Special Issue "Model Predictive Control: Algorithms and Applications"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 December 2020).

Special Issue Editor

Prof. Dr. Maciej Ławryńczuk
E-Mail Website
Guest Editor
Institute of Control and Computation Engineering, Warsaw, University of Technology, 00-665 Warsaw, Poland
Interests: advanced process control; model predictive control; setpoint optimization; soft computing; artificial intelligence
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Model predictive control (MPC) is an advanced control method which makes it possible to effectively control multivariable and nonlinear processes subject to constraints. MPC is not only an active area of research, but also has a great number of applications in different fields. MPC has been traditionally used in process control, but due to the availability of powerful microcontrollers, MPC algorithms have become increasingly popular in fast embedded systems, e.g., automotive applications, drones, quadcopters, and robots.

The aim of this Special Issue is to present state-of-the-art algorithms used in MPC and report interesting applications of MPC in different fields. The possible topics of interest include, but are not limited to, the following areas:

Algorithms

Control quality assessment of MPC
Data-driven MPC
Economic MPC
Embedded MPC
Hierarchical and decentralized MPC
IT solutions for MPC
Large-scale MPC
Learning MPC
MPC in IoT systems
Online learning in MPC
Optimization algorithms for MPC
Setpoint optimization in MPC
Stability and robustness of MPC
Stochastic MPC

Applications

Applications of MPC in automotive systems
Applications of MPC in energy systems
Applications of MPC in heating, ventilation, air conditioning (HVAC) systems
Applications of MPC in industrial process control

Dr. Maciej Ławryńczuk
Guest Editor

Manuscript Submission Information

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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. Algorithms is an international peer-reviewed open access monthly 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 1400 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
  • computational algorithms for MPC
  • applications of MPC

Published Papers (8 papers)

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Research

Open AccessArticle
Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms
Algorithms 2021, 14(1), 25; https://0-doi-org.brum.beds.ac.uk/10.3390/a14010025 - 17 Jan 2021
Viewed by 538
Abstract
A method for the advanced construction of the dynamic matrix for Model Predictive Control (MPC) algorithms with linearization is proposed in the paper. It extends numerically efficient fuzzy algorithms utilizing skillful linearization. The algorithms combine the control performance offered by the MPC algorithms [...] Read more.
A method for the advanced construction of the dynamic matrix for Model Predictive Control (MPC) algorithms with linearization is proposed in the paper. It extends numerically efficient fuzzy algorithms utilizing skillful linearization. The algorithms combine the control performance offered by the MPC algorithms with nonlinear optimization (NMPC algorithms) with the numerical efficiency of the MPC algorithms based on linear models in which the optimization problem is a standard, easy-to-solve, quadratic programming problem with linear constraints. In the researched algorithms, the free response obtained using a nonlinear process model and the future trajectory of the control signals is used to construct an advanced dynamic matrix utilizing the easy-to-obtain fuzzy model. This leads to obtaining very good prediction and control quality very close to those offered by NMPC algorithms. The proposed approach is tested in the control system of a nonlinear chemical control plant—a CSTR reactor with the van de Vusse reaction. Full article
(This article belongs to the Special Issue Model Predictive Control: Algorithms and Applications)
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Open AccessArticle
Tuning of Multivariable Model Predictive Control for Industrial Tasks
Algorithms 2021, 14(1), 10; https://0-doi-org.brum.beds.ac.uk/10.3390/a14010010 - 03 Jan 2021
Cited by 1 | Viewed by 798
Abstract
This work is concerned with the tuning of the parameters of Model Predictive Control (MPC) algorithms when used for industrial tasks, i.e., compensation of disturbances that affect the process (process uncontrolled inputs and measurement noises). The discussed simulation optimisation tuning procedure is quite [...] Read more.
This work is concerned with the tuning of the parameters of Model Predictive Control (MPC) algorithms when used for industrial tasks, i.e., compensation of disturbances that affect the process (process uncontrolled inputs and measurement noises). The discussed simulation optimisation tuning procedure is quite computationally simple since the consecutive parameters are optimised separately, and it requires only a very limited number of simulations. It makes it possible to perform a multicriteria control assessment as a few control quality measures may be taken into account. The effectiveness of the tuning method is demonstrated for a multivariable distillation column. Two cases are considered: a perfect model case and a more practical case in which the model is characterised by some error. It is shown that the discussed tuning approach makes it possible to obtain very good control quality, much better than in the most common case in which all tuning parameters are constant. Full article
(This article belongs to the Special Issue Model Predictive Control: Algorithms and Applications)
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Open AccessArticle
A New Click-Through Rates Prediction Model Based on Deep&Cross Network
Algorithms 2020, 13(12), 342; https://0-doi-org.brum.beds.ac.uk/10.3390/a13120342 - 14 Dec 2020
Viewed by 707
Abstract
With the development of E-commerce, online advertising began to thrive and has gradually developed into a new mode of business, of which Click-Through Rates (CTR) prediction is the essential driving technology. Given a user, commodities and scenarios, the CTR model can predict the [...] Read more.
With the development of E-commerce, online advertising began to thrive and has gradually developed into a new mode of business, of which Click-Through Rates (CTR) prediction is the essential driving technology. Given a user, commodities and scenarios, the CTR model can predict the user’s click probability of an online advertisement. Recently, great progress has been made with the introduction of Deep Neural Networks (DNN) into CTR. In order to further advance the DNN-based CTR prediction models, this paper introduces a new model of FO-FTRL-DCN, based on the prestigious model of Deep&Cross Network (DCN) augmented with the latest optimization technique of Follow The Regularized Leader (FTRL) for DNN. The extensive comparative experiments on the iPinYou datasets show that the proposed model has outperformed other state-of-the-art baselines, with better generalization across different datasets in the benchmark. Full article
(This article belongs to the Special Issue Model Predictive Control: Algorithms and Applications)
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Open AccessArticle
The Model Order Reduction Method as an Effective Way to Implement GPC Controller for Multidimensional Objects
Algorithms 2020, 13(8), 178; https://0-doi-org.brum.beds.ac.uk/10.3390/a13080178 - 23 Jul 2020
Cited by 2 | Viewed by 1298
Abstract
The paper addresses issues associated with implementing GPC controllers in systems with multiple input signals. Depending on the method of identification, the resulting models may be of a high order and when applied to a control/regulation law, may result in numerical errors due [...] Read more.
The paper addresses issues associated with implementing GPC controllers in systems with multiple input signals. Depending on the method of identification, the resulting models may be of a high order and when applied to a control/regulation law, may result in numerical errors due to the limitations of representing values in double-precision floating point numbers. This phenomenon is to be avoided, because even if the model is correct, the resulting numerical errors will lead to poor control performance. An effective way to identify, and at the same time eliminate, this unfavorable feature is to reduce the model order. A method of model order reduction is presented in this paper that effectively mitigates these issues. In this paper, the Generalized Predictive Control (GPC) algorithm is presented, followed by a discussion of the conditions that result in high order models. Examples are included where the discussed problem is demonstrated along with the subsequent results after the reduction. The obtained results and formulated conclusions are valuable for industry practitioners who implement a predictive control in industry. Full article
(This article belongs to the Special Issue Model Predictive Control: Algorithms and Applications)
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Open AccessArticle
Numerically Efficient Fuzzy MPC Algorithm with Advanced Generation of Prediction—Application to a Chemical Reactor
Algorithms 2020, 13(6), 143; https://0-doi-org.brum.beds.ac.uk/10.3390/a13060143 - 14 Jun 2020
Cited by 3 | Viewed by 1528
Abstract
In Model Predictive Control (MPC) algorithms, control signals are generated after solving optimization problems. If the model used for prediction is linear then the optimization problem is a standard, easy to solve, quadratic programming problem with linear constraints. However, such an algorithm may [...] Read more.
In Model Predictive Control (MPC) algorithms, control signals are generated after solving optimization problems. If the model used for prediction is linear then the optimization problem is a standard, easy to solve, quadratic programming problem with linear constraints. However, such an algorithm may offer insufficient performance if applied to a nonlinear control plant. On the other hand, if a model used for prediction is nonlinear, then non–convex optimization problem must be solved at each algorithm iteration. Then the numerical problems may occur during solving it and the time needed to calculate the control signals cannot be determined. Therefore approaches based on linearized models are preferred in practical applications. A fuzzy algorithm with an advanced generation of the prediction is proposed in the article. The prediction is obtained in such a way that the algorithm is formulated as a quadratic optimization problem but offers performance very close to that of the MPC algorithm with nonlinear optimization. The efficiency of the proposed approach is demonstrated in the control system of a nonlinear chemical control plant—a CSTR (Continuous Stirred–Tank Reactor) with van de Vusse reaction. Full article
(This article belongs to the Special Issue Model Predictive Control: Algorithms and Applications)
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Open AccessArticle
Performance Assessment of Predictive Control—A Survey
Algorithms 2020, 13(4), 97; https://0-doi-org.brum.beds.ac.uk/10.3390/a13040097 - 17 Apr 2020
Cited by 5 | Viewed by 1882
Abstract
Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology. More and more advanced predictive structures have been implemented. The first applications were in chemical engineering, and now Model Predictive Control can be found [...] Read more.
Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology. More and more advanced predictive structures have been implemented. The first applications were in chemical engineering, and now Model Predictive Control can be found in almost all kinds of applications, from the process industry to embedded control systems or for autonomous objects. Currently, each implementation of a control system requires strict financial justification. Application engineers need tools to measure and quantify the quality of the control and the potential for improvement that may be achieved by retrofitting control systems. Furthermore, a successful implementation of predictive control must conform to prior estimations not only during commissioning, but also during regular daily operations. The system must sustain the quality of control performance. The assessment of Model Predictive Control requires a suitable, often specific, methodology and comparative indicators. These demands establish the rationale of this survey. Therefore, the paper collects and summarizes control performance assessment methods specifically designed for and utilized in predictive control. These observations present the picture of the assessment technology. Further generalization leads to the formulation of a control assessment procedure to support control application engineers. Full article
(This article belongs to the Special Issue Model Predictive Control: Algorithms and Applications)
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Open AccessArticle
Comparison and Interpretation Methods for Predictive Control of Mechanics
Algorithms 2019, 12(11), 232; https://0-doi-org.brum.beds.ac.uk/10.3390/a12110232 - 04 Nov 2019
Cited by 6 | Viewed by 1700
Abstract
Objects that possess mass (e.g., automobiles, manufactured items, etc.) translationally accelerate in direct proportion to the force applied scaled by the object’s mass in accordance with Newton’s Law, while the rotational companion is Euler’s moment equations relating angular acceleration of objects that possess [...] Read more.
Objects that possess mass (e.g., automobiles, manufactured items, etc.) translationally accelerate in direct proportion to the force applied scaled by the object’s mass in accordance with Newton’s Law, while the rotational companion is Euler’s moment equations relating angular acceleration of objects that possess mass moments of inertia. Michel Chasles’s theorem allows us to simply invoke Newton and Euler’s equations to fully describe the six degrees of freedom of mechanical motion. Many options are available to control the motion of objects by controlling the applied force and moment. A long, distinguished list of references has matured the field of controlling a mechanical motion, which culminates in the burgeoning field of deterministic artificial intelligence as a natural progression of the laudable goal of adaptive and/or model predictive controllers that can be proven to be optimal subsequent to their development. Deterministic A.I. uses Chasle’s claim to assert Newton’s and Euler’s relations as deterministic self-awareness statements that are optimal with respect to state errors. Predictive controllers (both continuous and sampled-data) derived from the outset to be optimal by first solving an optimization problem with the governing dynamic equations of motion lead to several controllers (including a controller that twice invokes optimization to formulate robust, predictive control). These controllers are compared to each other with noise and modeling errors, and the many figures of merit are used: tracking error and rate error deviations and means, in addition to total mean cost. Robustness is evaluated using Monte Carlo analysis where plant parameters are randomly assumed to be incorrectly modeled. Six instances of controllers are compared against these methods and interpretations, which allow engineers to select a tailored control for their given circumstances. Novel versions of the ubiquitous classical proportional-derivative, “PD” controller, is developed from the optimization statement at the outset by using a novel re-parameterization of the optimal results from time-to-state parameterization. Furthermore, time-optimal controllers, continuous predictive controllers, and sampled-data predictive controllers, as well as combined feedforward plus feedback controllers, and the two degree of freedom controllers (i.e., 2DOF). The context of the term “feedforward” used in this study is the context of deterministic artificial intelligence, where analytic self-awareness statements are strictly determined by the governing physics (of mechanics in this case, e.g., Chasle, Newton, and Euler). When feedforward is combined with feedback per the previously mentioned method (provenance foremost in optimization), the combination is referred to as “2DOF” or two degrees of freedom to indicate the twice invocation of optimization at the genesis of the feedforward and the feedback, respectively. The feedforward plus feedback case is augmented by an online (real time) comparison to the optimal case. This manuscript compares these many optional control strategies against each other. Nominal plants are used, but the addition of plant noise reveals the robustness of each controller, even without optimally rejecting assumed-Gaussian noise (e.g., via the Kalman filter). In other words, noise terms are intentionally left unaddressed in the problem formulation to evaluate the robustness of the proposed method when the real-world noise is added. Lastly, mismodeled plants controlled by each strategy reveal relative performance. Well-anticipated results include the lowest cost, which is achieved by the optimal controller (with very poor robustness), while low mean errors and deviations are achieved by the classical controllers (at the highest cost). Both continuous predictive control and sampled-data predictive control perform well at both cost as well as errors and deviations, while the 2DOF controller performance was the best overall. Full article
(This article belongs to the Special Issue Model Predictive Control: Algorithms and Applications)
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Open AccessArticle
Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control
Algorithms 2019, 12(10), 220; https://0-doi-org.brum.beds.ac.uk/10.3390/a12100220 - 21 Oct 2019
Cited by 4 | Viewed by 1530
Abstract
This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II [...] Read more.
This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissions and total fuel consumption. Then TOPSIS method is adopted to select an optimal solution from Pareto front obtained from MPC_CPDMO-NSGA-II algorithm and is applied to the VISSIM environment. The control strategies are variable speed limit (VSL) and ramp metering (RM). In order to verify the performance of the proposed algorithm, the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. The result is compared with fixed speed limit strategy and single optimization method respectively. Simulation results show that it can effectively alleviate traffic congestion, reduce emissions and fuel consumption, as compared with fixed speed limit strategy and classical model predictive control method based on single optimization method. Full article
(This article belongs to the Special Issue Model Predictive Control: Algorithms and Applications)
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