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Artigos de revistas sobre o assunto "Reduced-Order state estimator"
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Debnath, Sarupa, Soumya Ranjan Sahoo, Bernard Twum Agyeman e Jinfeng Liu. "Input-Output Selection for LSTM-Based Reduced-Order State Estimator Design". Mathematics 11, n.º 2 (12 de janeiro de 2023): 400. http://dx.doi.org/10.3390/math11020400.
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In this work, we propose a sensitivity-based approach to construct reduced-order state estimators based on recurrent neural networks (RNN). It is assumed that a mechanistic model is available but is too computationally complex for estimator design and that only some target outputs are of interest and should be estimated. A reduced-order estimator that can estimate the target outputs is sufficient to address such a problem. We introduce an approach based on sensitivity analysis to determine how to select the appropriate inputs and outputs for data collection and data-driven model development to estimate the desired outputs accurately. Specifically, we consider the long short-term memory (LSTM) neural network, a type of RNN, as the tool to train the data-driven model. Based on it, an extended Kalman filter, a state estimator, is designed to estimate the target outputs. Simulations are carried out to illustrate the effectiveness and applicability of the proposed approach.
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Debnath, Sarupa, Soumya R. Sahoo, Bernard T. Agyeman e Jinfeng Liu. "Input-output selection for LSTM-based reduced-order state estimator design". IFAC-PapersOnLine 56, n.º 2 (2023): 6940–45. http://dx.doi.org/10.1016/j.ifacol.2023.10.512.
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Singalandapuram Mahadevan, Boopathi, John H. Johnson e Mahdi Shahbakhti. "Development of a Kalman filter estimator for simulation and control of particulate matter distribution of a diesel catalyzed particulate filter". International Journal of Engine Research 21, n.º 5 (17 de julho de 2018): 866–84. http://dx.doi.org/10.1177/1468087418785855.
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The knowledge of the temperature and particulate matter mass distribution is essential for monitoring the performance and durability of a catalyzed particulate filter. A catalyzed particulate filter model was developed, and it showed capability to accurately predict temperature and particulate matter mass distribution and pressure drop across the catalyzed particulate filter. However, the high-fidelity model is computationally demanding. Therefore, a reduced order multi-zone particulate filter model was developed to reduce computational complexity with an acceptable level of accuracy. In order to develop a reduced order model, a parametric study was carried out to determine the number of zones necessary for aftertreatment control applications. The catalyzed particulate filter model was further reduced by carrying out a sensitivity study of the selected model assumptions. The reduced order multi-zone particulate filter model with 5 × 5 zones was selected to develop a catalyzed particulate filter state estimator considering its computational time and accuracy. Next, a Kalman filter–based catalyzed particulate filter estimator was developed to estimate unknown states of the catalyzed particulate filter such as temperature and particulate matter mass distribution and pressure drop (Δ P) using the sensor inputs to the engine electronic control unit and the reduced order multi-zone particulate filter model. A diesel oxidation catalyst estimator was also integrated with the catalyzed particulate filter estimator in order to provide estimates of diesel oxidation catalyst outlet concentrations of NO2 and hydrocarbons and inlet temperature for the catalyzed particulate filter estimator. The combined diesel oxidation catalyst–catalyzed particulate filter estimator was validated for an active regeneration experiment. The validation results for catalyzed particulate filter temperature distribution showed that the root mean square temperature error by using the diesel oxidation catalyst–catalyzed particulate filter estimator is within 3.2 °C compared to the experimental data. Similarly, the Δ P estimator closely simulated the measured total Δ P and the estimated cake pressure drop error is within 0.2 kPa compared to the high-fidelity catalyzed particulate filter model.
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Li, Yunji, Wenzhuo Zhou e Yajun Wu. "Event-Triggered Fault Estimation and Fault Tolerance for Cyber-Physical Systems with False Data Injection Attacks". Actuators 12, n.º 5 (10 de maio de 2023): 197. http://dx.doi.org/10.3390/act12050197.
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This paper investigates an event-triggered framework for addressing fault estimation and fault tolerance issues in discrete-time cyber-physical systems (CPSs) with partial state saturations and random false data injection attacks (FDIAs). A stochastic variable is introduced to characterize the random FDIAs and to establish the corresponding model. A reduced-order fault estimator and an event condition are co-derived to reconstruct system states and actuator faults. The proposed event-triggered transmission scheme helps reduce network utilization in the sensor-to-estimator channel. A sufficient condition for the proposed event-triggered estimator is derived, which minimizes state and fault estimation errors even when the controlled plants are subject to exogenous disturbances, fault signals, and random attacks. Furthermore, a fault-tolerant compensation controller is proposed using the estimated states and faults, ensuring that the considered systems achieve mean-squared stability. Finally, a DC motor platform is developed to further demonstrate the effectiveness of the designed estimator-based fault-tolerant controller.
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Nguyen Van, Chi, e Thuy Nguyen Vinh. "Soc Estimation of the Lithium-Ion Battery Pack using a Sigma Point Kalman Filter Based on a Cell’s Second Order Dynamic Model". Applied Sciences 10, n.º 5 (10 de março de 2020): 1896. http://dx.doi.org/10.3390/app10051896.
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This paper deals with the state of charge (SoC) estimation of a lithium-ion battery pack (LiBP) connected by some cells in series and parallel. The voltage noise, noise and current bias of current through the LiBP are taken into account in the SoC estimation problem. In order to describe the cell dynamic more accurately, especially for practical applications with charge and discharge amplitude varying suddenly, in this paper we use the second dynamic order model of the cell to estimate the SoC of the LiBP. By applying the sigma point Kalman filter (SPKF), the average SoC of the pack and bias current of current measurement are estimated by first estimator; the second estimator estimates the SoC differences of the cell modules from average SoC of the pack. The SoC of the cell modules are the sum of average SoCs of the pack and the SoC differences. By only using two estimators, the calculation complexity for SoC estimation is more reduced; this is very useful for the LiBP, which has the number of cells connected in a large series. This method was applied for the pack of SAMSUNG ICR18650-22P connected by seven cell modules; the cell modules were connected by nine cells in parallel; the LiBP was charged and discharged with amplitude varying suddenly. The estimated SoC of seven cell modules is smaller than 2% for a temperature operating range typically −5 °C to 45 °C. The comparison of the accuracy of SoC estimation based on the first and the second order dynamic models is made; the result shows that the SoC estimation used the second order dynamic model is more exact.
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Pécute;Rez-Lozano, Rigoberto, e Rogelio Soto. "From Pole Placement Feedback to Estimator Design Using Analog Computers". International Journal of Electrical Engineering & Education 30, n.º 4 (outubro de 1993): 317–28. http://dx.doi.org/10.1177/002072099303000405.
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From pole placement feedback to estimator design using analog computers This paper describes analog-based control lab. simulations. Simulations deal with pole placement feedback and asymptotic observers. For asymptotic state observers, the transfer function approach is just outlined. All simulations are implemented using the state variable approach. Simulations are extended to analyze heuristic pole-placement selection and to evaluate reduced-order estimators.
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AHUJA, S., e C. W. ROWLEY. "Feedback control of unstable steady states of flow past a flat plate using reduced-order estimators". Journal of Fluid Mechanics 645 (22 de fevereiro de 2010): 447–78. http://dx.doi.org/10.1017/s0022112009992655.
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We present an estimator-based control design procedure for flow control, using reduced-order models of the governing equations linearized about a possibly unstable steady state. The reduced-order models are obtained using an approximate balanced truncation method that retains the most controllable and observable modes of the system. The original method is valid only for stable linear systems, and in this paper, we present an extension to unstable linear systems. The dynamics on the unstable subspace are represented by projecting the original equations onto the global unstable eigenmodes, assumed to be small in number. A snapshot-based algorithm is developed, using approximate balanced truncation, for obtaining a reduced-order model of the dynamics on the stable subspace.The proposed algorithm is used to study feedback control of two-dimensional flow over a flat plate at a low Reynolds number and at large angles of attack, where the natural flow is vortex shedding, though there also exists an unstable steady state. For control design, we derive reduced-order models valid in the neighbourhood of this unstable steady state. The actuation is modelled as a localized body force near the trailing edge of the flat plate, and the sensors are two velocity measurements in the near wake of the plate. A reduced-order Kalman filter is developed based on these models and is shown to accurately reconstruct the flow field from the sensor measurements, and the resulting estimator-based control is shown to stabilize the unstable steady state. For small perturbations of the steady state, the model accurately predicts the response of the full simulation. Furthermore, the resulting controller is even able to suppress the stable periodic vortex shedding, where the nonlinear effects are strong, thus implying a large domain of attraction of the stabilized steady state.
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Zaini, Zaini, Dwi Mutiara Harfina e Agung P. Iswar. "Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi". Andalas Journal of Electrical and Electronic Engineering Technology 1, n.º 2 (10 de dezembro de 2021): 48–57. http://dx.doi.org/10.25077/ajeeet.v1i2.12.
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Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.
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Zaini, Zaini, Dwi Mutiara Harfina e Agung P. Iswar. "Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi". Andalas Journal of Electrical and Electronic Engineering Technology 1, n.º 02 (10 de dezembro de 2021): 48–57. http://dx.doi.org/10.25077/ajeeet.v1i02.12.
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Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.
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Suppan, Thomas, Markus Neumayer, Thomas Bretterklieber e Stefan Puttinger. "Prior design for tomographic volume fraction estimation in pneumatic conveying systems from capacitive data". Transactions of the Institute of Measurement and Control 42, n.º 4 (18 de novembro de 2019): 716–28. http://dx.doi.org/10.1177/0142331219884808.
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Pneumatic conveying systems have become a standard technique for the transport of bulk materials such as powdery or granulates. The spatial dependence of the material density and the stream velocity in such transport systems require a volumetric measurement principle for flow measurement. In this paper we analyse the capability to estimate the volume fraction from capacitive sensing data using electrical capacitance tomography (ECT). In particular, we investigate the capability of back-projection type imaging algorithms. The ill-posed nature of the imaging problem of ECT require the incorporation of prior knowledge in the design of the estimator. We analyse the different flow profiles in pneumatic conveying in order to generate specific sample-based prior information to improve the estimation performance and robustness. We discuss the construction of different linear image reconstruction algorithms and present a framework, which allows a detailed statistical analysis of the estimator performance. Simulation studies show the estimation behaviour of different algorithms with respect to the incorporated prior information. We demonstrate, that the incorporation of specific prior knowledge leads to an improved estimator behaviour; for example, reduced variance and unbiased estimates. We implemented laboratory experiments in order to analyse the presented approach for the application in real pneumatic conveying processes. We demonstrate the improved robust estimation behaviour by means of comparative reconstruction results obtained with different algorithms and priors. Furthermore, the uncertainty of the estimated volume fraction is analysed in steady state conveying processes. Hereby, it is demonstrated, that appropriate prior information improves the estimation performance also for measurements coming from real pneumatic conveying processes, making ECT a suitable tool for the volume fraction estimation in such transport systems.
Teses / dissertações sobre o assunto "Reduced-Order state estimator"
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Zhang, Yuqing. "Fixed-time algebraic distributed state estimation for linear systems". Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2025. http://www.theses.fr/2025ISAB0001.
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Au cours des dernières décennies, le déploiement massif de capteurs embarqués en réseau dotés des capacités de communication dans des systèmes à grande échelle a suscité un intérêt croissant de la part des chercheurs dans le domaine de l’estimation distribuée. Cette thèse vise à développer une méthode d’estimation d’état distribuée algébrique à temps fixe pour les systèmes linéaires à temps variant d’ordre entier et les systèmes linéaires à temps invariant d’ordre fractionnaire dans des environnements bruités, en concevant un ensemble d’estimateurs locaux d’ordre réduit au niveau des capteurs en réseau.Pour ce faire, nous introduisons d’abord un schéma d’estimation distribuée en définissant un ensemble denoeuds récupérés à chaque noeud de capteur, basé sur un graphe dirigé plus relâché que celui qui est fortement connecté. En utilisant cet ensemble récupéré, nous construisons une transformation inversible pour la décomposition d’observabilité afin d’identifier le sous-système local observable de chaque noeud. De plus, cette transformation permet une représentation distribuée de l’état entier du système à chaque noeud sous forme de combinaison linéaire de son propre état local observable et de ceux des noeuds de son ensemble récupéré. Cela garantit que chaque noeud peut atteindre l’estimation distribuée d’état, à condition que les estimations des états locaux observables soient assurées. En conséquence, ce schéma distribué se concentre sur l’estimation des états locaux observables, permettant une estimation distribuée à travers le réseau de capteurs.En nous appuyant sur cette base, afin de traiter l’estimation algébrique à temps fixe pour chaque sous-système local observable identifié, différentes méthodes d’estimation à fonctions modulatrices sont explorées pour établir des formules algébriques indépendantes des conditions initiales, les rendant efficaces en tant qu’estimateurs locaux de ordre réduit à temps fixe. Pour les systèmes linéaires à temps variant d’ordre entier, la transformation utilisée pour développer le schéma d’estimation distribuée aboutit à une forme normale linéaire partiellement observable à temps variant. La méthode des fonctions modulatrices généralisées est ensuite appliquée pour estimer chaque état local observable à travers des formules intégrales algébriques des sorties du système et de leurs dérivées. Pour les systèmes linéaires à temps invariant d’ordre fractionnaire, une autre transformation est utilisée pour convertir chaque sous-système local observable identifié sous une forme normale observable d’ordre fractionnaire, permettant l’application de la méthode d’estimation à fonctions modulatrices généralisées d’ordre fractionnaire. Cette méthode calcule directement des formules intégrales algébriques pour les variables pseudo-état locales observables.Ensuite, en combinant ces formules algébriques avec la représentation distribuée dérivée, nous réalisons l’estimation d’état distribuée algébrique à temps fixe pour les systèmes étudiés. De plus, une analyse d’erreur est réalisée pour démontrer la robustesse de l’estimateur distribué conçu en présence de bruits continus de processus et de mesure, ainsi que de bruits discrets de mesure. Enfin, plusieurs exemples de simulation sont fournis pour valider l’efficacité du schéma d’estimation distribuée proposéIn recent decades, the widespread deployment of networked embedded sensors with communication capabilities in large-scale systems has drawn significant attentions fromresearchers to the field of distributed estimation. This thesis aims to develop a fixed-time algebraic distributed state estimation method for both integer-order linear time-varying systems and fractional-order linear-invariant systems in noisy environments, by designing a set of reduced-order local estimators at the networked sensors.To achieve this, we first introduce a distributed estimation scheme by defining a recovered node set at each sensor node, based on a digraph assumption that is more relaxed than the strongly connected one. Using this recovered set, we construct an invertible transformation for the observability decomposition to identify each node’s local observable subsystem. Additionally, this transformation allows for a distributed representation of the entire system state at each node by a linear combination of its own local observable state and those of the nodes in its recovered set. This ensures that each node can achieve the distributed state estimation, provided that the estimations for the set of local observable states are ensured. As a result, this distributed scheme focuses on estimating the local observable states, enabling distributed estimation across the sensor network.Building on this foundation, to address the fixed-time algebraic state estimation for each identified local observable subsystem, different modulating functions estimation methods are investigated to derive the initial-condition-independent algebraic formulas, making them effective as reduced-order local fixed-time estimators. For integer-order linear time-varying systems, the transformation used in developing distributed estimation scheme yields a linear time-varying partial observable normal form. The generalized modulating functions method is then applied to estimate each local observable state through algebraic integral formulas of system outputs and their derivatives. For fractional-order linear-invariant systems, another transformation is used to convert each identified local observable subsystem into a fractional-order observable normal form, allowing for the application of the fractional-order generalized modulating functions estimation method. This method directly computes algebraic integral formulas for local observable pseudo-state variables.Subsequently, by combining these algebraic formulas with the derived distributed representation, we achieve the fixed-time algebraic distributed state estimation for the studied systems. Additionally, an error analysis is conducted to demonstrate the robustness of the designed distributed estimator in the presence of both continuous process and measurement noises, as well as discrete measurement noises. Finally, several simulation examples are provided to validate the effectiveness of the proposed distributed estimation scheme
Livros sobre o assunto "Reduced-Order state estimator"
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G, Kalit, e Ames Research Center, eds. Mean-square error bounds for reduced-order linear state estimators. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1987.
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G, Kalit, e Ames Research Center, eds. Mean-square error bounds for reduced-order linear state estimators. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1987.
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Baram, Yoram. Mean-square error bounds for reduced-order linear state estimators. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1987.
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Chen, Nan. Stochastic Methods for Modeling and Predicting Complex Dynamical Systems: Uncertainty Quantification, State Estimation, and Reduced-Order Models. Springer International Publishing AG, 2023.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Reduced-Order state estimator"
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Gershon, Eli, e Uri Shaked. "Reduced-Order H ∞ Output-Feedback Control". In Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems, 61–74. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5070-1_3.
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Mayfield, Albert E., Steven J. Seybold, Wendell R. Haag, M. Tracy Johnson, Becky K. Kerns, John C. Kilgo, Daniel J. Larkin et al. "Impacts of Invasive Species in Terrestrial and Aquatic Systems in the United States". In Invasive Species in Forests and Rangelands of the United States, 5–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45367-1_2.
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AbstractThe introduction, establishment, and spread of invasive species in terrestrial and aquatic environments is widely recognized as one of the most serious threats to the health, sustainability, and productivity of native ecosystems (Holmes et al. 2009; Mack et al. 2000; Pyšek et al. 2012; USDA Forest Service 2013). In the United States, invasive species are the second leading cause of native species endangerment and extinction, and their costs to society have been estimated at $120 billion annually (Crowl et al. 2008; Pimentel et al. 2000, 2005). These costs include lost production and revenue from agricultural and forest products, compromised use of waterways and terrestrial habitats, harm to human and animal health, reduced property values and recreational opportunities, and diverse costs associated with managing (e.g., monitoring, preventing, controlling, and regulating) invasive species (Aukema et al. 2011; Pimentel et al. 2005). The national significance of these economic, ecological, and social impacts in the United States has prompted various actions by both legislative and executive branches of the Federal Government (e.g., the Nonindigenous Aquatic Nuisance Prevention and Control Act of 1990; the Noxious Weed Control and Eradication Act of 2002; Executive Order 13112 of 1999, amended in 2016).
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Ulin-Avila, Erick, e Juan Ponce-Hernandez. "Kalman Filter Estimation and Its Implementation". In Adaptive Filtering - Recent Advances and Practical Implementation [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97406.
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In this chapter, we use the Kalman filter to estimate the future state of a system. We present the theory, design, simulation, and implementation of the Kalman filter. We use as a case example the estimation of temperature using a Resistance Temperature Detector (RTD), which has not been reported before. After a brief literature review, the theoretical analysis of a Kalman filter is presented along with that of the RTD. The dynamics of the RTD system are analytically derived and identified using Matlab. Then, the design of a time-varying Kalman filter using Matlab is presented. The solution to the Riccati equation is used to estimate the future state. Then, we implement the design using C-code for a microprocessor ATMega328. We show under what conditions the system may be simplified. In our case, we reduced the order of the system to that of a system having a 1st order response, that of an RC system, giving us satisfactory results. Furthermore, we can find two first order systems whose response defines two boundaries inside which the evolution of a second order system remains.
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Hoang, H. S., P. De Mey, O. Talagrand e R. Baraille. "A NEW REDUCED-ORDER ADAPTIVE FILTER FOR STATE ESTIMATION IN HIGH DIMENSIONAL SYSTEMS". In Adaptive Systems in Control and Signal Processing 1995, 155–60. Elsevier, 1995. http://dx.doi.org/10.1016/b978-0-08-042375-3.50025-1.
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Gonçalves, Guilherme A. A., Argimiro R. Secchi e Evaristo C. Biscaia. "Fast Nonlinear Predictive Control and State Estimation of Distillation Columns Using First-Principles Reduced-order Model". In Computer Aided Chemical Engineering, 715–20. Elsevier, 2014. http://dx.doi.org/10.1016/b978-0-444-63456-6.50120-4.
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Muraca, Pietro, e Ciro Picardi. "A REDUCED ORDER EXTENDED KALMAN FILTER ALGORITHM FOR PARAMETER AND STATE ESTIMATION OF AN INDUCTION MOTOR". In Algorithms and Architectures for Real-Time Control 1992, 225–30. Elsevier, 1992. http://dx.doi.org/10.1016/b978-0-08-042050-9.50041-5.
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Galdi, Michael, e Paporn Thebpanya. "Optimizing School Bus Stop Placement in Howard County, Maryland". In Geospatial Research, 1660–76. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9845-1.ch079.
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In the current system, school bus stops in Howard County, Maryland are manually placed along the school bus routes based on safety, cost-efficiency, and many other variables. With such liberal placement, bus stops are sometimes placed unnecessarily. This issue is prevalent in many school districts and often results in needlessly close bus stop proximity. In this study, the authors implemented a GIS-based heuristic to assist school officials in optimizing their districts bus stop placement. They also estimated the proportion of county-wide bus stops that could be eliminated by this approach. Following the constraints determined by State and local guidelines, the ArcGIS Network Analyst Extension was used to identify unnecessary bus stops across the study area. The initial output was re-evaluated by school officials in order to determine if those bus stops would be eliminated. The results indicate that approximately 30% of the existing bus stops were marked as “candidates for elimination” by the GIS process. After a review of these candidates, it was determined that at least 15% of the total school bus stops could be eliminated. Statistical estimates lent credence to the benefit of a re-evaluation of these bus stops. The method developed in this study can easily be replicated. Hence, it may inspire other school systems to exercise the same approach. Additionally, the results provide a gateway for future studies in examining more efficient school bus routes with less travel time, as well as investigating how much the carbon footprint of school bus fleets can be reduced.
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Xiang, Jundong. "Research on Active Equalization System of Power Battery". In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221222.
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A hierarchical active equalization topology based on Cuk converter and flyback converter was proposed to solve the problem of the inconsistency of series battery packs used in electric vehicle power cells. Using state of charge (SOC) as equalization standard, two equalization control strategies were developed, the second-order equivalent circuit model of lithium ion battery and open circuit voltage (OCV) characteristic curve were established to identify parameters and estimate SOC. The equalization of single battery in series battery was realized, and the inconsistency of series battery was reduced. Finally, a balanced experimental platform was built for verification. The layered active equalization framework gives full play to the advantages of the two kinds of active equalization, and improves the problem of the inconsistencies of the series battery. According to the SOC distribution of all the single cells in the series battery, the balancing control strategy is selected, which can effectively shorten the balancing time.
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Zakeralhoseini, Sajjad, e Jürg Schiffmann. "SMALL-SCALE TURBOPUMPS FOR WASTE HEAT RECOVERY APPLICATIONS BASED ON AN ORGANIC RANKINE CYCLE, MODELING, ANALYTICAL AND EXPERIMENTAL INVESTIGATIONS." In Proceedings of the 7th International Seminar on ORC Power System (ORC 2023), 655–64. 2024a ed. Editorial Universidad de Sevilla, 2024. http://dx.doi.org/10.12795/9788447227457_113.
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The organic Rankine cycle (ORC) system is considered a promising technology to exploit thermodynamic potential of waste heat. The weight and size of standard pumps can penalize the benefits of installing an ORC system on vehicles. Small-scale ORC applications would greatly benefit from more compact and efficient pumps. This paper presents the results of numerical and experimental analyses of small-scale turbopumps for ORC systems. A parameterized design tool is developed, allowing the rapid generation of numerous turbopump geometries and their fluid domains. The design tool creates a dataset of numerous turbopumps with different geometrical parameters. The turbopumps are investigated with CFD analysis, and the accomplished results are analyzed to characterize the influence of tip clearance and splitter blades on the performance (slip factor and head rise) of small-scale ORC turbopumps. The numerical results are employed to infer dimensionless maps (specific speed-specific diameter) and 1D models, which enable the capturing of the influence of down-scaling in the early design process of such machines. In the next step, two turbopumps are designed using the new design tool and then tested experimentally to validate the numerical procedure. The good agreement between experimental performance characteristics and developed models validates the computational results and reduced-order models. Following the experimental validation, the performance of an ORC system designed for the waste heat recovery of truck engines is estimated using the performance characteristics of the experimental turbopumps instead of a commercial multi-stage centrifugal pump. The developed turbopumps are one order of magnitude more compact compared to commercial systems. Further, the comparison suggests that the designed turbopumps improve the targeted ORC’s thermal efficiency by 0.51% and reduce its back-work ratio by 42% and 67%. Keywords: small-scale turbopump, organic Rankine cycle, computational fluid dynamics, experimental investigation, pre-design diagrams, 1D models
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Burlaka, Serhiy, e Tetiana Yemchik. "IMPROVING THE EFFICIENCY OF THE USE OF BIODIESEL FUEL MIXTURES IN THE SYSTEMS OF AUTONOMOUS ENERGY SUPPLY OF AGRICULTURAL ENTERPRISES". In Modernization of research area: national prospects and European practices. Publishing House “Baltija Publishing”, 2022. http://dx.doi.org/10.30525/978-9934-26-221-0-9.
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The research is devoted to solving important tasks of interdisciplinary research work on the topic: «Development of scientific and technical support for energy autonomy of the agro-industrial complex based on environmentally efficient use of agrobiomass for biofuel production», state registration number 0122U000844, implementation of which is planned for 2022–2024 at the expense of the state budget of Ukraine.The use of fossil fuels leads to environmental pollution and climate change in general. To ensure energy security and to improve the environmental conditions necessary for the use of alternative energy sources and to determine their potential impact on the climate and our planet. Looking for more in-depth analysis of all aspects of the use of biofuels in order to determine their potential through the use and economic components.During studies carried out comparing the performance of different fuels. It has been found that the fuel obtained from biomass is advantageous in environmental protection performance in the production process, is absorbed when the biomass in the growth process and the carbon dioxide in operation.Analyzing the following requirements fuel when executed estimate (European) emission standards (CO, ShNu, NO X) for the test cycle, reduced CO2 emissions, the minimum consumption of natural resources and energy, and minimal impact on the environment in its life cycle. There are theoretical ways to increase the effective capacity of machine units and to determine the impact of this increase on the technical, economic and environmental efficiency of a diesel internal combustion engine. It has been found that the use of biofuels from plant oils can reduce the use of fossil fuels and environmental improvements. However, when using a fuel, wherein the content of the organic component exceeds 50%, i.e. deterioration of technical and economic parameters of the diesel engine. Among the mixed fuel we studied the best technical and economic characteristics of a mixture containing 70% of mineral fuels and biofuels 30% rapeseed oil. Thus, the subject to reach a dosage adjustment of the change of the fuel mixture, depending on the speed of rotation of the crankshaft of the engine the best environmental performance.
Trabalhos de conferências sobre o assunto "Reduced-Order state estimator"
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Sato, Hinata, e Naohisa Otsuka. "SEIQRS Epidemic Model and its State Estimation using Interval Reduced-Order Positive Observer". In 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/iciea61579.2024.10664873.
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Thapa Magar, Kaman S., Mark J. Balas e Susan A. Frost. "Adaptive Disturbance Tracking Control With Wind Speed Reduced Order State Estimation for Region II Control of Large Wind Turbines". In ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/smasis2012-7944.
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In this paper we introduce an Adaptive Disturbance Tracking Control (ADTC) Theory and make some modifications to implement it to address Region II control problem of large wind turbines. Since ADTC requires measurement of wind speed, a wind speed and partial state estimator based on linearized lower-order model of wind turbine at Region II operating point was developed. The estimated wind speed was then used with the adaptive controller and the states were used for state feedback. The combination of partial state feedback and adaptive disturbance tracking control is implemented in National Renewable Energy Laboratory (NREL)’s 5 MW offshore wind turbine model and simulated in MATLAB/Simulink. The simulation result was then compared with existing fixed gain controller.
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Zhang, Jianwu, e Defeng Xu. "Hierarchical Estimator of Dual Clutch Torques for a Power-Split Hybrid Electric Vehicle". In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-8927.
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Abstract For fast drive mode transitions by shifting clutches equipped in the dedicated compound power-split hybrid transmission, correct estimations of pressure and torque of the clutches are crucial for control strategies. A hierarchical estimator is proposed herein for individual estimation of the clutch torques, consisting of not only the reference layer containing the unknown input observer of vehicle resistance and the reduced-order observer of drive shaft torque, but also the estimation layer combining the unknown input observer with the reduced-order observer. The estimator is implemented to strike a balance between estimation accuracy in the steady state and real time response in the transient state. For validation of the estimator, simulations and real car tests are carried out in specific drive conditions. By numerical results, it’s demonstrated that excellent predictive abilities are found including reasonably small estimation error and adaptive capability and, as a result, shift to shift induced driveline oscillations and vehicle jerks are reduced significantly.
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Lopez, Luis Felipe, Joseph J. Beaman e Rodney L. Williamson. "A Reduced-Order Model for Dynamic Vacuum Arc Remelting Pool Depth Estimation and Control". In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-5958.
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Vacuum arc remelting (VAR) is an industrial metallurgical process widely used throughout the specialty metals industry to cast large alloy ingots. A reduced-order model of the growing and solidifying ingot was developed specifically for dynamic control and estimation of the depth of molten liquid pool atop the ingot in a VAR process. This model accounts only for the thermal aspects of the system ignoring high-fidelity physics such as fluid flow and electromagnetic effects. Spectral methods were used to obtain a set of nonlinear dynamic equations which capture the transient characteristics of liquid pool shape variations around a quasi-steady operating condition. These nonlinear equations are then linearized about this operating condition and further simplified by suppressing fast modes. The resulting system can be described by only six state variables. The reduced order model compares favorably to pool depth changes predicted by an accurate finite-volume model. A first approach to use this model in the design of a dynamic VAR pool depth estimator and controller is also proposed.
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Beaman, Joseph J., Rodney L. Williamson, David K. Melgaard e Jon Hamel. "A Nonlinear Reduced Order Model for Estimation and Control of Vacuum Arc Remelting of Metal Alloys". In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79239.
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Vacuum arc remelting (VAR) is an industrial metallurgical process widely used throughout the specialty metals industry to cast large alloy ingots. The VAR process is carried out in a vacuum with the aim of melting a large consumable electrode (.4 m in diameter and 3000 kg in mass and larger) in such a way that that the resulting ingot has improved homogeneity. The VAR control problem consists of adjusting arc current to control electrode melt rate, which also depends on the electrode temperature distribution and adjusting electrode ram speed to control the arc gap between the electrode and the ingot. The process is governed by a 1 dimensional heat conduction partial differential equation with a moving boundary, which leads to an infinite dimensional, nonlinear system. In addition to the process nonlinearity, the inputs and all of the available measurements are corrupted with noise. In order to design a controller and a Kalman based estimator for this process, integral methods are used to derive a set of two coupled nonlinear ordinary differential equations in time, which capture the steady state and transient characteristics of melting in a VAR furnace. The model with the experimentally measured noise is then used to construct an estimator and a controller. The system can be described by two state variables that change in time: thermal boundary layer and melted length or alternatively electrode gap. The reduced order model compares favorably to an accurate finite difference model as well as melting data acquired for Ti-6Al-4V. It will be shown how this model can be used to obtain dynamic closed loop melt rate control while simultaneously controlling electrode gap. This controller and estimator were tested on a laboratory furnace at Timet.
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Yung, Kobe Hoi-Yin, Qing Xiao, Atilla Incecik e Peter Thompson. "Mooring Force Estimation for Floating Offshore Wind Turbines With Augmented Kalman Filter: a Step Towards Digital Twin". In ASME 2023 5th International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/iowtc2023-119374.
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Abstract During the recent research studies Digital Twin (DT) simulation models for Structural Health Monitoring (SHM) based on data-driven mode have been developed, which can provide accurate simulation and prediction of mooring forces of Floating Offshore Wind Turbines (FOWTs). However, the performance of this kind modelling is highly affected by the quantity of real data training set and it is limited to some specific configuration and the recorded environmental conditions. More importantly, the data-driven DT cannot interpret the physical meaning of structural dynamic interactions. Therefore, a new Physics-Based estimator is proposed in this work. The fully coupled FOWT simulations are carried out in QBlade Ocean and the simulation results are used to prepare the Reduced-Order Model by system identification for different sea states. The proposed estimator is based on the Augmented Kalman Filter in which the unknown mooring force is augmented as a state. The prediction of state is adjusted with the measurable platform motion data. It demonstrates the ability of filtering the noise in measurements and capturing the dynamic behaviour of FOWT with acceptable low computational cost. This real-time state estimator also provides the foundation of developing the DT modelling framework of FOWT and enables us to scale-up FOWTs in the next stage.
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Noursadeghi, Elaheh, e Ioannis Raptis. "A Particle Filtering-Based Approach for Distributed Fault Diagnosis and Estimation of Multi-Robot Systems". In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9789.
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This paper deals with the problem of designing a distributed fault diagnosis and estimation algorithm for multi-robot systems that are subject to faults in the form of abrupt velocity biases. To solve this problem, the multi-robot collective is converted to a network of interconnected diagnostic nodes (DNs) that is deployed to monitor the health of the system. Each node consists of a reduced-order estimator with relative state measurements and an online parameter learning filter. The local estimator executes a distributed variation of the particle filtering algorithm using the local sensor measurements and the fault progression model of the robots. The parameter learning filter is used to obtain an approximation of the severity of faults. Numerical simulations demonstrate the efficiency of the proposed approach.
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Clark, William W., Joo H. Kim e Franz J. Shelley. "Hybrid Feedforward/Kalman-Filter Controller for Reaction Force Suppression". In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0943.
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Abstract This paper presents a method for actively controlling dynamic reaction forces in flexible structures subject to persistent excitations. The motivation is provided by applications in which dynamic loads result in structural or machinery failures (for example, in the bearings of rotating machinery), or in situations where transmitted vibrations degrade the performance of a system or process. This work is a deviation from much of the vibration control work which focuses on suppressing structural displacements instead of forces. The controller is based on an adaptive feedforward disturbance cancellation scheme which has come to be a commonly used method among researchers for persistent disturbance cancellation. In order to use that method for control of forces, however, some modifications are necessary. First, since reaction forces are not typically measured in structures, a Kalman filter state estimator is developed to provide the reaction force signal to the feedforward algorithm, and second, the residual flexibility matrix is used in conjunction with the Kalman filter in order to provide accurate force estimates in spite of a reduced-order model. This paper presents the development of the reaction forces, the Kalman filter estimator, and the hybrid feedforward control law, and applies the overall method to a flexible beam test rig to suppress the dynamic reaction forces at its supports.
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Sanaei, Alireza, Shuai He, Joshua Pope, Santosh Verma, Rick Mifflin e Amr El-Bakry. "Apply Reduced-Physics Modeling to Accelerate Depletion Planning Optimization Under Subsurface Uncertainty". In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210217-ms.
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Abstract Robust decision-making regarding reservoir management using model-based strategies requires a large number of evaluations which can be enormously time-consuming if incorporating the full-field simulation. This challenge becomes more acute when dealing with subsurface uncertainty represented by multiple geologic scenarios. Various reduced-physics and reduced-order models such as streamlines and upscaling are commonly applied to accelerate the optimization process by reducing the computational burden of each evaluation. In this paper we propose an innovative integrated workflow that applies Flow Diagnostics, a reduced-physics modelling tool, to accomplish this acceleration and the mesh-adaptive direct search (NOMAD) algorithm to efficiently optimize well location. Flow Diagnostics (FD) is a reduced-physics approach that characterizes key flow behaviors and reservoir heterogeneity by combining a single-phase pressure solution with a time-of-flight estimator based on steady-state flux. The time-of-flight values for each grid cell can subsequently be combined with initial reservoir conditions to estimate saturation-weighted 3-phase production using 1D Buckley-Leverett fractional flow relationships. Our testing of this technique on a 2-phase, water flooding asset showed that, while it does not accurately predict exact volumes, there is a strong rank-order correlation with the full numerical simulator. Thus, this technique is an efficient one to assist in the decision-making and optimization process. To demonstrate this workflow, we applied this strategy to a synthetic base model of a deep water reservoir. Synthetic uncertainty was added to the base case to generate multiple subsurface scenarios. Representative P10, P50 and P90 cases were selected to represent low-, middle-, and high-side cases based on cumulative distribution function (CDF) of NPV. Different well placement optimization studies using high fidelity models and reduced order models were then performed on these cases (optimization of each single scenario on its own and optimization across all representative scenarios simultaneously) to compare solutions and effectiveness. Results indicated that, when the optimized well plans were run on the simulator, optimization under uncertainty provided a solution more robust across the uncertainty space. The results of the full study successfully demonstrate the efficacy of our workflow.
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Gou, Fung-Yuan, e N. Harris McClamroch. "Optimal Reduced-Order State Estimators for Unstable Plants". In 1989 American Control Conference. IEEE, 1989. http://dx.doi.org/10.23919/acc.1989.4790633.
Texto completo da fonteRelatórios de organizações sobre o assunto "Reduced-Order state estimator"
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Jameel, Yusuf, Paul West e Daniel Jasper. Reducing Black Carbon: A Triple Win for Climate, Health, and Well-Being. Project Drawdown, novembro de 2023. http://dx.doi.org/10.55789/y2c0k2p3.
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Black carbon – also referred to as soot – is a particulate matter that results from the incomplete combustion of fossil fuels and biomass. As a major air and climate pollutant, black carbon (BC) emissions have widespread adverse effects on human health and climate change. Globally, exposure to unhealthy levels of particulate matter, including BC, is estimated to cause between three and six million excess deaths every year. These health impacts – and the related economic losses – are felt disproportionately by those living in low- and middle-income countries. Furthermore, BC is a potent greenhouse gas with a short-term global warming potential well beyond carbon dioxide and methane. Worse still, it is often deposited on sea ice and glaciers, reducing reflectivity and accelerating melting, particularly in the Arctic and Himalayas. Therefore, reducing BC emissions results in a triple win, mitigating climate change, improving the lives of more than two billion people currently exposed to unclean air, and saving trillions of dollars in economic losses. Today, the majority of BC emissions stem from just a handful of sectors and countries. Over 70% of BC comes from the residential and transportation sectors, with the latter being the dominant source in high-income countries and the former driving emissions in low- and middle-income nations. On a country-level, China and India are the biggest emitters accounting for one-third of global BC emissions. When combined with Brazil, Indonesia, and Nigeria, these five countries alone emit 50% of all BC. While BC emissions trends over the past 20 years have been inconsistent globally, there has been a notable decline in Europe, North America, and China. Conversely, emissions have been rising in regions like Africa, South Asia, and Central Asia. The Intergovernmental Panel on Climate Change recommends deep reductions in BC emissions by 2030 to achieve the Paris Climate Agreement goal of limiting warming to below 1.5°C, yet very few countries have addressed BC in their climate plans. Fortunately, solutions that can rapidly reduce BC emissions by the end of this decade are readily available. By implementing the right policies, deploying targeted interventions in hotspots, and redirecting climate finance, policymakers and funders can mitigate the climate effects of BC while saving millions of lives and trillions of dollars. Below are key recommendations to achieve these aims based on the findings of this report: Urgently implement clean cooking solutions Providing clean cooking fuels and technologies in sub-Saharan Africa and South Asia, especially in the hotspots of the Indo-Gangetic Plains, Nigeria, and Uganda, can significantly reduce BC emissions. Countries with low penetration of clean cooking fuel must urgently develop policies that make clean cooking a priority for health and climate. Target transportation to reduce current – and prevent future – emissions Retrofitting older diesel engines with diesel particulate filters can remove up to 95% of BC. Countries around the world must implement policies to phase out polluting vehicles, set emission standards, and accelerate the uptake of EVs and hybrids, especially in urban regions where transportation demand is growing rapidly. A successful shift to EVs demands national investments complemented with international financing and private capital. Multilateral development banks need to play a pivotal role in this transition, with strategies like concessional finance to fast-track key projects and stimulate private sector investment. Reduce BC from the shipping industry BC emissions from the shipping industry must be urgently reduced to protect the Arctic ecosystem. Shifting shipping away from heavy fuel oil and equipping ships with diesel particulate filters is a cost-effective approach that would quickly and significantly reduce emissions. Regulate air quality Stringent emissions standards, clean air laws, baselines, and mandatory monitoring programs can effectively reduce BC emissions. Such policies have already resulted in large reductions in Europe, North America, and, more recently, China. However, several low- and middle-income countries have no legal protection for ambient air quality and lack legislatively-mandated standards. Implementing strong and legally binding policies can result in a large decrease in BC emissions, particularly across the transportation and industry sectors. Include BC in nationally determined contributions and the UNFCCC Only 12 countries have explicitly addressed BC in their nationally determined contributions (NDCs). This limited focus on BC is partly due to its omission from the United Nations Framework Convention on Climate Change’s (UNFCCC) list of climate pollutants, an oversight that should be reconsidered given that reducing BC would save countless lives and slow global warming. As nations review their NDCs by 2025, they must incorporate BC reduction efforts to meet climate and well-being targets. Improve BC measurements and estimates BC estimates are plagued by uncertainties. Therefore, there is an urgent need for more accurate inventories in order to develop better emission reduction plans. Stakeholders must collaborate to develop a consistent BC measurement protocol, prioritize the collection of high-quality data, and use state of the art models to enhance estimates and reduce uncertainties.
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Lers, Amnon, Majid R. Foolad e Haya Friedman. genetic basis for postharvest chilling tolerance in tomato fruit. United States Department of Agriculture, janeiro de 2014. http://dx.doi.org/10.32747/2014.7600014.bard.
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ABSTRACT Postharvest losses of fresh produce are estimated globally to be around 30%. Reducing these losses is considered a major solution to ensure global food security. Storage at low temperatures is an efficient practice to prolong postharvest performance of crops with minimal negative impact on produce quality or human health and the environment. However, many fresh produce commodities are susceptible to chilling temperatures, and the application of cold storage is limited as it would cause physiological chilling injury (CI) leading to reduced produce quality. Further, the primary CI becomes a preferred site for pathogens leading to decay and massive produce losses. Thus, chilling sensitive crops should be stored at higher minimal temperatures, which curtails their marketing life and in some cases necessitates the use of other storage strategies. Development of new knowledge about the biological basis for chilling tolerance in fruits and vegetables should allow development of both new varieties more tolerant to cold, and more efficient postharvest storage treatments and storage conditions. In order to improve the agricultural performance of modern crop varieties, including tomato, there is great potential in introgression of marker-defined genomic regions from wild species onto the background of elite breeding lines. To exploit this potential for improving tomato fruit chilling tolerance during postharvest storage, we have used in this research a recombinant inbred line (RIL) population derived from a cross between the red-fruited tomato wild species SolanumpimpinellifoliumL. accession LA2093 and an advanced Solanum lycopersicumL. tomato breeding line NCEBR-1, developed in the laboratory of the US co-PI. The original specific objectives were: 1) Screening of RIL population resulting from the cross NCEBR1 X LA2093 for fruit chilling response during postharvest storage and estimation of its heritability; 2) Perform a transcriptopmic and bioinformatics analysis for the two parental lines following exposure to chilling storage. During the course of the project, we learned that we could measure greater differences in chilling responses among specific RILs compared to that observed between the two parental lines, and thus we decided not to perform transcriptomic analysis and instead invest our efforts more on characterization of the RILs. Performing the transcriptomic analysis for several RILs, which significantly differ in their chilling tolerance/sensitivity, at a later stage could result with more significant insights. The RIL population, (172 lines), was used in field experiment in which fruits were examined for chilling sensitivity by determining CI severity. Following the field experiments, including 4 harvest days and CI measurements, two extreme tails of the response distribution, each consisting of 11 RILs exhibiting either high sensitivity or tolerance to chilling stress, were identified and were further examined for chilling response in greenhouse experiments. Across the RILs, we found significant (P < 0.01) correlation between field and greenhouse grown plants in fruit CI. Two groups of 5 RILs, whose fruits exhibited reproducible chilling tolerant/sensitive phenotypes in both field and greenhouse experiments, were selected for further analyses. Numerous genetic, physiological, biochemical and molecular variations were investigated in response to postharvest chilling stress in the selected RILs. We confirmed the differential response of the parental lines of the RIL population to chilling stress, and examined the extent of variation in the RIL population in response to chilling treatment. We determined parameters which would be useful for further characterization of chilling response in the RIL population. These included chlorophyll fluorescence Fv/Fm, water loss, total non-enzymatic potential of antioxidant activity, ascorbate and proline content, and expression of LeCBF1 gene, known to be associated with cold acclimation. These parameters could be used in continuation studies for the identification and genetic mapping of loci contributing to chilling tolerance in this population, and identifying genetic markers associated with chilling tolerance in tomato. Once genetic markers associated with chilling tolerance are identified, the trait could be transferred to different genetic background via marker-assisted selection (MAS) and breeding. The collaborative research established in this program has resulted in new information and insights in this area of research and the collaboration will be continued to obtain further insights into the genetic, molecular biology and physiology of postharvest chilling tolerance in tomato fruit. The US Co-PI, developed the RIL population that was used for screening and measurement of the relevant chilling stress responses and conducted statistical analyses of the data. Because we were not able to grow the RIL population under field conditions in two successive generations, we could not estimate heritability of response to chilling temperatures. However, we plan to continue the research, grow the RIL progeny in the field again, and determine heritability of chilling tolerance in a near future. The IS and US investigators interacted regularly and plan to continue and expand on this study, since combing the expertise of the Co-PI in genetics and breeding with that of the PI in postharvest physiology and molecular biology will have great impact on this line of research, given the significant findings of this one-year feasibility project.
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Monetary Policy Report - October 2022. Banco de la República Colombia, outubro de 2022. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr4-2022.
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1.1 Macroeconomic summary In September, headline inflation (11.4% annually) and the average of core inflation indicators (8.6% annually) continued on a rising trend, and higher increases than expected were recorded. Forecasts increased again, and inflation expectations remained above 3%. Inflationary surprises in the third quarter were significant and widespread, and they are the result of several shocks. On the one hand, international cost and price shocks, which have mainly affected goods and foods, continue to exert upwards pressure on national inflation. In addition to these external supply shocks, domestic supply shocks have also affected foods. On the other hand, the strong recovery of aggregate demand, especially for private consumption and for machinery and equipment, as well as a higher accumulated depreciation of the Colombian peso and its pass-through to domestic prices also explain the rise in inflation. Indexation also contributes, both through the Consumer Price Index (CPI) and through the Producer Price Index (PPI), which continues to have a significant impact on electricity prices and, to a lesser degree, on other public utilities and rent. In comparison with July’s report, the new forecast trajectory for headline and core inflation (excluding food and regulated items) is higher in the forecast horizon, mainly due to exchange rate pressures, higher excess demand, and indexation at higher inflation rates, but it maintains a trend of convergence towards the target. In the case of food, a good domestic supply of perishable foods and some moderation in international processed food prices are still expected. However, the technical staff estimates higher pressures on this group’s prices from labor costs, raw material prices, and exchange rates. In terms of the CPI for regulated items, the new forecast supposes reductions in electricity prices at the end of the year, but the effects of indexation at higher inflation rates and the expected rises in fuel prices would continue to push this CPI group. Therefore, the new projection suggests that, in December, inflation would reach 11.3% and would decrease throughout 2023 and 2024, closing the year at 7.1% and 3.5%, respectively. These forecasts have a high level of uncertainty, due especially to the future behavior of international financial conditions, external price and cost shocks, the persistence of depreciation of the Colombian peso, the pace of adjustment of domestic demand, the indexation degree of nominal contracts, and the decisions that would be made regarding domestic fuel and electricity prices. Economic activity continues to surprise on the upside, and the projection of growth for 2022 rose from 6.9% to 7.9% but lowered for 2023 from 1.1% to 0.5%. Thus, excess demand is higher than estimated in the previous report, and it would diminish in 2023. Economic growth in the second quarterwas higher than estimated in July due to stronger domestic demand, mainly because of private consumption. Economic activity indicators for the third quarter suggest that the GDP would stay at a high level, above its potential, with an annual change of 6.4%, and 0.6% higher than observed in the second quarter. Nevertheless, these numbers reflect deceleration in its quarterly and annual growth. Domestic demand would show similar behavior, with a high value, higher than that of output. This can be explained partly by the strong behavior of private consumption and investment in machinery and equipment. In the third quarter, investment in construction would have continued with mediocre performance, which would still place it at levels lower than those observed before the pandemic. The trade deficit would have widened due to high imports with a stronger trend than that for exports. It is expected that, in the forecast horizon, consumption would decrease from its current high levels, partly as a consequence of tighter domestic financial conditions, lower repressed demand, higher exchange rate pressures on imported goods prices, and the deterioration of actual income due to the rise in inflation. Investment would continue to lag behind, without reaching the levels observed before the pandemic, in a context of high financing costs and high uncertainty. A lower projected behavior in domestic demand and the high levels of prices for oil and other basic goods that the country exports would be reflected in a reduction in the trade deficit. Due to all of this, economic growth for all of 2022, 2023, and 2024 would be 7.9%, 0.5%, and 1.3%, respectively. Expected excess demand (measured via the output gap) is estimated to be higher than contemplated in the previous report; it would diminish in 2023 and could turn negative in 2024. These estimates remain subject to a high degree of uncertainty related to global political tension, a rise in international interest rates, and the effects of this rise on demand and financial conditions abroad. In the domestic context, the evolution of fiscal policy as well as future measures regarding economic policy and their possible effects on macroeconomic imbalances in the country, among others, are factors that generate uncertainty and affect risk premia, the exchange rate, investment, and the country’s economic activity. Interest rates at several of the world’s main central banks continue to rise, some at a pace higher than expected by the market. This is in response to the high levels of inflation and their inflation expectations, which continue to exceed the targets. Thus, global growth projections are still being moderated, risk premia have risen, and the dollar continues to gain strength against other main currencies. International pressures on global inflation have heightened. In the United States, core inflation has not receded, pressured by the behavior of the CPI for services and a tight labor market. Consequently, the U.S. Federal Reserve continued to increase the policy interest rate at a strong pace. This rate is expected to now reach higher levels than projected in the previous quarter. Other developed and emerging economies have also increased their policy interest rates. Thus, international financial conditions have tightened significantly, which reflects in a widespread strengthening of the dollar, increases in worldwide risk premia, and the devaluation of risky assets. Recently, these effects have been stronger in Colombia than in the majority of its peers in the region. Considering all of the aforementioned, the technical staff of the bank increased its assumption regarding the U.S. Federal Reserve’s interest rate, reduced the country’s external demand growth forecast, and raised the projected trajectory for the risk premium. The latter remains elevated at higher levels than its historical average, within a context of high local uncertainty and of extensive financing needs from the foreign sector and the public sector. All of this results in higher inflationary pressures associated to the depreciation of the Colombian peso. The uncertainty regarding external forecasts and its impact on the country remain elevated, given the unforeseeable evolution of the conflict between Russia and Ukraine, of geopolitical tensions, and of the tightening of external financial conditions, among others. A macroeconomic context of high inflation, inflation expectations and forecasts above 3%, and a positive output gap suggests the need for contractionary monetary policy, compatible with the macroeconomic adjustment necessary to eliminate excess demand, mitigate the risk of unanchoring in inflation expectations, and guarantee convergence of inflation at the target. In comparison with the July report forecasts, domestic demand has been more dynamic, with a higher observed output level that surpasses the economy’s productive capacity. Headline and core inflation have registered surprising rises, associated with the effects of domestic and external price shocks that were more persistent than anticipated, with excess demand and indexation processes in some CPI groups. The country’s risk premium and the observed and expected international interest rates increased. As a consequence of this, inflationary pressures from the exchange rate rose, and in this report, the probability of the neutral real interest rate being higher than estimated increased. In general, inflation expectations for all terms and the bank’s technical staff inflation forecast for 2023 increased again and continue to stray from 3%. All of the aforementioned elevated the risk of unanchoring inflation expectations and could heighten widespread indexation processes that push inflation away from the target for a longer time. In this context, it is necessary to consolidate a contractionary monetary policy that tends towards convergence of inflation at the target in the forecast horizon and towards the reduction of excess demand in order to guarantee a sustainable output level trajectory. 1.2 Monetary policy decision In its September and October of 2022 meetings, Banco de la República’s Board of Directors (BDBR) decided to continue adjusting its monetary policy. In September, the BDBR decided by a majority vote to raise the monetary policy interest rate by 100 basis points (bps), and in its October meeting, unanimously, by 100bps. Therefore, the rate is at 11.0%. Boxes 1 Food inflation: a comparison with other countries