Relevant bibliographies by topics / Predictive-Reactive strategy

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Predictive-Reactive strategy'

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Published: 8 June 2024

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Journal articles on the topic "Predictive-Reactive strategy"

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Tighazoui, Ayoub, Christophe Sauvey, and Nathalie Sauer. "Predictive-reactive strategy for identical parallel machine rescheduling." Computers & Operations Research 134 (October 2021): 105372. http://dx.doi.org/10.1016/j.cor.2021.105372.

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Kalinowski, Krzysztof, Damian Krenczyk, and Cezary Grabowik. "Predictive - Reactive Strategy for Real Time Scheduling of Manufacturing Systems." Applied Mechanics and Materials 307 (February 2013): 470–73. http://dx.doi.org/10.4028/www.scientific.net/amm.307.470.

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In this paper a solution of soft real time scheduling in manufacturing systems is presented. The basic requirements of scheduling as a real time system are discussed. The proposed rescheduling method uses predictive-reactive strategy and multi thread searching approach with rule-based heuristics, meta-heuristics and random modules.
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Di, Zhengfei, Demin Xu, and Kehan Zhang. "Continuous Control Set Model Predictive Control for an Indirect Matrix Converter." Energies 14, no. 14 (July 8, 2021): 4114. http://dx.doi.org/10.3390/en14144114.

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A continuous control set model predictive power control strategy for an indirect matrix converter is proposed in this paper. The load reactive power, the load active power, and the input reactive power are controlled simultaneously. This control strategy can obtain output waveforms with fixed switching frequency. Additionally, an optimal switching sequence is proposed to simplify the commutations of the indirect matrix converter. To suppress the input filter resonance, an active damping method is proposed. Experimental results prove that the proposed method features controllable input reactive power, controllable load active and reactive power, fixed switching frequency output waveforms, zero-current switching operations, and effectively suppresses input filter resonance.
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Zhang, Ming Guang, and Xiao Jing Chen. "Control Strategy of Low Voltage Ride-Through for Grid-Connected Photovoltaic Power System Based on Predictive Current." Applied Mechanics and Materials 556-562 (May 2014): 1753–56. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.1753.

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The control strategy based on predictive current is proposed to solve problems that destruct stable operation of grid-connected photovoltaic system during asymmetrical fall. A mathematical model of PV inverter is established to calculate current instruction; a method of tracking based on predictive current is proposed to reduce the fluctuations of 2 times frequency. In the meantime, PV inverter provides reactive power to support voltage recovery according to the depth of grid voltage sags and realize LVRT. The result also shows that the proposed control strategy can reduce wave of DC voltage and provide reactive power to support voltage recovery.
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Nawaz, Muhammad, Muhammad Asghar Saqib, Syed Abdul Rahman Kashif, and Mehr Gul. "Constrained model predictive control for an induction heating load." Transactions of the Institute of Measurement and Control 41, no. 1 (March 28, 2018): 210–18. http://dx.doi.org/10.1177/0142331218758887.

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This paper explores a model predictive control (MPC) strategy with constraints satisfaction for a high power induction heating load. The MPC predicts the state variables and future control sequence of the system in advance and achieves on-line-optimization with a reduced error. The state-space model of the system with a parallel resonant load is developed and then MPC is applied. The proposed approach controls the DC link current at the rectifier output and reactive component of the supply current. The DC current is used to regulate the power of the heating load and the reactive component of input current is kept at zero to attain the unity power factor. The results show that the proposed strategy regulates the power of the heating load, achieves unity power factor at input of the system and handles the variables within the defined constraints effectively.
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MELLAH, Hacene, Amar MAAFA, Hamza SAHRAOUI, Abdelghani YAHIOU, and Houria SMAIL. "Generalized Predictive Control of the Active and Reactive Stator Powers of the DFIG for Wind Energy Generation." Eurasia Proceedings of Science Technology Engineering and Mathematics 26 (December 30, 2023): 295–305. http://dx.doi.org/10.55549/epstem.1409597.

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This paper proposes a Generalized Predictive Control (GPC) strategy for Wind Power Generation (WPG) based on a doubly fed induction generator (DFIG). The objective is to study and apply a robust active and reactive power control strategy based on GPC of the DFIG, this is likely to optimize the energy production and improve the quality of the energy produced. In order to maximize the amount of WPG taken even when the turbine is uncertain or the wind speed varies abruptly, the design is built utilizing the Maximum Power Point Tracking (MPPT) theory. Through numerical modeling with the aid of the Matlab/Simulink software, the predictive control (GPC) of the active and reactive stator powers of the DFIG was validated. A comparison between the GPC of the GADA and the indirect method based on a typical PI controller is developed in order to illustrate the viability of the suggested method. According to the simulation results of this comparison, the suggested method is viable and has promising results.
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Zhu, Jian-hong, Pengkun Zhang, Xinsong Zhang, Lin Qin, Chengxiang Sun, and Han Li. "Model Predictive Control on Transient Flux Linkage and Reactive Power Compensation of Doubly Fed Induction Wind Generator." International Journal of Energy Research 2024 (March 5, 2024): 1–15. http://dx.doi.org/10.1155/2024/6648691.

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For weak grid scenario with high new energy proportion, large fluctuations of load are prone to cause low-voltage ride through. Moreover, stator transient magnetic flux will cause overvoltage and overcurrent problems in the rotor of doubly fed induction generator. Based on model predictive control, a control strategy for transient flux linkage and reactive power compensation is proposed. Firstly, regarding the issue of reactive power allocation of grid side converters (GSC) and rotor side converters (RSC), an allocation strategy is derived under minimizing winding energy loss on case of low-voltage ride through, enabling the wind energy conversion system to provide reactive power support during grid voltage recovery process. Meanwhile, an improved mixed second- and third-order generalized integrator (MSTOGI) phase-locked loop (PLL) is used to extract the positive and negative sequence components of the power grid voltage, further for RSC control. Secondly, in response to power grid faults, considering the influence of stator DC transient flux and negative sequence flux components on rotor current, by injecting rotor transient compensation current and stator flux feedforward compensation into RSC, the rotor impulse voltage and loop current are reduced. Moreover, combined with model predictive control algorithm, a control strategy of rotor current is designed. Finally, a simulation platform is built to validate the effectiveness of the proposed method based on comparing with several traditional vector control low-voltage ride through methods.
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Tighazoui, Ayoub, Christophe Sauvey, and Nathalie Sauer. "Predictive-reactive Strategy for Flowshop Rescheduling Problem: Minimizing the Total Weighted Waiting Times and Instability." Journal of Systems Science and Systems Engineering 30, no. 3 (April 19, 2021): 253–75. http://dx.doi.org/10.1007/s11518-021-5490-8.

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Rahima, Bachar, Golea Amar, Benchouia Mohamed Toufik, and Chebaani Mohamed. "High-performance active power filter implementation based on predictive current control." International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no. 1 (March 1, 2019): 277. http://dx.doi.org/10.11591/ijpeds.v10.i1.pp277-287.

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This paper presents the use of the predictive strategy concept to improve the Active Power Filter (APF) performance, by compensation of the reactive power and elimination of the harmonic currents generated by non-linear loads. Predictive control is generating considerable interest when it comes to implementing current control strategies in active power filter. The proposed strategy provides a simple controller incorporating Phase Locked Loop (PLL) independency. The prediction is evaluated using a cost function that quantifies the desired system behavior. The cost function used in this work evaluates the filtered error of the currents. This strategy minimized the number of sensors, ease of practical implementation and reduced system size and cost. The effectiveness of the proposed controller is confirmed through simulation and experimental validation using a hardware prototype based on dSPACE-1104
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He, Tingting, Dylan Dah-Chuan Lu, Mingli Wu, Qinyao Yang, Teng Li, and Qiujiang Liu. "Four-Quadrant Operations of Bidirectional Chargers for Electric Vehicles in Smart Car Parks: G2V, V2G, and V4G." Energies 14, no. 1 (December 31, 2020): 181. http://dx.doi.org/10.3390/en14010181.

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This paper presents the four-quadrant operation modes of bidirectional chargers for electric vehicles (EVs) framed in smart car parks. A cascaded model predictive control (MPC) scheme for the bidirectional two-stage off-board chargers is proposed. The controller is constructed in two stages. The model predictive direct power control for the grid side is applied to track the active/reactive power references. The model predictive direct current control is proposed to achieve constant current charging/discharging for the EV load side. With this MPC strategy, EV chargers are able to transmit the active and reactive powers between the EV batteries and the power grid. Apart from exchanging the active power, the vehicle-for-grid (V4G) mode is proposed, where the chargers are used to deliver the reactive power to support the grid, simultaneously combined with grid-to-vehicle or vehicle-to-grid operation modes. In the V4G mode, the EV battery functions as the static var compensator. According to the simulation results, the system can operate effectively in the full control regions of the active and reactive power (PQ) plane under the aforementioned operation modes. Fast dynamic response and great steady-state system performances can be verified through various simulation and experimental results.
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Dissertations / Theses on the topic "Predictive-Reactive strategy"

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Tighazoui, Ayoub. "Ré-ordonnancement des systèmes de production flexibles avec contraintes de blocage mixtes soumis à des aléas de commandes ou de production." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0187.

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La transformation numérique que vit l’entreprise aujourd’hui a définitivement changé le comportement des consommateurs. En effet, via les nouveaux outils d’information et de communication, un client peut désormais à tout moment créer, modifier ou annuler une commande. Ces évènements fortuits ont un impact sur l’organisation au sein des unités de production. Ils engendrent une perturbation de l’ordonnancement planifié. Par conséquent, un processus de réordonnancement est nécessaire pour réviser efficacement le planning déjà établi, de préférence avec le moins de modifications possibles. Cette thèse propose des modèles mathématiques et des méthodes d’optimisation pour résoudre des problèmes de réordonnancement, afin de parer des perturbations dans des environnements machines différents, et évaluer la performance des solutions obtenues, grâce à un critère combinant l’efficacité et la stabilité de l’ordonnancement. La stratégie prédictive-réactive a été adoptée dans cette étude. Elle consiste, dans la phase prédictive, à résoudre un problème d’ordonnancement classique ayant comme objectif de minimiser la somme des temps d’attente des jobs pondérés par leurs poids, représentant le critère d’efficacité. Dès l’apparition d’une perturbation, vient ensuite la phase réactive, qui consiste à mettre à jour le problème initial en modifiant ses données, puis à résoudre le nouveau problème de réordonnancement, ayant cette fois comme objectif de minimiser le critère d’efficacité décrit auparavant, combiné avec le critère de stabilité. Ce dernier est défini par la somme des différences entre les dates de fin des jobs avant et après l’apparition de la perturbation, pondérées par le poids des jobs. Cette combinaison des deux critères est significative, et peut être très utile dans des applications industrielles, où le temps d’attente des jobs représente le temps d’attente des produits devant le poste de travail, tandis que les poids des jobs représentent l’importance des clients. La mesure de stabilité permet de limiter la déviation par rapport au planning déjà établi, car celle-ci engendre des coûts supplémentaires. Cette approche a été appliquée à différents environnements de machines, en commençant par une machine unique, représentant un seul poste de travail. Puis, sur des machines parallèles, représentant des postes de travail identiques. Et enfin, dans un atelier de type Flowshop, où l’ensemble des jobs doivent passer sur un ensemble de machines dans le même ordre. Ce dernier cas a aussi été étudié en considérant des contraintes de blocage mixtes entre les machines. Ces différents problèmes de réordonnancement ont été modélisés en premier lieu sous forme de modèles mathématiques, adaptés à une Programmation Linéaire en Nombres Entiers (PLNE). Cependant, leur complexité NP-difficile n’a permis leur résolution que pour un nombre limité de jobs. Par conséquent, des méthodes heuristiques ont été développées, permettant de parcourir plus de jobs en un temps raisonnable. La performance des méthodes développées a été discutée et analysée, à la fois en termes de qualité de solution et de temps de calcul
Today’s digital transformation has definitely changed the customers practices. In fact, through the new information and communication tools, a customer can at any time create, modify, or cancel an order. These unexpected events have a direct impact on the work organization of the production unit, generating a disruption of the already established schedule. Therefore, a rescheduling process is necessary for efficiently revising the existing schedule, preferably with less movements. In this Ph.D, mathematical models and optimization methods are developed for rescheduling problems, under different types of disruptions, in several machine environments. The performance of the obtained solutions is measured with a new criterion, simultaneously combining the schedule efficiency and stability.The predictive-reactive scheduling strategy has been adopted in this work. It consists, in the predictive phase, to solve a classical scheduling problem minimizing the Total Weighted Waiting Time (TWWT) of the jobs, regarded as the schedule efficiency criterion. After the disruption appearance, the reactive phase starts. It consists in updating the initial problem by modifying its data, then solving the new rescheduling problem with the objective of minimizing the TWWT combined with a stability criterion. The schedule stability is measured with the Total Weighted Completion Time Deviation (TWCTD). This association of criteria is significant, and it can be very helpful in industrial applications, where the job waiting time estimates the duration that the job has waited in front of a workstation. The stability criterion is then used for limiting the deviation from the already established schedule, since this matter generates supplementary costs.This approach has been applied for different machine environments. Firstly, on a single machine, illustrating the case of a single workstation. Secondly, on parallel machines, describing the case of identical workstations. Finally, on a flowshop system where a set of jobs are treated in the same order by a set of machines. The flowshop rescheduling problem is also considered with mixed blocking constraints.These rescheduling problems have firstly been modeled as Mixed Integer Linear Programing (MILP) models. Due to their NP-hard complexity, the resolution is only possible for a limited number of jobs. Thus, heuristic methods have been designed, exploring more jobs in a reasonable time. The proposed methods have been discussed and analyzed, both in terms of solution quality and computing time
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Book chapters on the topic "Predictive-Reactive strategy"

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Gao, CaiXia, FuZhong Wang, and ZiYi Fu. "Reactive Power Predictive Compensation Strategy for Heavy DC Hoist." In Proceedings of 2016 Chinese Intelligent Systems Conference, 313–21. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2335-4_30.

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Karthigaiselvan, K., and Rames Chandra Panda. "Implementation of MPC Strategy in Reactive Separation Techniques and Its Benefits: A Demonstration with Natural Gas Sweetening Process." In Model Predictive Control - Theory and Applications [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1001101.

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Model Predictive Control (MPC) is a widely used method that has numerous applications in process industries. In the MPC group of controllers, a clear model is used directly for predicting future plant behavior and calculating corrective control action required to maintain the output at the desired set point value. It is well known that most chemical processes present inherent nonlinearities on account of disturbance, set-point changes.MPC variants based on nonlinear process models have produced stiff control of process along with improved handling of constraints, abnormal dynamics and time delays. One of the variants EMPC applied for various chemical processes have produced economic performance index over the horizon for achieving optimal output targets. In addition to that, adaptive MPC is better in handling the nonlinearity and time varying characteristics during run time by modifying model. The control of reactive separation process is difficult on account of process nonlinearity and interactions of vapor-liquid equilibrium with chemical reactions. Reactive separation is multi -input and multi-output(MIMO) system .In order to obtain the optimal performance, energy conservation and cost effectiveness of MIMO system , the application of optimal controller is inevitable. The application of optimal controller have exhibited better performance compared to tuned linear controller inspite of presence of unknown input delays. The mpc coupled with neural network have exhibited better controllability in case of reactive distillation process. This chapter will cover recent developments in MPC applicable to reactive separation techniques that consist of reactive distillation, reactive absorption, extractive reaction, reactive membrane separation which are used in applications such as LPG processing, natural gas sweetening process etc
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Conference papers on the topic "Predictive-Reactive strategy"

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Chen, Tianhua, Jianhua Chen, Zhaoyang Yan, Yifeng Li, Yun Chai, and Lei Du. "Voltage Control Strategy Considering Dynamic Reactive Reserve Margin Under Predictive Fault." In 2021 IEEE Sustainable Power and Energy Conference (iSPEC). IEEE, 2021. http://dx.doi.org/10.1109/ispec53008.2021.9735982.

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Gogov, Bogdancho. "PREDICTIVE POLICING." In SECURITY HORIZONS. Faculty of Security- Skopje, 2023. http://dx.doi.org/10.20544/icp.8.1.23.p20.

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Crime control strategies in the past have largely been focused on reactive tactics, while the focus of policing was to respond quickly to incidents and crimes. However, as the crime and security situation changed, so did the paradigm shift from a reactive style of policing to proactive policing. Proactive work aims to prevent crime rather than just react to it. It has been shown that crime prevention is more closely related to proactive policing than to reactive policing. Crime prevention strategies such as community-oriented policing, problem-oriented policing, intelligence-led policing were introduced with having in mind proactive policing. In recent decades, a new proactive data-driven policing strategy has emerged, namely predictive policing. It uses information technology, data and analytical techniques in order to identify the most likely places and times of future criminal events or persons at high risk of committing or becoming victims of a crime. The use of predictive analytics and machine learning has attracted enormous attention, linking predictive policing with digital innovation. Although it can be argued that data collection and processing has always been an important aspect of policing, technological advances and the increased availability of police data have led to a shift from predominantly reactive policing to proactive policing. It should be emphasized that predictive policing is not intended to replace the already tried and tested proactive policing techniques such as evidence-based policing. Improvements to traditional proactive policing techniques such as machine learning and sophisticated algorithms are enabling the police to track both individuals and areas with greater accuracy in order to predict when, where and by whom a crime may be committed.
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Rojas, Diego, Marco Rivera, Sergio Toledo, and Patrick Wheeler. "Reactive Power Control Using a Model-Based Predictive Control Strategy Applied to an Indirect Matrix Converter." In 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA). IEEE, 2021. http://dx.doi.org/10.1109/icaacca51523.2021.9465205.

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Tonellato, Giulio, Michaël Kummert, José Candanedo, Gabrielle Beaudry, and Philippe Pasquier. "An exploration of a heuristic predictive control strategy for ground source heat pumps coupled to standing column wells." In International Ground Source Heat Pump Association. International Ground Source Heat Pump Association, 2024. http://dx.doi.org/10.22488/okstate.24.000033.

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Standing column wells (SCW) have shown to be a cost-effective hybrid ground heat exchanger (GHE) configuration suitable for high-density urban areas as they benefit from groundwater advection while not requiring particularly productive aquifers. However, operating SCWs in cold climates can represent a control challenge since the groundwater can approach freezing conditions while recirculating; on the other hand, submersible pumps can cause a significant power usage. This article explores the potential of a heuristic predictive pumping control strategy built with an accurately calibrated white-box model of a real case study in Mirabel, Quebec, Canada. Results show that it can be used to both contain pumping power and safely operate the SCWs without using an electric boiler as anti-freezing protection. In an exceptionally cold month, the predictive control strategy effectively prevents the boiler intervention, resulting in a 3.25 times reduction in peak power, with a slight increase in energy use (5.75%). In less extreme winter weather, the energy usage difference will be smaller, but the reactive control will likely use the boiler whenever the weather gets particularly cold. This study provides the basis for the development of predictive control rules that may be easily implemented in similar configurations.
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CASTRO, ALLAN GREGORI DE, PAULO ROBERTO UBALDO GUAZZELLI, STEFAN THIAGO CURY ALVES DOS SANTOS, WILLIAM CéSAR DE ANDRADE PEREIRA, CARLOS MATHEUS RODRIGUES DE OLIVEIRA, GEYVERSON TEIXEIRA DE PAULA, and JOSé ROBERTO BOFFINO DE ALMEIDA MONTEIRO. "Finite Control-Set Model Predictive Torque Control of Nonsinusoidal PMSM: a Generalized Approach for Torque Ripple Mitigation and MTPA Operation." In Seminar on Power Electronics and Control (SEPOC 2021). sepoc, 2021. http://dx.doi.org/10.53316/sepoc2021.024.

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Permanent Magnet Synchronous Motors (PMSMs) may present spatial harmonics depending on the design guidelines or imprecisions on the manufacturing process. The interaction of conventional sinusoidal current feeding strategies with these spatial harmonics can produce a considerable torque ripple. This paper deals with a modified Finite Control-Set Model Predictive Torque Control (FCS-MPTC) loop as an active torque ripple minimization solution for PMSMs with spatial harmonics. The proposed approach designs a novel cost function, based on the cross product reactive instantaneous power theory. The benefits of the proposed generalized approach include providing smooth torque production and the Maximum Torque per Ampère (MTPA) operation on PMSMs with a number of spatial harmonic sources, including those on zero-sequence. The effectiveness of the presented control strategy is demonstrated comparatively to conventional sinusoidal current feeding strategy on a PMSM drive employing a three-phase four-leg two level voltage source inverter under both steady and transient state.
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Romdhanne, Bilel Ben, Mourad Boudia, and Nicolas Bondoux. "Amadeus Migration Process a Simulation-Driven Process to Enhance the Migration to a Multi-Cloud Environment." In 12th International Conference on Digital Image Processing and Vision. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131308.

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With the development of the cloud offers, we observe a prominent trend of applications being migrated from private infrastructure to the cloud. Depending on the application’s complexity, the migration can be complex and needs to consider several dimensions, such as dependency issues, service continuity, and the service level agreement (SLA). Amadeus, the travel industry leader, had partnered with Microsoft to migrate its IT ecosystem to the Azure cloud. This work addresses the specificity of cloud-to-cloud migration and the multi-cloud constraints. In this paper, we summarise the Amadeus Migration process. The process aims to drive the migration from an initial private cloud environment to a target environment that can be a public or hybrid cloud. Further, the process focuses on a prediction phase that guides the migration process. This paper expects to provide an efficient decision-making process that guides managers and architects to optimise and secure their migration process while considering micro-servicesoriented applications targeting an efficient deployment over multi-cloud or hybrid cloud. The prediction relies on the network simulation to predict applications’ behaviour in the cloud and evaluate different scenarios and deployment topologies beforehand. The objective is to predict migrated applications’ behaviour and identify any issue related to the performance, the application’s dependency on other components, or the deployment in the cloud. The migration process proposed in this paper relies on SimGrid, a toolkit developed by INRIA[52] for distributed application modelling. This framework offers a generic process to model IT infrastructure and can assist cloud-to-cloud migration. Specific attention is given to predictive and reactive optimisations. The first results show predictive optimisation's impact on securing KPI and reactive optimisation to optimise the solution cost. Thus, we reach an average cost reduction of 40% in comparaison with the same deployment strategy while keeping the same SLA.
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Cateriano Yáñez, Carlos, Jörg Richter, Georg Pangalos, Gerwald Lichtenberg, and Javier Sanchís Saez. "Active Power Filter Shape Class Model Predictive Controller tuning by Multiobjective Optimization." In CARPE Conference 2019: Horizon Europe and beyond. Valencia: Universitat Politècnica València, 2019. http://dx.doi.org/10.4995/carpe2019.2019.10166.

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As the share of renewable energy sources (RES) in distribution grids increases, several power quality challenges arise. Due to its intermittent nature, RES lead to voltage and frequency fluctuations in the grid that affect power quality. Moreover, as RES are connected via power converters, there is also a higher harmonic distortion pollution introduced by the switching power electronics involved, (Liang, 2017). A proven solution is the implementation of Active Power Filters (APF), which are able to compensate the unbalanced, harmonic, and reactive components of a load under different supply conditions. In order to achieve the desired compensation characteristics, the selection of an appropriate control strategy is critical, (Kumar & Mishra, 2016). Classic APF control strategies achieve said goals, although with struggles under changing load scenarios with limitations on their operational modes, (Weihe, Cateriano Yáñez, Pangalos, & Lichtenberg, 2018).This paper proposes the use of an advanced model-based control method, i.e. Model Predictive Control (MPC), to improve the performance of APF devices. Model-based control methods allow for better performance when the model of the plant is known before hand or through measurements, the MPC extends this further by introducing a cost function that ensures optimal operation even under constraints, (Maciejowski, 2002). References Kumar, P., & Mishra, M. K. (2016). A comparative study of control theories for realizing APFs in distribution power systems. 2016 National Power Systems Conference (NPSC), 1–6. https://doi.org/10.1109/NPSC.2016.7858905 Liang, X. (2017). Emerging Power Quality Challenges Due to Integration of Renewable Energy Sources. IEEE Transactions on Industry Applications, 53(2), 855–866. https://doi.org/10.1109/TIA.2016.2626253 Maciejowski, J. M. (2002). Predictive Control with Constraints. Pearson education. Weihe, K., Cateriano Yáñez, C., Pangalos, G., & Lichtenberg, G. (2018, July). Comparison of Linear State Signal Shaping Model Predictive Control with Classical Concepts for Active Power Filter Design. 167–174. Retrieved from http://www.scitepress.org/PublicationsDetail.aspx?ID=QatbWGUbqSE=&t=1
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Fard, Hamed Javaheri, Hamid Reza Najafi, and Ghodratollah Heidari. "Design of discrete predictive direct power control strategy on the doubly-fed induction generator based on Micro-Hydro Power Plant with the aim of active and reactive powers control." In 2016 21st Conference on Electrical Power Distribution Networks Conference (EPDC). IEEE, 2016. http://dx.doi.org/10.1109/epdc.2016.7514794.

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Sivanuja, T., and YG Sandanayake. "Industry 4.0 enabled predictive maintenance of facilities: A study on applicability, benefits and challenges." In 10th World Construction Symposium. Building Economics and Management Research Unit (BEMRU), University of Moratuwa, 2022. http://dx.doi.org/10.31705/wcs.2022.47.

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Maintenance management is an important function under Facilities Management (FM). Industries moved to preventive maintenance models to counteract the inefficiencies of reactive maintenance and further evolved into predictive maintenance (PdM) models. The demand for Industry 4.0 enabled PdM for FM has risen as a result of the industrial revolution and the dynamic nature of the FM functions. Thus, the study aimed to investigate the applicability, benefits, and challenges of applying Industry 4.0 concept for effective PdM in FM. The qualitative research approach was undertaken to accomplish the aim. A comprehensive literature review followed by 15 semi-structured interviews was carried out with experts in the maintenance sector who have Industry 4.0 knowledge. The data was collected from experts in Australia, Qatar, Dubai, Singapore, and Sri Lanka, and analysed through code-based content analysis using NVivo 12. The results demonstrate that there is a huge potential for using Industry 4.0 smart technologies such as big data analytics, Cyber-Physical Systems (CPS), autonomous robots, Cloud Computing, Industrial Internet of Things (IIoT), cybersecurity, Machine Learning (ML), Augmented Reality (AR), Data Mining (DM), system integration, and simulation for PdM under FM. Applying Industry 4.0 concept for effective PdM under FM provides significant benefits such as the deployment of a zero-failure strategy, establishment of machine-to-machine communication and interaction, detection of early anomalies and extended equipment lifetime. Lack of technological knowledge, capital, data management, employees’ interest, integration between systems, standardized procedures, and internet access are identified key challenges.
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Zhong, Qian, and Ronald W. Yeung. "Performance of a Wave-Energy-Converter Array Operating Under Model-Predictive Control Based on a Convex Formulation." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-78739.

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Economics decision drives the operation of ocean-wave energy converters (WEC) to be in a “farm mode”. Control strategy developed for a WEC array will be of high importance for improving the aggregate energy extraction efficiency of the whole system. Model-predictive control (MPC) has shown its strong potential in maximizing the energy output in devices with hard constraints on operation states and machinery inputs (See Ref. [1–3]). Computational demands for using MPC to control an array in real time can be prohibitive. In this paper, we formulate the MPC to control an array of heaving point absorbers, by recasting the optimization problem for energy extraction into a convex Quadratic Programming (QP) problem, the solution of which can be carried out very efficiently. Large slew rates are to be penalized, which can also guarantee the convexity of the QP and improve the computational efficiency for achieving the optimal solution. Constraints on both the states and the control input can be accommodated in this MPC method. Full hydro-dynamic interference effects among the WEC array components are taken into account using the theory developed in [4]. Demonstrative results of the application are presented for arrays of two, three, and four point absorbers operating at different incident-wave angles. Effects of the interacting waves on power performance of the array under the new MPC control are investigated, with simulations conducted in both regular and irregular seas. Heaving motions of individual devices at their optimal conditions are shown. Also presented is the reactive power required by the power takeoff (PTO) system of the array to achieve optimality. We are pleased to contribute this article in celebration of our collegiality with Professor Bernard Molin on the occasion of his honoring symposium.
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