Relevant bibliographies by topics / MPC

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'MPC'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 July 2024

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Journal articles on the topic "MPC"

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Firsov, Alexander A., Elena N. Strukova, Darya S. Shlykova, Yury A. Portnoy, Varvara K. Kozyreva, Mikhail V. Edelstein, Svetlana A. Dovzhenko, Mikhail B. Kobrin, and Stephen H. Zinner. "Bacterial Resistance Studies UsingIn VitroDynamic Models: the Predictive Power of the Mutant Prevention and Minimum Inhibitory Antibiotic Concentrations." Antimicrobial Agents and Chemotherapy 57, no. 10 (July 29, 2013): 4956–62. http://dx.doi.org/10.1128/aac.00578-13.

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ABSTRACTIn light of the concept of the mutant selection window, i.e., the range between the MIC and the mutant prevention concentration (MPC), MPC-related pharmacokinetic indices should be more predictive of bacterial resistance than the respective MIC-related indices. However, experimental evidence of this hypothesis remains limited and contradictory. To examine the predictive power of the ratios of the area under the curve (AUC24) to the MPC and the MIC, the selection of ciprofloxacin-resistant mutants of fourEscherichia colistrains with different MPC/MIC ratios was studied. Each organism was exposed to twice-daily ciprofloxacin for 3 days at AUC24/MIC ratios that provide peak antibiotic concentrations close to the MIC, between the MIC and the MPC, and above the MPC. ResistantE. coliwas intensively enriched at AUC24/MPCs from 1 to 10 h (AUC24/MIC from 60 to 360 h) but not at the lower or higher AUC24/MPC and AUC24/MIC ratios. AUC24/MPC and AUC24/MIC relationships of the areas under the time courses of ciprofloxacin-resistantE. coli(AUBCM) were bell-shaped. A Gaussian-like function fits the AUBCM-AUC24/MPC and AUBCM-AUC24/MIC data combined for all organisms (r2= 0.69 and 0.86, respectively). The predicted anti-mutant AUC24/MPC ratio was 58 ± 35 h, and the respective AUC24/MIC ratio was 1,080 ± 416 h. Although AUC24/MPC was less predictive of strain-independentE. coliresistance than AUC24/MIC, the established anti-mutant AUC24/MPC ratio was closer to values reported forStaphylococcus aureus(60 to 69 h) than the respective AUC24/MIC ratio (1,080 versus 200 to 240 h). This implies that AUC24/MPC might be a better interspecies predictor of bacterial resistance than AUC24/MIC.
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Strukova, Elena N., Yury A. Portnoy, Andrey V. Romanov, Mikhail V. Edelstein, Stephen H. Zinner, and Alexander A. Firsov. "Searching for the Optimal Predictor of Ciprofloxacin Resistance in Klebsiella pneumoniae by UsingIn VitroDynamic Models." Antimicrobial Agents and Chemotherapy 60, no. 3 (December 7, 2015): 1208–15. http://dx.doi.org/10.1128/aac.02334-15.

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There is growing evidence of applicability of the hypothesis of the mutant selection window (MSW), i.e., the range between the MIC and the mutant prevention concentration (MPC), within which the enrichment of resistant mutants is most probable. However, it is not clear if MPC-based pharmacokinetic variables are preferable to the respective MIC-based variables as interstrain predictors of resistance. To examine the predictive power of the ratios of the area under the curve (AUC24) to the MPC and to the MIC, the selection of ciprofloxacin-resistant mutants of threeKlebsiella pneumoniaestrains with different MPC/MIC ratios was studied. Each organism was exposed to twice-daily ciprofloxacin for 3 days at AUC24/MIC ratios that provide peak antibiotic concentrations close to the MIC, between the MIC and the MPC, and above the MPC. ResistantK. pneumoniaemutants were intensively enriched at an AUC24/MIC ratio of 60 to 360 h (AUC24/MPC ratio from 2.5 to 15 h) but not at the lower or higher AUC24/MIC and AUC24/MPC ratios, in accordance with the MSW hypothesis. AUC24/MPC and AUC24/MIC relationships with areas under the time courses of ciprofloxacin-resistantK. pneumoniae(AUBCM) were bell shaped. These relationships predict highly variable “antimutant” AUC24/MPC ratios (20 to 290 h) compared to AUC24/MIC ratios (1,310 to 2,610 h). These findings suggest that the potential of the AUC24/MPC ratio as an interstrain predictor ofK. pneumoniaeresistance is lower than that of the AUC24/MIC ratio.
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Palupi, Maria Fatima, Eli Nugraha, Meutia Hayati, and Neneng Atikah. "Mutant Prevention Concentration Siprofloksasin Terhadap Escherichia coli Patogen dari Kloaka Broiler Secara In Vitro." Jurnal Sain Veteriner 39, no. 1 (April 1, 2021): 20. http://dx.doi.org/10.22146/jsv.57040.

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Mutant prevention concentration (MPC) is an in vitro test used to determine the lowest drug concentration needed to inhibit the growth of a single-step-mutant bacterial subpopulation. The purpose of this study was to determine the MPC value of ciprofloxacin against pathogenic Escherichia coli to obtained the range of mutant selection windows (MSW) of ciprofloxacin. Ciprofloxacin is a quinolone group that is included in the Highest Priority Critically Important Antimicrobials for Human Medicine but is also used for the treatment of bacterial infections in production animals. Twenty-four of pathogenic E. coli isolates sensitive to ciprofloxacin were tested to obtain MPC values and minimum inhibitory concentration (MIC) values. Test the MPC and MIC values to get the MSW range is done by the method of agar dilution. Mueller-Hinton agar containing standard ciprofloxacin was inoculated with 1010 cfu E. coli for the MPC test and 104 for the MIC test. Based on the MPC test results, the MPC value of ciprofloxacin was 4-64 μg / mL (22.96 ± 19.07 μg / mL) and there was one isolate which had an MPC> 256 μg / mL. These results give a wide range of MSW with a lower limit of the MIC value of 0.25 - 2 µg / mL (0.55 ± 0.37 µg / mL) to the upper limit of the MPC value of 4-64 µg / mL (22.96 ± 19.07 μg / mL). Based on the results of this MPC assessment it can be concluded that the dose of ciprofloxacin in production animals has a wide range of MSW that is allow for single-step mutants.
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Firsov, Alexander A., Maria V. Golikova, Elena N. Strukova, Yury A. Portnoy, Andrey V. Romanov, Mikhail V. Edelstein, and Stephen H. Zinner. "In VitroResistance Studies with Bacteria That Exhibit Low Mutation Frequencies: Prediction of “Antimutant” Linezolid Concentrations Using a Mixed Inoculum Containing both Susceptible and Resistant Staphylococcus aureus." Antimicrobial Agents and Chemotherapy 59, no. 2 (December 1, 2014): 1014–19. http://dx.doi.org/10.1128/aac.04214-14.

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ABSTRACTBacterial resistance studies usingin vitrodynamic models are highly dependent on the starting inoculum that might or might not contain spontaneously resistant mutants (RMs). To delineate concentration-resistance relationships with linezolid-exposedStaphylococcus aureus, a mixed inoculum containing both susceptible cells and RMs was used. An RM selected after the 9th passage of the parent strain (MIC, 2 μg/ml) on antibiotic-containing media (RM9; MIC, 8 μg/ml) was chosen for the pharmacodynamic studies, because the mutant prevention concentration (MPC) of linezolid against the parent strain in the presence of RM9 at 102(but not at 104) CFU/ml did not differ from the MPC value determined in the absence of the RMs. Five-day treatments with twice-daily linezolid doses were simulated at concentrations either between the MIC and MPC or above the MPC.S. aureusRMs (resistant to 2× and 4× MIC but not 8× and 16× MIC) were enriched at ratios of the 24-h area under the concentration-time curve (AUC24) to the MIC that provide linezolid concentrations between the MIC and MPC for 100% (AUC24/MIC, 60 h) and 86% (AUC24/MIC, 120 h) of the dosing interval. No such enrichment occurred when linezolid concentrations were above the MIC and below the MPC for a shorter time (37% of the dosing interval; AUC24/MIC, 240 h) or when concentrations were consistently above the MPC (AUC24/MIC, 480 h). These findings obtained using linezolid-susceptible staphylococci supplemented with RMs support the mutant selection window hypothesis. This method provides an option to delineate antibiotic concentration-resistance relationships with bacteria that exhibit low mutation frequencies.
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Guan, Weide, Shoudao Huang, Derong Luo, and Fei Rong. "A Reverse Model Predictive Control Strategy for a Modular Multilevel Converter." Energies 12, no. 2 (January 18, 2019): 297. http://dx.doi.org/10.3390/en12020297.

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In recent years, modular multilevel converters (MMCs) have developed rapidly, and are widely used in medium and high voltage applications. Model predictive control (MPC) has attracted wide attention recently, and its advantages include straightforward implementation, fast dynamic response, simple system design, and easy handling of multiple objectives. The main technical challenge of the conventional MPC for MMC is the reduction of computational complexity of the cost function without the reduction of control performance of the system. Some modified MPC scan decrease the computational complexity by evaluating the number of on-state sub-modules (SMs) rather than the number of switching states. However, the computational complexity is still too high for an MMC with a huge number of SMs. A reverse MPC (R-MPC) strategy for MMC was proposed in this paper to further reduce the computational burden by calculating the number of inserted SMs directly, based on the reverse prediction of arm voltages. Thus, the computational burden was independent of the number of SMs in the arm. The control performance of the proposed R-MPC strategy was validated by Matlab/Simulink software and a down-scaled experimental prototype.
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Serrano-Rodríguez, J. M., C. Cárceles-García, C. M. Cárceles-Rodríguez, M. L. Gabarda, J. M. Serrano-Caballero, and E. Fernández-Varón. "Susceptibility and PK/PD relationships of Staphylococcus aureus strains isolated from the milk of sheep and goats with clinical mastitis to five veterinary fluoroquinolones." Veterinary Record 180, no. 15 (February 17, 2017): 376. http://dx.doi.org/10.1136/vr.103964.

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Minimum inhibitory concentration (MIC) and mutant prevention concentration (MPC) of veterinary fluoroquinolones as enrofloxacin, its metabolite ciprofloxacin, danofloxacin, difloxacin and marbofloxacin against Staphylococcus aureus strains (n=24) isolated from milk of sheep and goats affected by clinical mastitis were evaluated. The authors have used the MIC and MPC, as well as the pharmacokinetic-pharmacodynamic relationships in plasma and milk. MIC values were significantly different between drugs, unlike MPC values. Lower MIC values were obtained for danofloxacin and difloxacin, middle and higher values for enrofloxacin, ciprofloxacin and marbofloxacin. However, differences in MPC values were not found between drugs. At conventional doses, the AUC24/MIC and AUC24/MPC ratios were close to 30–80 hours and 5–30 hours, with exception of danofloxacin, in plasma and milk. The time inside the mutant selection window (TMSW) was close to 3–6 hours for enrofloxacin, ciprofloxacin and marbofloxacin, near to 8 hours for danofloxacin and 12–22 hours for difloxacin. From these data, the mutant selection window could be higher for danofloxacin and difloxacin compared with the other fluoroquinolones tested. The authors concluded that enrofloxacin and marbofloxacin, at conventional doses, could prevent the selection of bacterial subpopulations of S aureus, unlike danofloxacin and difloxacin, where higher doses could be used.
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Credito, Kim, Klaudia Kosowska-Shick, and Peter C. Appelbaum. "Mutant Prevention Concentrations of Four Carbapenems against Gram-Negative Rods." Antimicrobial Agents and Chemotherapy 54, no. 6 (March 22, 2010): 2692–95. http://dx.doi.org/10.1128/aac.00033-10.

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ABSTRACT We tested the propensities of four carbapenems to select for resistant Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii mutants by determining the mutant prevention concentrations (MPCs) for 100 clinical strains with various ß-lactam phenotypes. Among the members of the Enterobacteriaceae family and A. baumannii strains, the MPC/MIC ratios were mostly 2 to 4. In contrast, for P. aeruginosa the MPC/MIC ratios were 4 to ≥16. The MPC/MIC ratios for β-lactamase-positive K. pneumoniae and E. coli isolates were much higher (range, 4 to >16 μg/ml) than those for ß-lactamase-negative strains.
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Liang, Beibei, Nan Bai, Yun Cai, Rui Wang, Karl Drlica, and Xilin Zhao. "Mutant Prevention Concentration-Based Pharmacokinetic/Pharmacodynamic Indices as Dosing Targets for Suppressing the Enrichment of Levofloxacin-Resistant Subpopulations of Staphylococcus aureus." Antimicrobial Agents and Chemotherapy 55, no. 5 (February 22, 2011): 2409–12. http://dx.doi.org/10.1128/aac.00975-10.

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ABSTRACTMIC- and mutant prevention concentration (MPC)-based pharmacokinetic/pharmacodynamic (PK/PD) indices were compared for suitability as attainment targets for restricting amplification of levofloxacin-resistant mutant subpopulations. When threeStaphylococcus aureusstrains were examined with a hollow-fiber PK/PD model, area under the concentration-time curve over 24 h (AUC24)/MPC values of >25 and maximum concentration of drug in serum (Cmax)/MPC values of >2.2 predicted resistance outcome among different isolates with an interisolate kappa coefficient of 1. MIC-based mutant-restrictive PK/PD values varied >8-fold and exhibited only a moderate interisolate agreement (kappa coefficient of 0.5). Thus, MPC-based PK/PD indices are more suitable than MIC-based indices for predicting mutant-restricting fluoroquinolone doses when multiple bacterial isolates are considered.
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Zhanel, George G., Vibhu Vishisht, Ed Tam, Daryl J. Hoban, and James A. Karlowsky. "Mutant Prevention Concentrations of Doripenem and Meropenem Alone and in Combination with Colistin (Polymyxin E), Levofloxacin and Tobramycin inPseudomonas aeruginosa." Canadian Journal of Infectious Diseases and Medical Microbiology 20, suppl a (2009): 67A—71A. http://dx.doi.org/10.1155/2009/801612.

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BACKGROUND: With a limited number of available antimicrobial agents to treatPseudomonas aeruginosainfections, the prevention of emergence of antimicrobial resistance and its subsequent spread is critical. In the present study, the mutant prevention concentration (MPC) of doripenem was examined and compared with meropenem for its ability to prevent resistant mutant selection forP aeruginosawhen used alone and in combination with the other antipseudomonal agents colistin (polymyxin E), levofloxacin and tobramycin. OBJECTIVE: To determine if two antimicrobial agents that possessed different mechanisms of action and separate demonstrated activities againstP aeruginosawould produce a reduced MPC in combination compared with the MPC of each agent alone. METHODS: Twelve clinical isolates ofP aeruginosawere plated on Mueller-Hinton agar containing 1×, 2×, 4×, 8×, 16× and 32× the doripenem, imipenem or meropenem minimum inhibitory concentration (MIC), and also on agar containing doripenem or meropenem in combination with either tobramycin (6 μg/mL), colistin (2 μg/mL or 8 μg/mL), levofloxacin (8 μg/mL) or azithromycin (0.4 μg/mL). The MPC for each antimicrobial agent-isolate combination was defined as the lowest antibiotic concentration that prevented the visible growth of mutant colonies at 48 h of incubation. The MPC/MIC (μg/mL) ratio was defined as the ratio of the MPC obtained to the original MIC. RESULTS: The MPC/MIC ratios of doripenem alone ranged from 8 to 32 for the twelve isolates tested compared with 32 for two isolates and greater than 32 for 10 isolates with imipenem, and 32 for three isolates and greater than 32 for nine isolates with meropenem. All antimicrobials tested exhibited markedly elevated MPCs compared with their original MICs with MPC/MIC ratios of 8 to 32 for doripenem, 32 to greater than 32 for imipenem, 32 to greater than 32 for meropenem, 32 to greater than 32 for colistin (tested at 2 μg/mL), 8 to 16 for levofloxacin and 8 to 32 for tobramycin. When a second antimicrobial was used in combination with doripenem, the MPC/MIC ratio decreased up to twofold for doripenem combined with colistin (2 μg/mL), decreased four- to 16-fold for doripenem combined with colistin (8 μg/mL), decreased eight- to 32-fold for doripenem combined with levofloxacin, and decreased four- to 16-fold for doripenem combined with tobramycin. Adding a second antimicrobial in combination with meropenem resulted in the following decreases in MPC/MIC: no decrease for meropenem combined with colistin (2 μg/mL), four to greater than eightfold decrease for meropenem combined with colistin (8 μg/mL), four- to 16-fold decrease for meropenem combined with levofloxacin, and two- to 16-fold decrease for meropenem combined with tobramycin. For all antimicrobial combinations tested, doripenem yielded greater decreases in MPC/MIC ratios than did meropenem. CONCLUSION: The present study found that individual antipseudomonal antimicrobial agents tested against 12 clinical isolates ofP aeruginosahad eight- to greater than 32-fold higher MPCs than MICs, that combining doripenem or meropenem with a second active antipseudomonal agent with a different mechanism of action was more effective at preventing resistance selection than the two agents used individually, and finally, that doripenem was less likely than both imipenem and meropenem to select for spontaneous resistance mutants ofP aeruginosa.
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Shetgaonkar, Ajay, Aleksandra Lekić, José Luis Rueda Torres, and Peter Palensky. "Microsecond Enhanced Indirect Model Predictive Control for Dynamic Power Management in MMC Units." Energies 14, no. 11 (June 5, 2021): 3318. http://dx.doi.org/10.3390/en14113318.

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The multi-modular converter (MMC) technology is becoming the preferred option for the increased deployment of variable renewable energy sources (RES) into electrical power systems. MMC is known for its reliability and modularity. The fast adjustment of the MMC’s active/reactive powers, within a few milliseconds, constitutes a major research challenge. The solution to this challenge will allow accelerated integration of RES, without creating undesirable stability issues in the future power system. This paper presents a variant of model predictive control (MPC) for the grid-connected MMC. MPC is defined using a Laguerre function to reduce the computational burden. This is achieved by reducing the number of parameters of the MMC cost function. The feasibility and effectiveness of the proposed MPC is verified in the real-time digital simulations. Additionally, in this paper, a comparison between an accurate mathematical and real-time simulation (RSCAD) model of an MMC is given. The comparison is done on the level of small-signal disturbance and a Mean Absolute Error (MAE). In the MMC, active and reactive power controls, AC voltage control, output current control, and circulating current controls are implemented, both using PI and MPC controllers. The MPC’s performance is tested by the small and large disturbance in active and reactive powers, both in an offline and online simulation. In addition, a sensitivity study is performed for different variables of MPC in the offline simulation. Results obtained in the simulations show good correspondence between mathematical and real-time analytical models during the transient and steady-state conditions with low MAE. The results also indicate the superiority of the proposed MPC with the stable and fast active/reactive power support in real-time simulation.
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Dissertations / Theses on the topic "MPC"

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Lima, Marcelo Lopes de. "Distributed satisficing MPC." reponame:Repositório Institucional da UFSC, 2014. https://repositorio.ufsc.br/xmlui/handle/123456789/122752.

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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2013.
Made available in DSpace on 2014-08-06T17:19:47Z (GMT). No. of bitstreams: 1 325382.pdf: 1772540 bytes, checksum: 52b8f812eaccad89bad0282c1c7c5db4 (MD5)
Abstract : To obtain a Pareto-optimal solution, the classical cooperative MPC implementsa categorical altruism imposed by a fixed global cost sharedby all the local controllers. Instead, this thesis implements a situationalaltruism where a global cost, neither imposed nor fixed, emerges fromconvex local costs and local specifications. The satisficing controllersemploy a distributed algorithm to find a solution that lies in a convexregion that is satisfactory and sufficient for all controllers (satisficing= satisfy + suffice), while optimizing in the direction of the analyticcenter of such a region. The system is modeled as being a network oflinear subsystems, coupled by their inputs, and the algorithm uses adistributed interior-point method to avoid fixed points when the constraintsare also coupled. The optimal solution of the satisficing MPC,besides Pareto-optimal, gives more importance to the controllers witha worst performance at the moment. Situational altruism permits amore balanced division of resources, avoiding the exploitation of onecontroller by the others. The satisficing MPC is shown to be stabilizingeven if suboptimal, provided that it is satisficing. To this end,stabilizing constraints are added to the basic formulation.
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Kück, Corvin. "MPC Design For Autonomous Drifting." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215873.

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The goal of this thesis is to evaluate the performance of different controllers to keep a remotecontrolledvehicle in a sustained drift. A bicycle model and an empirical tyre model are used formodelling the vehicle. The parameters for the used Fiala tyre model are experimentally identifiedand the simulation results of the modelled vehicle are compared to measured experimental data. Itfollows a stability analysis of the modelled system. The system is then linearized around one ofthe drift equilibria to allow controller design. A state feedback controller is designed to stabilizethe system, the controller gains are optimized using a Linear Quadratic Regulator (LQR) design,subsequently a Model Predictive Controller (MPC) is designed. Finally, the performance of the 3controllers is evaluated for a simulation with a disturbance acting on the system.
Målet med denna studie är att undersöka prestandan för olika reglerstrukturer när en radiostyrd bildriftar. En cykelmodell och en empirisk däcksmodell används för att modellera bilen.Parametrarna som användes för Fiala däcksmodellen är framtagna genom experiment ochsimuleringsresultatet av den modellerade bilen jämförs med verklig data. En stabilitetsanalys ärockså gjord för det modellerade systemet. Systemet är sedan linjäriserat runt ett jämviktsläge fördrifting för att kunna skapa en regulator. En tillståndsregulator med återkoppling används för attstabilisera systemet. Förstärkningskonstanterna för regulatorn optimeras med linjärkvadratiskreglering och sedan designas en modell prediktiv kontroller. Slutligen utvärderas prestandan,genom simulering, hos de tre regulatorerna när en störning finns i systemet.
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Torabi, Zahra. "Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances. The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average performance result for the real closed-loop system. To this end, we present our outcomes on local and global performance, illustrated by a numerical example.
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Jimoh, Mohammed Tajudeen. "A vision for MPC performance maintenance." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4739/.

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Model predictive control (MPC) is an advanced control that has found widespread use in industries, particularly in process industries like oil refining and petrochemicals. Although the basic premise behind MPC is easy to comprehend, its inner workings are still generally viewed or regarded as too advanced for actual plant operator understanding. This lack of understanding is exposed when MPC performance deteriorates sometime after commissioning, as is often the case in some commercially operated process plants. Currently operators might have difficulty over reasoning about MPC performance degradation and formulating steps to investigate the cause. A tool is described that aims to make MPC more transparent to the operators. Commonly reported causes of MPC performance degradation are discussed and ways in which the operator can recognise them when they occur are outlined. Issues that are addressed include: making the set of controlled variables to be used for a given set of manipulated variables simpler and clearer; ways to recognise when a MPC controller is performing poorly and to identify the source of performance deterioration. An aim is to determine under what instances the operator can return the MPC performance to previous levels or request for specialist support or simply switch the MPC off. A goal is to avoid the kind of often reported situation where the operator gets worried that the controller is deteriorating and ends up taking knee jerk actions that cause further problems in MPC. At the top of the maintenance tool hierarchy is the trends comparison group, where MPC reference graphical performance trends are compared with actual graphical performance trends counterpart. If any abnormality is observed in these trends, the operator is encouraged to choose an option from a list of preliminary diagnostic questions contained in a group below trends comparison group, which best describes the observed abnormality. Each abnormality is associated with a list of suspected causes. When a suspected cause is chosen from the associated list, the operator is led to the symptoms investigation window, which contains scripts detailing steps for systematic examination of each symptom, with a view to either rejecting or confirming the suspicion. Assisted in the investigation are four background information windows: the virtual plant without MPC window, the virtual plant with MPC window, the transfer function matrix window and steady state gain, relative gain array (RGA) and relative weight array (RWA) window. The windows contain information and guidance that the operator might refer to from time to time during symptom investigation. Development of the maintenance tool is still at the design stage. The key components described have been research implementing MPC on three nonlinear process models, a continuous stirred tank reactor (CSTR), an evaporator process and a fluid catalytic cracking unit (FCCU). Case studies representing different MPC degradation scenarios are simulated, followed by a systematic procedure for diagnosing, isolating and recovering from such degradation, based on assumed operator’s perspective and expert’s technical reasoning. The knowledge gained from the case studies is used to develop an outline of a vision for a data-driven model predictive maintenance tool to help the operator make sensible judgements about performance degradation, the form and direction of diagnosis and fault isolation, and possibly, the recovery procedure.
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Klaesson, Filip, and John Friberg. "Autonomous Overtaking Using Reachability Analysisand MPC." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230162.

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The era of autonomous cars is on the rise. Asdrivers lose control of the steering wheel, it is crucial that thecars themselves can guarantee safety for all traffic participants.This study aims to design a complete control system that cansafely perform an overtaking maneuver. To guarantee safety ofthe maneuver, reachability calculations will be carried out andanalyzed. The overtaking will be planned by using the modelpredictive control, MPC, framework. To complete the controlsystem a modified proportional controller will be designed totrack the planned path. The control system is implemented inMATLAB and the entire overtaking maneuver is simulated. Theresults show that the designed control framework successfullyperforms the overtaking on a straight two-lane highway in asafe manner.
Autonoma bilar är på frammarsch. När förare inte längre har kontroll över ratten är det avgörande att bilarna själva kan garantera säkerheten för alla trafikanter. Denna studie syftar till att utforma ett komplett styrsystem som kan utföra en säker omkörning. Omkörningen planeras med hjälp av ramverket för modell-prediktiv reglering. För att garantera säkerhet används nåbarhetsanalys. Slutligen utformas en modifierad proportionell regulator för att följa den planerade omkörningsvägen. Styrsystemet har implementerats i MATLAB och hela omkörningen har simulerats. Resultaten visar att det konstruerade styrsystemet utför omkörningen på en rak motorväg med två filer på ett säkert och framgångsrikt sätt.
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Andersson, Amanda, and Elin Näsholm. "Fast Real-Time MPC for Fighter Aircraft." Thesis, Linköpings universitet, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148580.

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The main topic of this thesis is model predictive control (MPC) of an unstable fighter aircraft. When flying it is important to be able to reach, but not exceed the aircraft limitations and to consider the physical boundaries on the control signals. MPC is a method for controlling a system while considering constraints on states and control signals by formulating it as an optimization problem. The drawback with MPC is the computational time needed and because of that, it is primarily developed for systems with a slowly varying dynamics. Two different methods are chosen to speed up the process by making simplifications, approximations and exploiting the structure of the problem. The first method is an explicit method, performing most of the calculations offline. By solving the optimization problem for a number of data sets and thereafter training a neural network, it can be treated as a simpler function solved online. The second method is called fast MPC, in this case the entire optimization is done online. It uses Cholesky decomposition, backward-forward substitution and warm start to decrease the complexity and calculation time of the program. Both methods perform reference tracking by solving an underdetermined system by minimizing the weighted norm of the control signals. Integral control is also implemented by using a Kalman filter to observe constant disturbances. An implementation was made in MATLAB for a discrete time linear model and in ARES, a simulation tool used at Saab Aeronautics, with a more accurate nonlinear model. The result is a neural network function computed in tenth of a millisecond, a time independent of the size of the prediction horizon. The size of the fast MPC problem is however directly affected by the horizon and the computational time will never be as small, but it can be reduced to a couple of milliseconds at the cost of optimality.
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Bin, Elisa. "MPC-based Visual Servo Control for UAVs." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284503.

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Vision information is essential for planning and control of autonomous systems.Vision-based control systems leverage rich visual input for motion planningand manipulation tasks. This thesis studies the problem of Image-BasedVisual Servo (IBVS) control for quadrotor UAVs. Despite the effectiveness ofvision-based systems, the control of quadrotors with IBVS presents the nontrivialchallenge of matching the 6 DoF control output obtained by the IBVSwith the 4DoF of the quadrotor. The novelty of this work lies in addressing theunder-actuation problem of quadrotors using linear Model Predictive Control(MPC). MPC is a well-known optimization control technique that leverages amodel of the system to predict its future behaviour as a function of the inputsignal. We extensively evaluate the performance of the designed solution onboth simulated environment and real-world experiments.
Visuell information är grundläggande för planering och kontroll av autonomasystem. Visionsbaserade kontrollsystem drar nytta av rik visuell inmatningför rörelseplanerings- och manipuleringsuppgifter. Den här avhandlingenstuderar problemet med Image-Based Visual Servo (IBVS) -kontroll förquadrotor UAVs. Trots effektiviteten hos visionsbaserade system utgör kontrollenav quadrotorer med IBVS den icke-triviala utmaningen att matcha 6DoF-kontrollutgång som erhållits av IBVS med 4DoF från quadrotorn. Nyheteni detta arbete ligger i en ny formulering av underaktiveringsproblemetför quadrotorer med linjär Model Predictive Control (MPC). MPC är en välkändoptimeringskontrollteknik som utnyttjar en modell av systemet för attförutsäga dess framtida beteende som en funktion av insignalen. Vi utvärderaromfattande prestandan för den designade lösningen i både simulerad miljö ochverkliga experiment.
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Maguire, Emma. "Monitoring of Lubricant Degradation with RULER and MPC." Thesis, Linköping University, Department of Physics, Chemistry and Biology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57846.

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Traditional oil analysis methods - e.g. acidity and viscosity measurements - have been used to monitor lubricant conditions. These methods can detect when the useful life of a lubricant is over but fall short when trying to gain insight on how long a lubricant in current use could last. This makes it difficult to make proactive decisions and estimate oil drain periods. Lubricants do not start to degrade until the antioxidants, which prevent from oxidation, have depleted to a certain level where they no longer can protect the base oil from degradation. During the degradation process insoluble contaminants form that can lead to sludge and varnish.

Four engine oils were oxidized using oxygen pressurized vessels and four hydraulic oils were oxidized with turbine oil stability test (TOST). At different stages of oxidation, sample aliquots were withdrawn and analysed. A blend of engine oil and biodiesel was also tested as well as a mixture of hydraulic oil and water. Samples of engine oils were also tested from a rig test running at SCANIA’s facilities in Södertälje, Sweden. The samples were evaluated with Remaining Useful Life Evaluation Routine (RULER) and Membrane Patch Colorimetry (MPC). RULER is a voltammetric method that measures the antioxidant level in a lubricant sample and MPC measure the insoluble contaminants by spectrophotometric analysis. Results from these analyses were compared to conventional methods such as acid number, viscosity, and Fourier Transform Infrared spectroscopy (FTIR).

Results from the MPC-analyses showed that this method is dependent on the type of the lubricant tested. RULER performed well for all tested lubricants. It was shown that this analyse method can predict when the lubricant is going to start to degrade due to oxidation. Tests showed that the oxidation of the lubricant starts when there are 20-25% of the antioxidants remaining.

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Meum, Patrick. "Optimal Reservoir control using nonlinear MPC and ECLIPSE." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9610.

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Recent years advances within well deployment and instrumentation technology offers huge potentials for increased oil recovery from reservoir production. Wells can now be equipped with controllable valves at reservoir depth, which may possibly alter the production profitability of the field completely, if the devices are used in an intelligent manner. This thesis investigates this potential by using model predictive control to maximize reservoir production performance and total oil production. The report describes an algorithm for nonlinear model predictive control, using a single shooting, multistep, quasi-Newton method, and implements it on an existing industrial MPC platform - Statoil's in-house MPC tool SEPTIC. The method is an iterative method, solving a series of quadratic problems analogous to sequential quadratic programming, to find the optimal control settings. An interface between SEPTIC and a commercial reservoir simulator, ECLIPSE, is developed for process modelling and predictions. ECLIPSE provides highly realistic and detailed reservoir behaviour and is used by SEPTIC to obtain numerical gradients for optimization. The method is applied to two reservoir examples, Case 1 and Case 2, and develops optimal control strategies for each of these. The two examples have conceptually different model structures. Case 1 is a simple introduction model. Case 2 is a benchmark model previously used in Yeten, Durlofsky and Aziz (2002) and models a North Sea type channelized reservoir. It is described by a set of different realizations, to capture a notion of model uncertainty. The report addresses each of the available realizations and shows how the value of an optimal production strategy can vary for equally probable realizations. Improvements in reservoir production performance using the model predictive control method are shown for all cases, compared to basic controlled references cases. For the benchmark example improvements range up to as much as 68% increase in one realization, and 30% on average for all realizations. This is an increase from the results previously published for the benchmark, with a 3% average. However, the level of improvement shows significant variation, and is only marginal for example Case 1. A thorough field analysis should therefore be performed before deciding to take the extra cost of well equipment and optimal control.

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Fleming, James. "Robust and stochastic MPC of uncertain-parameter systems." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310.

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Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions (LDIs) and linear parameter varying (LPV) systems. The designer is faced with a choice of using conservative bounds that may give poor performance, or accurate ones that require heavy online computation. This thesis presents a framework to achieve a more flexible trade-off between these two extremes by using a state tube, a sequence of parametrised polyhedra that is guaranteed to contain the future state. To define controllers using a tube, one must ensure that the polyhedra are a sub-set of the region defined by constraints. Necessary and sufficient conditions for these subset relations follow from duality theory, and it is possible to apply these conditions to constrain predicted system states and inputs with only a little conservatism. This leads to a general method of MPC design for uncertain-parameter systems. The resulting controllers have strong theoretical properties, can be implemented using standard algorithms and outperform existing techniques. Crucially, the online optimisation used in the controller is a convex problem with a number of constraints and variables that increases only linearly with the length of the prediction horizon. This holds true for both LDI and LPV systems. For the latter it is possible to optimise over a class of gain-scheduled control policies to improve performance, with a similar linear increase in problem size. The framework extends to stochastic LDIs with chance constraints, for which there are efficient suboptimal methods using online sampling. Sample approximations of chance constraint-admissible sets are generally not positively invariant, which motivates the novel concept of ‘sample-admissible' sets with this property to ensure recursive feasibility when using sampling methods. The thesis concludes by introducing a simple, convex alternative to chance-constrained MPC that applies a robust bound to the time average of constraint violations in closed-loop.
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Books on the topic "MPC"

1

C, Yanchyshyn Anne, ed. MPC flashbacks. Winnipeg: A.C. Yanchyshyn, 1989.

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Klaučo, Martin, and Michal Kvasnica. MPC-Based Reference Governors. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17405-7.

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Health, Malawi Ministry of, and Malawi Essential Drugs Programme, eds. The Malawi prescriber's companion (MPC). [Lilongwe]: Malawi Govt., Ministry of Health, 1993.

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Ltd, Silvacom, Canadian Forestry Service, and Canada-Alberta Forest Resource Development Agreement., eds. MPC PSP catalogue: Program and catalogue. 3rd ed. Edmonton, Alta: Canadian Forestry Service, 1987.

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Hansen, Stephen. Delayed doves: MPC voting behaviour of externals. London: Centre for Economic Performance, London School of Economics and Political Science, 2008.

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The MPC and recent developments in monetary policy. London: Stationery Office, 2003.

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Robinson, P. L. The metaplectic representation, Mpc structures, and geometric quantization. Providence, R.I., USA: American Mathematical Society, 1989.

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Gallieri, Marco. Lasso-MPC – Predictive Control with ℓ1-Regularised Least Squares. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27963-3.

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Takács, Gergely. Model Predictive Vibration Control: Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures. London: Springer London, 2012.

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Pablo, Nogueira, and SpringerLink (Online service), eds. Mathematics of Program Construction: 11th International Conference, MPC 2012, Madrid, Spain, June 25-27, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Book chapters on the topic "MPC"

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Völz, Andreas. "Unscented MPC." In Modellprädiktive Regelung nichtlinearer Systeme mit Unsicherheiten, 23–37. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-16279-5_4.

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Frederiksen, Tore K., Benny Pinkas, and Avishay Yanai. "Committed MPC." In Public-Key Cryptography – PKC 2018, 587–619. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76578-5_20.

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Camacho, Eduardo F., and Carlos Bordons. "Multivariable MPC." In Model Predictive Control, 131–66. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8_6.

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Camacho, Eduardo F., and Carlos Bordons. "Constrained MPC." In Model Predictive Control, 167–207. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8_7.

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Camacho, Eduardo F., and Carlos Bordons. "Robust MPC." In Model Predictive Control, 209–28. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8_8.

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Ławryńczuk, Maciej. "MPC Algorithms." In Studies in Systems, Decision and Control, 1–30. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04229-9_1.

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Fernandez-Camacho, Eduardo, and Carlos Bordons-Alba. "Constrained MPC." In Model Predictive Control in the Process Industry, 129–54. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3008-6_6.

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Fernandez-Camacho, Eduardo, and Camacho Carlos Bordons-Alba. "Robust MPC." In Model Predictive Control in the Process Industry, 155–70. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3008-6_7.

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Holländer, Ulf, Axel Schroeder, and Ulrich Oldehaver. "MPC Capital AG." In Deutsche Standards Beispielhafte Geschäftsberichte, 248–51. Wiesbaden: Gabler Verlag, 2004. http://dx.doi.org/10.1007/978-3-322-99378-6_60.

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Takács, Gergely, and Boris Rohal’-Ilkiv. "Basic MPC Formulation." In Model Predictive Vibration Control, 207–51. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2333-0_6.

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Conference papers on the topic "MPC"

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Liu, Hong, Leibo Liu, Wenping Zhu, Qiang Li, Huiyu Mo, and Shaojun Wei. "L-MPC." In DAC '19: The 56th Annual Design Automation Conference 2019. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3316781.3317854.

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Luo, Yinghui, Xiaoshi Deng, Yilin Wu, and Junhuan Wang. "MPC-DPOS." In ICBTA 2019: 2019 2nd International Conference on Blockchain Technology and Applications. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3376044.3376061.

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Vale, Jonny, Jon Favreau, and Adam Valdez. "MPC presents." In SIGGRAPH '17: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3078280.3101033.

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Humphries, Thomas, Rasoul Akhavan Mahdavi, Shannon Veitch, and Florian Kerschbaum. "Selective MPC." In CCS '22: 2022 ACM SIGSAC Conference on Computer and Communications Security. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3548606.3560559.

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Zeilinger, Melanie N., Colin N. Jones, Davide M. Raimondo, and Manfred Morari. "Real-time MPC - Stability through robust MPC design." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400903.

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Khan, B., and J. A. Rossiter. "Triple mode MPC or laguerre MPC: A comparison." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5990881.

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Lucas, Christoph, Dominik Raub, and Ueli Maurer. "Hybrid-secure MPC." In Proceeding of the 29th ACM SIGACT-SIGOPS symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1835698.1835747.

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Smart, Nigel P. "Masking and MPC." In CCS'16: 2016 ACM SIGSAC Conference on Computer and Communications Security. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2996366.2996372.

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Imsland, L., J. A. Rossiter, B. Pluymers, and J. Suykens. "Robust triple mode MPC." In 2006 American Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/acc.2006.1655467.

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Vale, Jonny. "MPC "Godzilla" VFX breakdown." In SIGGRAPH '15: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2745234.2746838.

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Reports on the topic "MPC"

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Lajoie, John. PHENIX Muon Piston Calorimeter (MPC) APD and Prototype MPC Extension (MPC-EX) Tests. Office of Scientific and Technical Information (OSTI), June 2013. http://dx.doi.org/10.2172/1128068.

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Christelis, Dimitris, Dimitris Georgarakos, Tullio Jappelli, Luigi Pistaferri, and Maarten van Rooij. Wealth Shocks and MPC Heterogeneity. Cambridge, MA: National Bureau of Economic Research, June 2019. http://dx.doi.org/10.3386/w25999.

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Michael A. Bender, Martin Farach-Colton, and Bradley C. Kuszmaul. Final Report: Efficient Databases for MPC Microdata. Office of Scientific and Technical Information (OSTI), August 2011. http://dx.doi.org/10.2172/1048538.

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Ramsey, Monica, Dylan Scott, Charles Weiss, and Jeb Tingle. Development of magnesium phosphate cement (MPC) concrete mixture proportioning for airfield pavements : laboratory and field validation MPC test report. Engineer Research and Development Center (U.S.), February 2020. http://dx.doi.org/10.21079/11681/35475.

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J. Davis. 12 PWR ASSEMBLY MPC WASTE PACKAGE CRITICALITY ANALYSIS. Office of Scientific and Technical Information (OSTI), January 1996. http://dx.doi.org/10.2172/891573.

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Ampudia, Miguel, Russell Cooper, Julia Le Blanc, and Guozhong Zhu. MPC Heterogeneity in Europe: Sources and Policy Implications. Cambridge, MA: National Bureau of Economic Research, September 2018. http://dx.doi.org/10.3386/w25082.

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W.E. Wallin. ANALYSIS OF MPC WEIGHT, DIMENSIONAL ENVELOPE, AND CONFIGURATION REQUIREMENTS. Office of Scientific and Technical Information (OSTI), March 1995. http://dx.doi.org/10.2172/883429.

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W. Wallin. ANALYSIS OF MPC ACCESS REQUIREMENTS FOR ADDITION OF FILLER MATERIALS. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/891532.

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Beck, David Franklin, and Carol L. Stoy-McLeod. MPC&A training needs of the NIS/Baltics States. Office of Scientific and Technical Information (OSTI), July 2006. http://dx.doi.org/10.2172/893149.

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J.K. McCoy. ANALYSIS OF DEGRADATION DUE TO WATER AND GASES IN MPC. Office of Scientific and Technical Information (OSTI), September 1995. http://dx.doi.org/10.2172/862234.

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