Model-based Predictive Control Research Articles

Agent-Based System for Continuous Control and its Application to Activated Sludge Process

This paper presents a concept of architecture and ontology layouts for development of multiagent model-based predictive control systems. The presented architecture provides guidelines to simplify development of agent-based systems and improve its maintainability. The proposed multiagent system (MAS) layout is split into multiple subsystems that include agents dedicated to performing assigned tasks. MAS implementation was prepared which can use provided algorithms, actuators and can react to changes in its environment to reach the best available control quality. An example of MAS based on the proposed architecture is shown in application of dissolved oxygen (DO) concentration control in a laboratory activated sludge setup with biological reactor. For that application, MAS incorporates agent-based controllers from Boundary-Based Predictive Controllers (BBPC) family. Presented experiments prove flexibility, resilience and online reconfiguration ability of the proposed multiagent system.

Model-Based Predictive Control of a Solar Reactor

The present paper deals with the modelling and control of a solar reactor designed to produce syngas, by exploiting concentrated solar power. A model of the reactor based on the thermodynamic equilibrium is developed. Two model-based predictive control strategies are proposed: the first strategy (MPC strategy 1) aims to maintain the reactor's temperature at its nominal value whereas the second strategy (MPC strategy 2) aims to maintain the reactor's temperature at its nominal value, while maximizing the use of solar energy. Finally, these strategies are compared to a reference strategy, which is based on a combination of a rule-based controller and an adaptive PID controller with optimized gains. The robustness of the MPC controller to forecast errors is also studied by testing different DNI forecasting models. Parts of this paper were published as journal articleKarout, Y.; Curcio, A.; Eynard, J.; Thil, S.; Rodat, S.; Abanades, S.; Vuillerme, V.; Grieu, S. Model-Based Predictive Control of a Solar Hybrid Thermochemical Reactor for High-Temperature Steam Gasification of Biomass. Clean Technol. 2023, 5, 329-351. https://doi.org/10.3390/cleantechnol5010018

The control of superheater steam temperature in power plants using model predictive controller

The temperature of the steam produced by the superheater has been regarded as one of the most crucial parameters for steam power plant regulation. It must be precisely regulated within a restricted temperature range; overheating would result in the degradation of the substance. The recommended hybrid technique employed the Model Predictive Controller and Improved Fruit Fly Optimization Algorithm to regulate temperature levels and guarantee stability. Using the proposed method, model-based predictive control aims to enhance control quality and identify the optimal value. By employing this approach, the superheater can attain the optimal configurations for the steam temperature plant, thereby simultaneously enhancing the stability of the plant. The principal aim of this manuscript is to attain a reduction in the Integral Absolute Error to 40.6%. The simulation was conducted using the MATLAB/Simulink platform, and the outcomes demonstrate that the proposed method exhibits superior performance compared to those that have been documented in the literature.

Implementing a digital twin for optimized real-time control of Gothenburg's regional sewage system

Abstract The emerging challenges facing the City of Gothenburg and surrounding municipalities include the impacts of a variable climate, rapid growth and urbanization, aging infrastructure and changing community values and customer expectations. A digital twin (DT), based on the Future City Flow (FCF) platform, was created for the catchment to address many of these challenges in terms of improved real-time sewerage performance and predictability in the face of storms, improved DT visualization of the regional sewage transport system for operators and greater responsiveness to avoid service interruption. The FCF-based DT provides a sound base for modelling, simulating, forecasting and controlling the catchment. The versatility of the FCF DT makes it useful for gaining insight into catchment dynamics and the simulator can be used to study the effects of different scenarios or changes in the catchment. The predictive control imbedded in the FCF DT recommends setpoints for controlling the catchment that can be used by the operators for decision support or even be directly applied in the control system as model-based predictive control. A DT requires good quality online data from the catchment and, perhaps most importantly, high-quality rain forecasts, especially when prognosing long time horizons.

Performance analysis of a robust MF-PTC strategy for induction motor drive

The load model-based conventional predictive torque control (PTC) strategy performs as a high-performance controller at transient and steady-state conditions. However, its performance is poor on the issue of parameters variation. A robust model-free PTC (MF-PTC) strategy has been proposed in this paper to overcome the aforementioned drawback. An auto-regressive exogenous (ARX) model instead of a load model has been used to establish a model-free controller. This model is usually formed based on their input-output transfer function. A recursive least square algorithm has been employed to estimate the unknown parameters of the ARX model. Then, an observable canonical state-space model uses those estimated parameters to achieve an accurate prediction of the control variables. The performance of the proposed scheme can be affected by the variation of resistance. A resistance estimator based on the model reference adaptive system observer has been applied to improve the robustness of the system against variation of the stator and rotor resistance, and inductance measurement uncertainty. Simulation results show that the proposed MF-PTC scheme is robust against parameters uncertainty and works well at transient and steady-state conditions.

A Polytopic Model-Based Robust Predictive Control Scheme for Path Tracking of Autonomous Vehicles

A Polytopic Model-Based Robust Predictive Control Scheme for Path Tracking of Autonomous Vehicles

Research on vehicle speed prediction model based on traffic flow information fusion

Resource scarcity, global climate change and environmental pollution are increasingly constraining the development of the automotive industry. China proposes to reach the carbon peak by 2030; to reach the carbon neutral double carbon target by 2060 and gradually promote a green and low-carbon transition in energy development. The development of new energy vehicles is an important approach for China to realize its energy structure transformation in the automobile industry. HEV, as a transitional product of automobile energy transformation, has the advantages of both internal combustion engine vehicles and electric vehicles, which can improve the fuel efficiency and the emission problem of internal combustion engine vehicles and the range is longer compared to electric vehicles. One of the important aspects of HEV research is the design of whole vehicle energy management strategy based on the model predictions. Particularly, model-based predictive control is one of the mainstream energy management strategies nowadays, and its optimization effect is mainly subject to the model prediction accuracy. In this study, we constructed the ITS environment of a local roadway through simulation, compared the speed prediction effects of different speed prediction methods in different prediction time domains, and fused the historical information of vehicles (speed of the vehicle in front, distance, signal status, distance, and remaining time). It is found that N-BEATS is more effective in predicting vehicle speed in different prediction time domains, and the prediction accuracy of the speed prediction model is effectively improved after its fusion of multivariate information.

Distributed switched model-based predictive control for distributed large-scale systems with switched topology

Distributed switched large-scale systems are composed by dynamically coupled subsystems, in which interactions among subsystems vary over time according a switching signal. This paper presents a distributed robust switched model-based predictive control (DSwMPC) to control such systems. The proposed method guarantees stabilising the origin of the whole closed-loop system and ensures the constraints satisfaction in the presence of an unknown switching signal. In the distributed model-based predictive control (DMPC) used in this work, by considering the interactions among subsystems as an additive disturbance, the effect of the switch is reflected on the dynamic equation, local, and consistency constraint sets of the nominal subsystems. To compensate the effect of switching signal which creates a time-varying network topology, a robust tube-based switched model-based predictive control (RSwMPC) with switch–robust control invariant set as the target set robust to unknown mode switching is used as local controller. The scheme performance is assessed using three typical examples. The simulation results show that the input and state constraints are satisfied by the proposed DSwMPC at all times. They also validate that the closed-loop system converges to the origin. Also, a comparison of the DSwMPC with a centralised SwMPC (CSwMPC) and a decentralised SwMPC (DeSwMPC) are performed.

Dynamic optimisation of unbalanced distribution network management by model predictive control with Markov reward processes

In this work, a two-level control system is used to minimize the total active power losses of an active distribution system connected to the external grid and composed of a wind turbine, two photovoltaic power sources, and two batteries. At the first control level, a model-based predictive control (MPC) is run, using non-homogeneous Markov reward models for wind power prediction and homogeneous Markov reward models for photovoltaic power. At the second level, an algorithm is run for optimal management of voltage control assets, such as voltage regulating transformers, to minimize losses. Different scenarios have been considered, highlighting the advantages of using an MPC framework. This results in an optimization process that can be influenced by different time horizons depending on whether or not the MPC is applied. The predictions allow considering a long-horizon stepwise optimization process that leads to an increasing number of variables along with the decrease of total active power losses. When the MPC is not applied, a short-horizon analysis is performed with a decrease in both the number of variables and the quality of the results. Different cases are considered in which the nominal power of a photovoltaic unit and the battery capacity are modified.

Archetype Building Energy Models for Residential Sector: Importance of Uncontrolled Inputs in Predictive Control and Energy Flexibility

A 1st-order gray-box archetype is developed using high-quality measurements from an unoccupied experimental house to investigate the sensitivity of economic model-based predictive control performance under Québec’s dynamic electricity tariff for the residential sector, rate Flex-D, to uncertainty in the weather forecast. The archetype incorporates the outside air temperature and the solar gain as uncontrolled heat sources and has three parameters: the overall thermal capacitance, the thermal resistance to the outside air temperature, and the solar aperture. The performance of economic model-based predictive control is compared under the uncertain day-ahead forecast and the historical measurements of the same day. The results show that the most influential uncontrolled input directing the performance of economic model-based predictive control under rate Flex-D is the price signal, which makes the impact of uncertainty in the weather forecast almost negligible.

Modeling and control of vascular dementia disease by exact dosing of medicines

AbstractAlzheimer's and vascular dementia (VD) is the most common neurodegenerative syndromes so the patients lead to loss of memory, a cerebral and behavioral malfunction which may be diagnosed by the presence of two types of neuropathological characteristics such as extracellular plaques consisting of amyloid‐β‐peptides and intracellular neurofibrillary tangles of hyperphosphorylated‐tau‐proteins. Accumulation of these components in the brain needs prediction using models, an alternate tool of experimentation. Mathematical models representing neuroanatomical with pathophysiological characteristics are formulated using continuity balances of bio‐materials to encompass vascular‐cognitive‐impairment. The partial/ordinary differential mathematical model of VD has been simulated in this work and has been used to synthesize control schemes for the prevention of the disease. The non‐linear model has been implemented for prediction‐based automatic control of VD (in virtual mode) with four manipulated physical variables of the patient, concentrations of intracellular amyloid‐beta, tau‐proteins, and microglia; where the virtual controller considers medicinal doses (using virtual nanodosing pumps) to maintain the concentrations of extracellular amyloid‐β‐peptides and neuronal dead‐cells by regulating the oxygen level of the brain within the prescribed level in the clinic. A comparison of performances between the model‐based‐predictive controller (MPC) and PI controllers shows that the former controller is better. Wireless monitoring of amyloid‐beta and tau‐protein from Cerebro‐fluids can be used to design MPC for automatic nanodosing of medicines to prevent dementia. These strategies can be commercially exploited for pharmacological involvement and useful for society to prevent dementia and later Alzheimer's.

A hybrid-MPC based energy management system with time series constraints for a bioclimatic building

Microgrids play a crucial role in transitioning from the current energy grid model to one that seamlessly integrates renewable energy sources. However, the variability and unpredictability inherent in renewable energy pose new challenges in controlling energy flows within the microgrid. To ensure smooth operation in isolated mode or to minimize reliance on polluting energy sources, microgrids need to incorporate energy buffers and accurately predict renewable energy production, considering its intermittent nature. These factors significantly influence the development of effective microgrid controllers. This work presents a hybrid model-based predictive control to manage the energy flows in a microgrid. The proposed approach addresses both the discrete and continuous dynamics of the system through an optimized solution, incorporating, as main contribution, temporary constraints, which allows for integration of electric vehicle operating modes. The controller is tested in simulation with real data from a microgrid located at the CIESOL centre in Almería (Spain). Moreover, a comparison with a rule-based controller and a switching model predictive control is provided. Results demonstrate that the proposed approach effectively decreases the economic costs of microgrid management, outperforming the rule-based controller and the switching model predictive controller by 8.23% and 1.94%, respectively, over a thirty-day scenario.

Higher Efficiency with Model-based Predictive Knock Control

Higher Efficiency with Model-based Predictive Knock Control

Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics.

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.

Analysis Using Simulations of Predictive Control Algorithms Applied to the Matrix Converter

In this final work, a model-based predictive control strategy is proposed for three-phase matrix converter (MC) in order to control the power delivered to the balanced/linear loads for distributed generation applications. The discrete-time model of the MC, filter and load are used to predict the future behavior of the load currents for each of the possible switching states. The proposed predictive current method chooses, on one hand, a switching state that minimizes the error between the output currents and their references, and on the other hand, the reactive power output and his reference. The feasibility of the proposed method is verified through simulation results, evaluating these results using as a performance parameter the total harmonic distortion. These results validate the dynamic performance and efficiency of the proposed method.

Model based predictive control (MPC) with constraints applied to a multi-predictor scheme for linear delayed systems with an unstable pole

In the present work, a model based predictive control (MPC) strategy using a multi-predictor scheme for unstable high-order linear systems with delay is proposed. The stability analysis of this type of systems is difficult due to two main reasons, the appearance of an infinite number of poles when the feedback of the controller is considered and the second reason, is the delay size considered. In this work the use of a digital multi-predictor scheme is proposed, this device allows to estimate the internal variables of the process, which will be used for the design of the MPC to stabilize the plant. The present proposal considers an MPC with constraints in the control and output variables, which it is one of the most attractive features of this strategy. Numerical simulations are presented to illustrate how the efficiency of the system is increased due to the proposed control approach.

Synconn_build: A python based synthetic dataset generator for testing and validating control-oriented neural networks for building dynamics prediction

Applying model-based predictive control in buildings requires a control-oriented model capable of learning how various control actions influence building dynamics, such as indoor air temperature and energy use. However, there is currently a shortage of empirical or synthetic datasets with the appropriate features, variability, quality and volume to properly benchmark these control-oriented models. Addressing this need, a flexible, open-source, Python-based tool, synconn_build, capable of generating synthetic building operation data using EnergyPlus as the main building energy simulation engine is introduced. The uniqueness of synconn_build lies in its capability to automate multiple aspects of the simulation process, guided by user inputs drawn from a text-based configuration file. It generates various kinds of unique random signals for control inputs, performs co-simulation to create unique occupancy schedules, and acquires weather data. Additionally, it simplifies the typically tedious and complex task of configuring EnergyPlus files with all user inputs. Unlike other synthetic datasets for building operations, synconn_build offers a user-friendly generator that selectively creates data based on user inputs, preventing overwhelming data overproduction. Instead of emulating the operational schedules of real buildings, synconn_build generates test signals with more frequent variation to cover a broader range of operating conditions.•Synconn_build is an open-source tool designed to address the lack of datasets for benchmarking control-oriented building dynamics prediction models.•The tool automates simulations, data acquisition, and EnergyPlus configuration, guided by user inputs.•Synconn_build prevents data overproduction by selectively creating data, offering a user-friendly approach to dataset generation.

Collective large-scale wind farm multivariate power output control based on hierarchical communication multi-agent proximal policy optimization

Wind power is becoming an increasingly vital source of renewable energy worldwide. However, controlling power generation in wind farms faces significant challenges due to the inherent complexity of these systems. To address this issue and maximize power output, we propose a novel communication-based multi-agent deep reinforcement learning approach for large-scale wind farm control. We introduce a multivariate power model for wind farms to analyze the impact of wake effects on power output. This model considers controllable variables such as axial induction factor, yaw angle, and tilt angle. To coordinate the continuous controls in large-scale wind farms, we propose the hierarchical communication multi-agent proximal policy optimization (HCMAPPO) algorithm. The wind farm is divided into multiple wind turbine aggregators (WTAs), and neighboring WTAs can exchange information through hierarchical communication to optimize power output. Our simulation results demonstrate that the multivariate HCMAPPO approach significantly increases wind farm power output compared to traditional PID control, coordinated model-based predictive control, and the multi-agent deep deterministic policy gradient algorithm. The HCMAPPO algorithm can be trained using a thirteen-turbine wind farm environment and effectively applied to large-scale wind farms. Importantly, as the wind farm scale increases, there is no significant increase in wind turbine blade fatigue damage from wake control. This multivariate HCMAPPO control strategy enables the collective maximization of power output in large-scale wind farms.

Design and implementation of Model-Based Predictive Control for two-axis Solar Tracker

This paper presents the design and implementation of a model-based predictive controller (MPC) with the aim of reducing electrical energy consumption during the development of solar trajectory tracking tasks for a two-axis Solar Tracker. For the design of the MPC, it is necessary to know the specific parameters of each actuator of the Solar Tracker. Therefore, the design and implementation of an experimental methodology based on an algebraic approach to offline parameter identification is also presented. On the other hand, to validate the MPC proposal, an experimental methodology was carried out that allows evaluating and comparing its performance with that of two classical controllers. The above, in terms of tracking error and energy consumption. The results show that the MPC reduces on average 90% of the energy consumption, but it has a higher tracking error of approximately 0.5°. Therefore, based on this, analysis and discussion are provided.

Robust model-based predictive iterative learning control for systems with non-repetitive disturbances

Iterative Learning Control (ILC) is commonly used for batch processes. However, it may face difficulties when dealing with non-repetitive disturbances and inconsistent initial states. In situations with non-repetitive disturbances, the output may disobey constraints and negatively impact tracking performance when using existing predictive ILC algorithms. This paper introduces a new model-predictive ILC incorporating feed-forward and feedback mechanisms. This new approach evaluates and attenuates the impact of non-repetitive disturbances on the output. As a result, constraints are guaranteed, and tracking performance is preserved and improved, even in the presence of non-repetitive disturbances. Furthermore, if the desired trajectory is unattainable, the proposed ILC can robustly track an optimal trajectory while still guaranteeing constraints. The convergence is proven rigorously. Finally, two examples are provided to demonstrate the effectiveness of this new approach.