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Energies
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Model Predictive Control System Design and Implementation
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Model Predictive Control System Design and Implementation

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 22660

Special Issue Editors

Prof. Dr. José María Maestre

E-Mail Website1 Website2
Guest Editor
Systems and Automation Engineering Department, University of Seville, 41004 Sevilla, Spain
Interests: systems and control; model predictive control; water management; smart home; clustering
Prof. Dr. Carlos Bordons

E-Mail Website
Guest Editor
Laboratory of Engineering for Energy and Environmental Sustainability, University of Seville, 41004 Sevilla, Spain
Interests: AC/DC microgrid control; microgrids; model-predictive control
Special Issues, Collections and Topics in MDPI journals
Dr. Juan Manuel Escaño

E-Mail Website
Guest Editor
Systems and Automation Engineering Department, University of Seville, 41004 Sevilla, Spain
Interests: systems and control; model predictive control; neurofuzzy systems; fault-tolerant systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Almost half a century has passed since the very first works proposing a predictive approach appeared, and yet the model predictive control (MPC) framework seems as fresh as new, with literally hundreds of contributions made every year to enlarge this rich family of controllers. Indeed, MPC has arguably become one of the most successful advanced control methods due to its capacity to systematically integrate issues such as multiple inputs and outputs, optimization goals, constraints, and dead times, to name a few of its advantages. Being a computer-based approach, it is directly fueled by the constant and pervasive advances of information and communication technologies, which allow MPC to be applied in systems that were beyond the scope just a few years ago.

This Special Issue aims to compile advances regarding the design and implementation of predictive controllers in systems where energy plays a central role. From power converters to solar plants, from electricity networks to hydropower generation, there are myriad applications where MPC and energy are entwined, perhaps as part of a larger system. For this reason, this Special Issue aims to bring together contributions regarding topics such as:

  • Centralized, hierarchical, and distributed MPC methods in energy systems.
  • Design and implementation of flexible MPC methods in energy systems (e.g., clustering, coalitional, and plug and play).
  • Fast methods for implementing MPC in energy systems.
  • Cybersecurity considerations in the design of MPC controllers for energy systems.
  • Collaborative MPC-based approaches in industrial energy management.
  • Renewable power and its management by predictive controllers.
  • Learning strategies for MPC controllers in energy systems.
  • Advances in forecasting and nowcasting for MPC in energy systems.
  • MPC-based methods to flatten the demand in energy systems.
  • Data-driven fault detection, diagnosis, and prognosis solutions for energy systems.
  • Smart technology and IoT applications involving MPC and energy.

Other topics related to MPC and energy could also be of interest, so do not hesitate to contact any of the Guest Editors of the Special Issue if you believe that you have a work worth publishing in this regard.

Prof. Dr. José María Maestre
Prof. Dr. Carlos Bordons
Dr. Juan Manuel Escaño
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • model predictive control
  • flexible control methods
  • energy systems
  • collaborative systems
  • cyber security
  • data-driven MPC

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Published Papers (9 papers)

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Research

13 pages, 806 KiB  
Article
Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller
by Raymundo Cordero, Thyago Estrabis, Gabriel Gentil, Matheus Caramalac, Walter Suemitsu, João Onofre, Moacyr Brito and Juliano dos Santos
Energies 2022, 15(15), 5664; https://doi.org/10.3390/en15155664 - 4 Aug 2022
Cited by 4 | Viewed by 1537
Abstract
Some novel applications require the tracking/rejection of biased sinusoidal reference/distur-bances. According to the internal model principle (IMP), a controller must embed the model of a biased sinusoidal signal to track references and also reject perturbations modeled through the aforementioned signal. However, the design [...] Read more.
Some novel applications require the tracking/rejection of biased sinusoidal reference/distur-bances. According to the internal model principle (IMP), a controller must embed the model of a biased sinusoidal signal to track references and also reject perturbations modeled through the aforementioned signal. However, the design of that kind of controller is not straightforward, especially when they are implemented in digital processors. This paper presents a controller, based on generalized predictive control (GPC), designed for tracking/rejection of biased sinusoidal signals. In general, GPC is based on the prediction of the plant responses through an augmented prediction model. The proposed approach develops an augmented model that predicts the future errors. The prediction model and the control law used in the proposed approach embed the discrete-time model of a biased sinusoidal signal. Thus, the proposed controller can track/reject biased sinusoidal references/disturbances. The predicted errors and the future inputs of the proposed augmented model are used to define the cost function that measures the control performance. An optimization technique was applied to obtain the solution of the cost function, which is the optimal sequence of future model inputs that allows defining the control law. Experimental tests prove that the proposed controller can asymptotically track and reject biased sinusoidal signals. Full article
(This article belongs to the Special Issue Model Predictive Control System Design and Implementation)
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21 pages, 2681 KiB  
Article
Initialisation of Optimisation Solvers for Nonlinear Model Predictive Control: Classical vs. Hybrid Methods
by Maciej Ławryńczuk, Piotr M. Marusak, Patryk Chaber and Dawid Seredyński
Energies 2022, 15(7), 2483; https://doi.org/10.3390/en15072483 - 28 Mar 2022
Cited by 6 | Viewed by 2090
Abstract
In nonlinear Model Predictive Control (MPC) algorithms, the number of cost-function evaluations and the resulting calculation time depend on the initial solution to the nonlinear optimisation task. Since calculations must be performed fast on-line, the objective is to minimise these indicators. This work [...] Read more.
In nonlinear Model Predictive Control (MPC) algorithms, the number of cost-function evaluations and the resulting calculation time depend on the initial solution to the nonlinear optimisation task. Since calculations must be performed fast on-line, the objective is to minimise these indicators. This work discusses twelve initialisation strategies for nonlinear MPC. In general, three categories of strategies are discussed: (a) five simple strategies, including constant and random guesses as well as the one based on the previous optimal solution, (b) three strategies that utilise a neural approximator and an inverse nonlinear static model of the process and (c) four hybrid original methods developed by the authors in which an auxiliary quadratic optimisation task is solved or an explicit MPC controller is used; in both approaches, linear or successively linearised on-line models can be used. Efficiency of all methods is thoroughly discussed for a neutralisation reactor benchmark process and some of them are evaluated for a robot manipulator, which is a multivariable process. Two strategies are found to be the fastest and most robust to model imperfections and disturbances acting on the process: the hybrid strategy with an auxiliary explicit MPC controller based on a successively linearised model and the method which uses the optimal solution obtained at the previous sampling instant. Concerning the hybrid strategies, since a simplified model is used in the auxiliary controller, they perform much better than the approximation-based ones with complex neural networks. It is because the auxiliary controller has a negative feedback mechanism that allows it to compensate model errors and disturbances efficiently. Thus, when the auxiliary MPC controller based on a successively linearised model is available, it may be successfully and efficiently used for the initialisation of nonlinear MPC, whereas quite sophisticated methods based on a neural approximator are very disappointing. Full article
(This article belongs to the Special Issue Model Predictive Control System Design and Implementation)
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23 pages, 9921 KiB  
Article
Flexible Matrix of Controllers for Real Time Parallel Control
by Patryk Chaber and Andrzej Wojtulewicz
Energies 2022, 15(5), 1833; https://doi.org/10.3390/en15051833 - 2 Mar 2022
Cited by 2 | Viewed by 1900
Abstract
This work aims to develop a novel system, including software and hardware, to perform independent control tasks in a genuine parallel manner. Currently, to control processes with various sampling periods, distributed control systems are most commonly utilized. The main goal of this system [...] Read more.
This work aims to develop a novel system, including software and hardware, to perform independent control tasks in a genuine parallel manner. Currently, to control processes with various sampling periods, distributed control systems are most commonly utilized. The main goal of this system is to propose an alternative solution, which allows simultaneous control of both fast and slow processes. The presented approach utilizes FPGA (Field Programmable Gate Array) with Nios II processor (Intel Soft Processor Series) to implement and maintain instances of independent controllers. Instances can implement FDMC (Fast Dynamic Matrix Control) and PID (Proportional-Integral-Derivative) control algorithms with various sampling times. The FPGA-based design allows for true independence of controllers’ execution both from one another and the managing processor. Also, pure parallel execution allows for implementing slow and fast controllers in the same device. The complete flexible system with a matrix of controllers working in parallel in real-time was tested with both simulated and actual control processes (servomotor), yielding the same results as fully simulated experiments. Full article
(This article belongs to the Special Issue Model Predictive Control System Design and Implementation)
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13 pages, 3939 KiB  
Article
How Much Energy Do We Need to Fly with Greater Agility? Energy Consumption and Performance of an Attitude Stabilization Controller in a Quadcopter Drone: A Modified MPC vs. PID
by Michał Okulski and Maciej Ławryńczuk
Energies 2022, 15(4), 1380; https://doi.org/10.3390/en15041380 - 14 Feb 2022
Cited by 9 | Viewed by 2217
Abstract
Increasing demand for faster and more agile Unmanned Aerial Vehicles (UAVs, drones) is observed in many scenarios, including but not limited to medical supply or Search-and-Rescue (SAR) missions. Exceptional maneuverability is critical for robust obstacle avoidance during autonomous flights. A novel modification to [...] Read more.
Increasing demand for faster and more agile Unmanned Aerial Vehicles (UAVs, drones) is observed in many scenarios, including but not limited to medical supply or Search-and-Rescue (SAR) missions. Exceptional maneuverability is critical for robust obstacle avoidance during autonomous flights. A novel modification to the Model Predictive Controller (MPC) is proposed, which drastically improves the speed of the attitude controller of our quadcopter drone. The modified MPC is suitable for the onboard microcontroller and the 400 Hz main control loop. The peak and total energy consumption and the performance of the attitude controllers are assessed: the modified MPC and the default Proportional-Integral-Derivative (PID). The tests were conducted in a custom-implemented Flight Mode in the ArduCopter software stack, securing the drone in a test harness, which guarantees the experiments are repetitive. The ultimate MPC greatly increases maneuverability of the drone and may inspire more research related to faster obstacle avoidance and new types of hybrid attitude controllers to balance the agility and the power consumption. Full article
(This article belongs to the Special Issue Model Predictive Control System Design and Implementation)
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22 pages, 2927 KiB  
Article
Long-Horizon Nonlinear Model Predictive Control of Modular Multilevel Converters
by Victor Daniel Reyes Dreke and Mircea Lazar
Energies 2022, 15(4), 1376; https://doi.org/10.3390/en15041376 - 14 Feb 2022
Cited by 6 | Viewed by 1756
Abstract
Modular Multilevel Converters (MMCs) are a topology that can scale several voltage levels to obtain higher efficiency and lower harmonics than most voltage-source converters. MMCs are very attractive for renewable energy applications and fast charging stations for electric vehicles, where they can improve [...] Read more.
Modular Multilevel Converters (MMCs) are a topology that can scale several voltage levels to obtain higher efficiency and lower harmonics than most voltage-source converters. MMCs are very attractive for renewable energy applications and fast charging stations for electric vehicles, where they can improve performance and reduce costs. However, due to the complex architecture and the large number of submodules, the current control of modular multilevel converters is a challenging task. The standard solution in practice relies on hierarchical decoupling and single-input-single-output control loops, which are limited in performance. Linearization-based model predictive control was already proposed for current control in MMCs, as it can optimize transient response and better handle constraints. In this paper, we show that the validity of linear MMC models significantly limits the prediction horizon length, and we propose a nonlinear MPC (NMPC) solution for current control in MMCs to solve this issue. With NMPC, we can employ long prediction horizons up to 100 compared to a horizon of 10, which is the limit for the prediction range of a linear MMC model. Additionally, we propose an alternative MMC prediction model and corresponding cost function, which enables directly controlling the circulating current and improves the capacitor voltages’ behavior. Using the state-of-the-art in sequential quadratic programming for NMPC, we show that the developed NMPC algorithm can meet the real-time constraints of MMCs. A performance comparison with a time-varying linearization-based MPC for an MMC topology used in ultra-fast charging stations for electric vehicles illustrates the benefits of the developed approach. Full article
(This article belongs to the Special Issue Model Predictive Control System Design and Implementation)
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14 pages, 3977 KiB  
Article
Ergonomic and Economic Office Light Level Control
by Maria Ghita, Ricardo A. Cajo Diaz, Isabela R. Birs, Dana Copot and Clara M. Ionescu
Energies 2022, 15(3), 734; https://doi.org/10.3390/en15030734 - 19 Jan 2022
Cited by 4 | Viewed by 2405
Abstract
Light regulation systems in industrial or office buildings play an important role in minimizing the use of fossil energy resources, while providing both economic and ergonomic optimal functionality. Although industrial buildings resolve the problem of interaction or disturbance mitigation by providing constant light [...] Read more.
Light regulation systems in industrial or office buildings play an important role in minimizing the use of fossil energy resources, while providing both economic and ergonomic optimal functionality. Although industrial buildings resolve the problem of interaction or disturbance mitigation by providing constant light levels exclusively from artificial sources, office landscapes may benefit from up to a 20% decrease in costs if mixed light sources are optimized properly. In this paper, we propose a theoretical framework based on model predictive control (MPC) to resolve a multi-system with strong dynamic interactions and multi-objective cost optimization. Centralized and distributed predictive control strategies are compared on various office landscaping structures and functionality conditions. Economic and ergonomic indexes are evaluated in a scaled laboratory setting. Full article
(This article belongs to the Special Issue Model Predictive Control System Design and Implementation)
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30 pages, 14773 KiB  
Article
Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control
by Stefano Dettori, Alessandro Maddaloni, Filippo Galli, Valentina Colla, Federico Bucciarelli, Damaso Checcacci and Annamaria Signorini
Energies 2021, 14(13), 3998; https://doi.org/10.3390/en14133998 - 2 Jul 2021
Cited by 7 | Viewed by 3605
Abstract
The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which [...] Read more.
The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up the start-up of steam turbines and increase the energy produced while maintaining rotor stress as a constraint variable. A soft sensor for the online calculation of rotor stress is presented together with the steam turbine control logic. Then, we present how the computational cost of the controller was contained by reducing the order of the formulation of the optimization problem, adjusting the scheduling of the optimizer routine, and tuning the parameters of the controller itself. The performance of the control system has been compared with respect to the PI Controller architecture fed by the soft sensor results and with standard pre-calculated curves. The control architecture was evaluated in a simulation exploiting actual data from a Concentrated Solar Power Plant. The NMPC technique shows an increase in performance, with respect to the custom PI control application, and encouraging results. Full article
(This article belongs to the Special Issue Model Predictive Control System Design and Implementation)
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18 pages, 7305 KiB  
Article
A Novel Optimal Charging Algorithm for Lithium-Ion Batteries Based on Model Predictive Control
by Guan-Jhu Chen, Yi-Hua Liu, Yu-Shan Cheng and Hung-Yu Pai
Energies 2021, 14(8), 2238; https://doi.org/10.3390/en14082238 - 16 Apr 2021
Cited by 15 | Viewed by 3691
Abstract
Lithium-ion (Li-ion) batteries play a substantial role in portable consumer electronics, electric vehicles and large power energy storage systems. For Li-ion batteries, developing an optimal charging algorithm that simultaneously takes rises in charging time and charging temperature into account is essential. In this [...] Read more.
Lithium-ion (Li-ion) batteries play a substantial role in portable consumer electronics, electric vehicles and large power energy storage systems. For Li-ion batteries, developing an optimal charging algorithm that simultaneously takes rises in charging time and charging temperature into account is essential. In this paper, a model predictive control-based charging algorithm is proposed. This study uses the Thevenin equivalent circuit battery and transforms it into the state-space equation to develop the model predictive controller. The usage of such models in the battery optimal control context has an edge due to its low computational cost, enabling the realization of the proposed technique using a low-cost Digital Signal Processor (DSP). Compared with the widely employed constant current-constant voltage charging method, the proposed charging technique can improve the charging time and the average temperature by 3.25% and 0.76%, respectively. Full article
(This article belongs to the Special Issue Model Predictive Control System Design and Implementation)
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19 pages, 832 KiB  
Article
A Coalitional Model Predictive Control for the Energy Efficiency of Next-Generation Cellular Networks
by Eva Masero, Luis A. Fletscher and José M. Maestre
Energies 2020, 13(24), 6546; https://doi.org/10.3390/en13246546 - 11 Dec 2020
Cited by 9 | Viewed by 2117
Abstract
Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a [...] Read more.
Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates). Full article
(This article belongs to the Special Issue Model Predictive Control System Design and Implementation)
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