinity filter for singular systems in general form, subject to bounded time-varying uncertainties. The aim is to design linear admissible descriptor filters that ensure a guaranteed bound on the L-2 gain of the operator from the noise signals to the estimation error signal, irrespective of the time-varying uncertain parameters. A strict linear matrix inequality based method is proposed to the robust H-infinity filter design employing affine Lyapunov functions. A numerical example is presented to show the efficiency of the proposed method.
Short-term forecasting of electricity prices in the Colombian electricity market
(INST ENGINEERING TECHNOLOGY-IET, 2009) Lira, F.; Munoz, C.; Nunez, F.; Cipriano, A.
The restructuring of the electricity-generating industry from protected monopoly to an open competitive market has presented producers with a problem scheduling generation: finding the optimal bidding strategy to maximise their profits. In order to solve this scheduling problem, a reliable system capable of forecasting electricity prices is needed. This work evaluates the forecasting capabilities of several modelling techniques for the next-day-prices forecasting problem in the Colombian market, measured in USD/MWh. The models include exogenous variables such as reservoir levels and load demand. Results show that a segmentation of the prices into three intervals, based on load demand behaviour, contribute to an important standard deviation reduction. Regarding the models under analysis, Takagi-Sugeno-Kang models and ARMAX models identified by means of a Kalman filter perform the best forecasting, with an error rate below 6%.

Browsing by Author "Cipriano, A."

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