Integration Of Model Predictive Control Research Articles

Investigation of a model predictive control (MPC) strategy for seasonal thermochemical energy storage systems in district heating networks

This research investigates the integration of model predictive control (MPC) with seasonal thermochemical energy storage systems (STES) within district heating networks, focusing on the Nottingham district heating as a case study. The primary aim is to develop an MPC strategy that utilizes machine learning models for accurate heat load forecasting. This strategy optimizes the charging and discharging cycles of thermochemical energy storage systems to mitigate the mismatch between heating energy supply and demand by storing surplus heat during summer and utilizing it during winter. We employed and validated machine learning models, including support vector regression (SVR), regression trees, and long short-term memory (LSTM) networks, using historical heat load and meteorological data. A validated numerical model of the thermochemical energy storage system (TCES) was integrated into the MPC framework, formulated as a mixed-integer linear program to optimize the STES's operations. The performance of the MPC strategy was benchmarked against a rule-based control approach under varying supply capacities to evaluate scalability and robustness. Our findings reveal that each machine learning model achieved comparable performance, with CVRMSE values within the 9–11% range. The LSTM model, in particular, provided accurate multi-step forecasts essential for the MPC framework. Incorporating these models into the MPC strategy allowed for precise heat demand predictions, enhancing the management of energy storage and distribution. Results confirmed that MPC effectively shifts energy seasonally, reduces reliance on auxiliary heating during winter, and minimizes waste heat. The MPC strategy outperformed the rule-based control by storing a significantly higher percentage of waste heat and meeting a greater portion of the additional heat demand that was not covered by the auxiliary heat supply. The system demonstrated effective performance under varying supply capacities, with the MPC strategy efficiently utilizing stored heat to meet demand at 80% supply capacity, achieving a waste heat reduction to 4% and meeting most of the heat demand. However, performance declined at 60% capacity, indicating the need for careful consideration of supply capacities in system design. This study highlights the potential of integrating machine learning models with MPC to enhance the performance and adaptability of district heating systems with STES, minimizing waste heat and efficiently meeting energy demands.

Research on model predictive control of autonomous underwater vehicle based on physics informed neural network modeling

In the rapidly evolving field of Autonomous Underwater Vehicles (AUVs), achieving precise control remains a critical endeavor. This study presents a pioneering integration of Model Predictive Control (MPC) with a Physics-Informed Neural Network (PINN), aiming to enhance control system precision and operational efficiency in AUVs. The efficacy of MPC lies in its adept handling of the intricate constraints and inherent nonlinear dynamics intrinsic to AUV systems. Concurrently, the PINN architecture incorporates the fundamental physical laws represented by Partial Differential Equations (PDEs), augmenting the predictive fidelity of the system. Firstly, this research implements the novel PINN-enhanced MPC framework for trajectory tracking and conducts a comparative evaluation against adaptive proportional-integral-derivative (PID) and Gaussian-process-based MPC controllers. This comparative analysis elucidates the advancements in control mechanisms attributable to the PINN integration. Furthermore, this study meticulously assesses the PINN-MPC's proficiency in navigating through static and dynamic obstacles within three-dimensional marine environments, a critical capability for AUV operations. Through extensive and meticulous simulations, the proposed approach demonstrates notable progress in overcoming environmental challenges and executing intricate operational tasks, such as obstacle avoidance, with heightened efficiency and dexterity. This research constitutes a substantial contribution to the theoretical advancement and elucidation of control systems in the AUV domain, bearing profound practical implications. It lays the foundation for the development of increasingly sophisticated, advanced, and reliable AUV missions, signifying a crucial advancement in the realms of underwater exploration and operational technology.

Synergizing Wind and Solar Power: An Advanced Control System for Grid Stability

In response to the escalating global energy crisis, the motivation for this research has been derived from the need for sustainable and efficient energy solutions. A gap in existing renewable energy systems, particularly in terms of stability and efficiency under variable environmental conditions, has been recognized, leading to the introduction of a novel hybrid system that combines photovoltaic (PV) and wind energy. The innovation of this study lies in the methodological approach that has been adopted, integrating dynamic modeling with a sophisticated control mechanism. This mechanism, a blend of model predictive control (MPC) and particle swarm optimization (PSO), has been specifically designed to address the fluctuations inherent in PV and wind power sources. The methodology involves a detailed stability analysis using Lyapunov’s theorem, a critical step distinguishing this system from conventional renewable energy solutions. The integration of MPC and PSO, pivotal in enhancing the system’s adaptability and optimizing the maximum power point tracking (MPPT) process, improves control efficiency across key components like the doubly fed induction generator (DFIG), rectifier-sourced converter (RSC), and grid-side converter (GSC). Through rigorous MATLAB simulations, the system’s robust response to changing solar irradiance and wind velocities has been demonstrated. The key findings confirm the system’s ability to maintain stable power generation, underscoring its practicality and efficiency in renewable energy integration. Not only has this study filled a crucial gap in renewable energy control systems, but it has also set a precedent for future research in sustainable energy technologies.

Integrated Control and Optimization for Grid-Connected Photovoltaic Systems: A Model-Predictive and PSO Approach

To propel us toward a greener and more resilient future, it is imperative that we adopt renewable sources and implement innovative sustainable solutions in response to the escalating energy crisis. Thus, renewable energies have emerged as a viable solution to the global energy crisis, with photovoltaic energy being one of the prominent sources in this regard. This paper represents a significant step in the desired direction by focusing on detailed, comprehensive dynamic modeling and efficient control of photovoltaic (PV) systems as grid-connected energy sources. The ultimate goal is to enhance system reliability and ensure high power quality. The behavior of the suggested photovoltaic system is tested under varying sun radiation conditions. The PV system is complemented by a boost converter and a three-phase pulse width modulation (PWM) inverter, with MATLAB software employed for system investigation. This research paper enhances photovoltaic (PV) system performance through the integration of model-predictive control (MPC) with a high-gain DC–DC converter. It improves maximum power point tracking (MPPT) efficiency in response to the variability of solar energy by combining MPC with the traditional incremental conductance (IN-C) method. Additionally, the system incorporates a DC–AC converter for three-phase pulse width modulation, which is also controlled by predictive control technology supported by Particle Swarm Optimization (PSO) to further enhance performance. PSO was selected due to its capability to optimize complex systems and its proficiency in handling nonlinear functions and multiple variables, making it an ideal choice for improving MPC control performance. The simulation results demonstrate the system’s ability to maintain stable energy production despite variations in solar irradiation levels, thus highlighting its effectiveness.

Energy-oriented control retrofit for existing HVAC system adopting data-driven MPC – Methodology, implementation and field test

Energy conservation and carbon reduction of existing buildings have been receiving increasing attention with the proposed carbon neutrality goals. As presented in many studies, adopting model predictive control (MPC) for building HVAC system control is an effective means to realize building energy conservation. However, existing studies rarely addressed the critical challenges for practical applications and integration of MPC in existing Building Automation Systems (BASs). This study proposes a control retrofit approach, which is lightweight and replicable, to realize the improvement of energy efficiency of the existing building HVAC systems through integrating data-driven MPC into existing BASs. The critical challenges in the practical applications of MPC strategies, including MPC strategy development, large-scale data processing, strategy deployment and integration with existing BASs are addressed. The proposed approach was applied to the existing HVAC system of an actual airport terminal and the evaluation results indicate that the control retrofit approach is effective in achieving energy efficiency and thermal comfort improvement. The system daily energy consumption was reduced by 24.5% in average and the percentage of the discomfort time was reduced from 70.2% to 5.7% in average after the control retrofit. The proposed control retrofit approach, with the help of the scalability and distributed computing capabilities of the cloud computing platform, is expected to be able to realize lightweight control retrofit of large number of existing building HVAC systems.

Real-Time AI-Based Anomaly Detection and Classification in Power Electronics Dominated Grids

Real-time anomaly detection system (ADS) and anomaly classification system (ACS) techniques are becoming a crucial need for future power electronic dominated grid (PEDG). Artificial intelligence techniques such as recurrent neural networks, specifically long short-term memory (LSTM) provide a promising solution to detect anomalies in power grids. The main challenge is the implementation of these methods for real-time detection and classification for preventing catastrophic failure in PEDG. This article is addressing the challenge for detection and classification of anomalies in real-time in PEDG. The proposed approach is based on integration of model predictive control (MPC) and LSTM for realizing real-time ADS and ACS. The LSTM detection network can utilize the same time-series input data as the MPC, allowing for anomaly classification and correction. The proposed integrated LSTM-MPC approach has features of power electronics internal failure detection and corrective actions, which is an important aspect in future PEDG to differentiate inverters internal failures versus anomalies. Such internal failures include open circuit fault that needs to be detected and classified from a potential cyber-attack, allowing resilient operation of PEDG. The proposed integrated LSTM-MPC scheme for real-time ADS and ACS scheme is tested on a realistic 14-bus system dominated with inverters forming PEDG.

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.

Integration of model predictive control and backstepping approach and its stability analysis

Backstepping controller (BS) and model predictive controller (MPC) have been widely used for many applications by virtue of their own merits. BS works even with non-minimum phase and finite-time escape and MPC can handle state and input constraints explicitly. Nevertheless, BS requires repeated differentiations of the virtual control, whereas high computational loads of MPC are obstacles to practical implementation. This study proposes a control strategy that combines BS and MPC for nonlinear systems in strict-feedback form. It is proven that the controller renders the closed-loop system asymptotically stable. The proposed MPC-BS requires less computational load than that of MPC, since it only optimizes the virtual input of the first step and computes the input by backstepping approach. The explosion of terms caused by the consecutive differentiation in BS approach is also addressed.

A STRUCTURE APPROACH FOR A PHOTOVOLTAIC STATION CONTROL BASED ON ADAPTIVE FUZZY AGENT

The solar energy is directly converted into electrical energy by solar PV module. Each type of PV module has its own specific characteristic corresponding to the surrounding condition such as irradiation, and temperature and this makes the tracking of maximum power point (MPP) a complicated problem. To overcome this problem, many maximum power point tracking (MPPT) control algorithms have been presented. Fuzzy logic (FL) has been used for tracking the MPP of PV modules because it has the advantages of being robust, relatively simple to design and does not require the knowledge of an exact model where a mathematical model of the PV module, DC-DC converter, are used in the study of FL based MPPT algorithm. It is suggested to present this problem in the form of two-folds; first to identify the deviation of the power to maximum power point, and secondly, to control the voltage of the DC-DC converter corresponding to maximum power. In this paper, the first discussion approach will stress out the integration of model predictive control in maximum power point tracking MPPT and as progressing a second approach is identified as fuzzy logic controller FLC and perturb & Observe P&O algorithms are analyzed. All are interrelated to MPPT model for a photovoltaic module, PVM, to search for and generate the maximum power; in this case what’s called P-max. As per the first technique the focus is on the optimal duty ratio, D, for a series of multi diverse types of converters and load matching. The design of the MPPT for a stand-alone photovoltaic power generation system is applied where the system will consist of a solar array with nonlinear time varying characteristics, and a converter with appropriate filters. The integration of model predictive control will be addressed first in this paper. The second fold will implement an MPPT system that use the FLC and compare it with a classical MPPT P&O algorithm through the utilization of Simulink. The novel design in the FLC will be based on the use of asymmetrical membership functions to compensate for the asymmetrical P-V curve of solar panel.

Health‐aware model predictive control of wind turbines using fatigue prognosis

SummaryWind turbine components are subject to considerable fatigue because of extreme environmental conditions to which they are exposed, especially those located offshore. Wind turbine blades are under significant gravitational, inertial, and aerodynamic loads, which cause their fatigue and degradation during the wind turbine operational life. A fatigue problem is often present at the blade root because of the considerable bending moments applied to this zone. Interest in the integration of control with fatigue load minimization has increased in recent years. This paper investigates the fatigue assessment using a rainflow counting algorithm and the blade root moment information coming from the sensor available in a high‐fidelity simulator of a utility‐scale wind turbine. Then, the integration of the fatigue‐based system health management module with control is proposed. This provides a mechanism for the wind turbine to operate safely and optimize the trade‐off between components' life and energy production. In particular, this paper explores the integration of model predictive control with the fatigue‐based prognosis approach to minimize the damage of wind turbine components (the blades). A control‐oriented model of the fatigue based on the rainflow counting algorithm is proposed to obtain online information of the blades' accumulated damage that can be integrated with model predictive control. Then, the controller objective function is modified by adding an extra criterion that takes into account the accumulated damage. The scheme is implemented and tested in a well‐known wind turbine benchmark.

Novel Control of a Modular Multilevel Converter for Photovoltaic Applications

The number of applications of solar photovoltaic (PV) systems in power generation grids has increased in the last decade because of their ability to generate efficient and reliable power in a variety of low installation in domestic applications. Various PV converter topologies have therefore emerged, among which the modular multilevel converter (MMC) is very attractive due to its modularity and transformerless features. The modeling and control of the MMC has become an interesting issue due to the extremely large expansion of PV power plants at the residential scale and due to the power quality requirement of this application. This paper proposes a novel control method of MMC which is used to directly integrate the photovoltaic arrays with the power grid. Traditionally, a closed loop control has been used, although circulating current control and capacitors voltage balancing in each individual leg have remained unsolved problem. In this paper, the integration of model predictive control (MPC) and traditional closed loop control is proposed to control the MMC structure in a PV grid tied mode. Simulation results demonstrate the efficiency and effectiveness of the proposed control model.

Health-aware Model Predictive Control of Pasteurization Plant

In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.

Health-aware Model Predictive Control of Wind Turbines using Fatigue Prognosis

Wind turbines components are subject to considerable fatigue due to extreme environmental conditions to which are exposed, especially those located offshore. Interest in the integration of control with fatigue load minimization has increased in recent years. The integration of a system health management module with the control provides a mechanism for the wind turbine to operate safely and optimize the trade-off between components life and energy production.The research presented in this paper explores the integration of model predictive control (MPC) with fatigue-based prognosis approach to minimize the damage of wind turbine components (the blades). The controller objective is modified by adding an extra criterion that takes into account the accumulated damage. The scheme is implemented and tested using a high fidelity simulator of a utility scale wind turbine.

Advanced control and on-line process optimization in multilayer structures

The paper demonstrates the place, role and mutual interaction of advanced control algorithms and on-line set-point optimization in process control structures. First, a multilayer control structure resulting from a functional decomposition is briefly presented. The role and selected realizations of advanced control algorithms, in particular mostly applied now model predictive control (MPC) ones, at direct control and supervisory constraint control layers is discussed. Then possible solutions to on-line set-point optimization, depending of disturbance dynamics, are presented: dynamic set-point optimization including involved structures based on temporal decomposition, and steady-state set-point optimization for cases with disturbance dynamics both much slower than and comparable with the process dynamics. For the last case, important in industrial practice, different structures of interaction and even integration of MPC and steady-state optimization are discussed. The topics are illustrated by briefly presented examples, selected from given references.

Discussion on: “Improved MPC Design based on Saturating Control Laws”

This paper provides an interesting contribution on the integration of model predictive control (MPC) with control Lyapunov functions (CLF). Such an integration, as shown in Ref. [1], yields control strategies outperforming control design based on either MPC or CLF. In particular, the authors address the design of MPC strategies for linear discrete-time systems subject to input and/or state constraints with a large domain of attraction and a relatively low computational burden. Standard MPC with guaranteed stability either requires large control horizons or typically yields a much smaller domain of attraction. Fixed the length of the prediction horizon N, the domain of attraction can be enlarged by increasing the size of the terminal set without increasing the on-line computational burden. The authors combine the computation of an estimate of the domain of attraction, denoted as C1, for the saturated LQR regulator via linear difference inclusions (LDI) proposed in Ref. [2] and model predictive control ideas. The main contribution of the paper consists of a dual-mode algorithm that switches among two MPC algorithms with different terminal cost/set according to the feasibility. More precisely, a standard MPC algorithm with terminal cost/set related to the unsaturated LQR terminal control law and a modified MPC strategy employing as terminal set the estimate of the domain of attraction of the saturated LQR control law and as terminal cost the associated quadratic CLF, are used. The proposed terminal cost is an upper bound of the optimal one, and the resulting optimization problem amounts to the solution of a quadratic programming problem. The proposed procedure can encompass multivariable constrained control problems in the presence of state constraints. A first remark to be made is that although the authors consider only systems without uncertainty, the extension of the approach to the case of systems affected by additive unknown bounded noise and/or described by an uncertain polytopic model is rather straightforward [3]. According to the practitioners, what limits the performance and applicability of MPC are difficulties in modeling. Then it becomes fundamental to implement MPC algorithms for uncertain models with large domain of attraction and relatively low computational burden. However, the choice of presenting the results for systems without any kind of uncertainty is welcome from the point of view of simplicity. A second remark can be made in relation to the choice of the adopted LDI in order to describe the saturated system. The authors claim that the present results can be easily extended to any LDI description of the saturated system. The usage of an LDI in describing the closed-loop system clearly introduces some conservatism and then an interesting topic for future investigation would be the study of which is the best LDI description in order to reduce the conservatism.

Integration of Model Predictive Control and Optimization of Processes: Enabling Technology for Market Driven Process Operation

This paper discusses changes in the market of chemical processing industries and its consequences for process operation. The problem of continuously declining capital productivity is tackled, which has been faced by the chemical processing industries during the past decades. In the paper a motivation is given on how new technologies in the area of model predictive control and dynamic process optimization can contribute to improve business performance of the chemical processing industries in the changed markets. These new technologies have to support dynamic operation of plants at intended dynamics. Extensions and changes required to technologies that are currently applied in the oil refining industries and in some petrochemical industry applications are discussed. These extensions and changes are necessary to meet the specific requirements of the chemical processing industries.