This paper presents a scheme for designing a robust decentralized PI controller for an industrial utility boiler system. First, a new method for designing robust decentralized PI controllers for uncertain LTI MIMO sys...
This paper presents a scheme for designing a robust decentralized PI controller for an industrial utility boiler system. First, a new method for designing robust decentralized PI controllers for uncertain LTI MIMO systems is presented. Sufficient conditions for closed-loop stability and diagonal dominance of a multivariable system are given. For each isolated subsystem a first order approximation is obtained. Then, achieving robust stability and closedloop diagonal dominance is formulated as local robust performance problems. It is shown by selecting time constants of the closed-loop isolated subsystems appropriately, these local robust performance problems are solved and the interactions between closed-loop stabilized subsystems are attenuated. The internal model control (IMC) method is used to design local PI controllers. The suggested design strategy is applicable to unstable systems as well. Thereafter, the nonlinear model of an industrial utility boiler is linearized about its operating points and the nonlinearity is modeled as uncertainty for a nominal LTI MIMO system. Using the new proposed method, a decentralized PI controller for the uncertain LTI nominal model is designed. The designed controller is applied to the real system. The simulation results show the effectiveness of the proposed methodology.
This paper presents the results of the parameter estimation procedure for the primary circuit dynamics of a VVER-type nuclear power plant. The model structure is a low dimensional lumped nonlinear model published prev...
This paper presents the results of the parameter estimation procedure for the primary circuit dynamics of a VVER-type nuclear power plant. The model structure is a low dimensional lumped nonlinear model published previously in Fazekas et al. [2007a]. The parameter estimation method uses the modular decomposition of the system model for obtaining physically meaningful initial parameter estimates. The final parameter estimates are computed using the integrated model.
Predictive control algorithms compute the manipulated variable minimizing a cost function considering expected future errors. PI control algorithms can be equipped with predictive properties. Simple predictive control...
Predictive control algorithms compute the manipulated variable minimizing a cost function considering expected future errors. PI control algorithms can be equipped with predictive properties. Simple predictive control algorithms are derived using approximation of an aperiodic process by a first-order model with dead time. Applying a noise model the robustness properties of the algorithm are enhanced considering plant-model mismatch. The noise filter is considered as a design parameter. Simulation examples demonstrate the behavior of the predictive PI algorithm and the robustifying effect of the noise filter.
The paper deals with problem of estimating input channel delay in nonlinear system with a model-free approach. The proposed method is based on Lipschitz theory. It is an extension to the Lipschitz method which was pro...
The paper deals with problem of estimating input channel delay in nonlinear system with a model-free approach. The proposed method is based on Lipschitz theory. It is an extension to the Lipschitz method which was proposed for determining the order of a model. Our algorithm consists of two parts which in the first one estimation is made on the proper number of dynamics on the input and in the second part the pure delay of the input is obtained. The method is applied for estimation of the delay of two different models and the estimation was as accurate as possible.
Brain emotional learning based intelligent controller (BELBIC) is based on computational model of limbic system in the mammalian brain. In recent years, this model was applied in many linear and nonlinear control appl...
Brain emotional learning based intelligent controller (BELBIC) is based on computational model of limbic system in the mammalian brain. In recent years, this model was applied in many linear and nonlinear control applications. Previous studies show that this controller has fast response, simple implementation and robustness with respect to disturbances. It is also possible to define emotional signal based on control application objectives. But in the previous studies, internal instability of this controller was not considered and control task were done in limited time period. In this article mathematical description of BELBIC is investigated and improved to avoid internal instability. Simulation and implementation of improved model was done on level plant. The obtained results showed that instability of model has been solved in the new model without loss of performance by using Integral Anti Windup (IAW).
Tracking moving objects in variable cluttered environments is an active area of research. It is common to use some simplifying assumption in such environments to facilitate the design. In this paper a new method for s...
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Tracking moving objects in variable cluttered environments is an active area of research. It is common to use some simplifying assumption in such environments to facilitate the design. In this paper a new method for simulating the completely non-Gaussian cluttered environments is presented. The method is based on using the variable variance of process noise as a description of variability in such environments. The key objective is to find an effective algorithm for tracking a single moving object in variable cluttered environments, with utilization of the presented method. The new methodology is presented in two steps. In the first step we compare the accuracy of estimators in tracking a moving object, and in the second step, the goal is to find the best algorithm for tracking a single moving target in variable cluttered environments.
Considering the need of an advancedprocesscontrol in cement industry, this paper presents an adaptive model predictive algorithm to control a white cement rotary kiln. As any other burning process, the control scena...
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Considering the need of an advancedprocesscontrol in cement industry, this paper presents an adaptive model predictive algorithm to control a white cement rotary kiln. As any other burning process, the control scenario is to expect the controller to regulate the temperature and the period of baking a fixed quantity of raw material as desired, as well as to have the concentration of the combustion gases under control. To achieve these goals, this work presents a strategy which includes multivariable online identification of the kiln process and a constrained generalized predictive controller. An MLP neural network model derived from real plant data of Saveh cement factory in Iran is used as the kiln process simulator. The control efforts are made taken into account the operating constraints. At last the proposed control strategy is modified so as to gain good disturbance rejection ability.
In this paper, we use system identification methods for abnormal condition detection of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. A novel approa...
In this paper, we use system identification methods for abnormal condition detection of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. A novel approach is used in order to estimate the delays of the input channel of the kiln. By means of that, the identification task gets easier and the results are more accurate. To identify the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Finally, a model for the healthy mode of the kiln is obtained through which it is possible to detect abnormal conditions in the process. We distinguished two common abnormal conditions in kiln and another one which was not characteristically known for cement experts as well.
In this paper, we design a neurofuzzy controller to control several variables of a rotary cement kilns. The variables are back-end temperature, pre-heater temperature, oxygen content and CO2 gas content of the kiln. T...
In this paper, we design a neurofuzzy controller to control several variables of a rotary cement kilns. The variables are back-end temperature, pre-heater temperature, oxygen content and CO2 gas content of the kiln. The fuzzy control system, as an advancedcontrol option for the kilns, is intended to minimize the operator interaction in the controlprocess. The proposed fuzzy controller uses a neural network to optimize TSK-type fuzzy controller. Since there is no generally applicable analytical model for cement kilns, we use the real data derived from Saveh cement factory for the plant identification. A model, which is very similar to the real plant, is identified then; and the identified model is used for control design and simulations. Extensive simulation studies justify the effectiveness and applicability of the proposed control scheme in intelligent control of cement plant.
作者:
Millard, William B.The new 8th edition of the Advanced Trauma Life Support (ATLS) course manual contains a small but significant change. The phrase
“trauma is a surgical disease” long a point of contention with other specialties caring for trauma patients has been removed.
Now used in over 50 countries as the basis for training in the initial assessment and management of trauma this publication reflects the research and clinical experience of the American College of Surgeons (ACS) Committee on Trauma and expresses that organization's philosophy toward triage diagnosis and clinical care. Astute readers of the ATLS materials have noticed that a certain message is conspicuous by its absence. The preface to the 7th edition of the ATLS describes the ACS's role as follows:
In accordance with that role and recognizing that trauma is a surgical disease the ACS Committee on Trauma (COT) has worked to establish guidelines for the care of the trauma patient.
The 8th edition includes a substantially similar sentence minus the crucial phrase on trauma as a “surgical disease.” John B. Kortbeek MD FACS professor of surgery and critical care at the University of Alberta and a member of the COT who was instrumental in the revision process for the manual confirms that the deletion is intentional.
Dr. Kortbeek explains the change in historical terms. “The intent of making that statement” he says “was to emphasize that to have a successful trauma system and a successful trauma hospital surgeons needed to be included in the management team and the care of the trauma patient. That remains true today. What changed over time is that that statement became a focal point and could be interpreted in varying ways including in a negative exclusive way suggesting that only surgeons should be managing trauma patients which is not correct and never was the intent of the statement.” The ATLS he says presents a “common language” for a safe and effective response to trauma not a mandatory formula.
Harmonious relations among the various spe
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