Model-based Predictive Control Research Articles

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.

Will Fractional Order Model Based MPC Save Control Energy?

In real-world situations, many complex processes exhibit fractional order dynamic characteristics. In this context, the common integer order modeling methods may not appropriately capture the physics of these processes, leading to a less satisfactory controller design. This paper conducts a comparative analysis between the First order plus time delay (FOPTD) model and the Time Delay with Single Fractional Pole (TDWFP) model. These two modeling methods are employed to characterize a one-dimensional heat propagation process and two model predictive controllers are developed based on the models. The results demonstrate a better fitting performance in both time and frequency response with the TDWFP model. Furthermore, the TDWFP model-based Model Predictive Control exhibits reduced sensitivity to the prediction horizon and requires low control input energy. This indicates the potential of using fractional order model in MPC for improved control energy efficiency in practical applications.

RBF-ARX Model-Based Robust Predictive Control Strategy With One Degree-of-Freedom

RBF-ARX Model-Based Robust Predictive Control Strategy With One Degree-of-Freedom

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.

Non-linear Model-based Predictive Control for Trajectory Tracking and Control Effort Minimization in a Smartphone-based Quadrotor

In this paper, the design and implementation of a non-linear model-based predictive controller (NMPC) for predefined trajectory tracking and to minimize the control effort of a smartphone-based quadrotor is carried out. The optimal control actions are calculated in each iteration by means of an optimal control algorithm based on the non-linear model of the quadrotor, considering some aerodynamic effects. Control algorithm implementation and simulation tests are executed on a smartphone using the CasADi framework. In addition, a technique for estimating the energy consumed based on control signals is presented. NMPC controller performance is compared to an H-infinity controller and an LQI controller, using three predefined trajectories, where the NMPC average tracking error was around 50\% lower, and average estimated power and energy consumption slightly higher, with respect to the H-infinity and LQI controllers.

Grey-box and ANN-based building models for multistep-ahead prediction of indoor temperature to implement model predictive control

Model-based predictive control (MPC) strategies for heating, ventilation, and air-conditioning (HVAC) systems present an opportunity to lower building energy consumption and operational costs. Such approaches rely on the development of a model to precisely forecast building thermal dynamics, such as room air temperature or heating/cooling rate, and make control-related decisions. The control-oriented modeling of building energy systems should be accurate in predicting indoor conditions and present low computational complexity. These features are the key challenge of implementing advanced control methods such as MPC. Extant studies on building modeling for MPC have focused on step-ahead forecasting techniques to forecast building thermal dynamics, while multistep-ahead forecasting is essential. Moreover, machine learning model suitable in case of the domain-based engineering expertise are also not available. To this aim, we perform a comparative analysis of the grey-box model based on a resistance-capacitance (RC) thermal network and a machine learning model composed of an artificial neural network (ANN) for multistep-ahead prediction of building thermal dynamics using current and historical data. Actual experimental data obtained from the Flexible Research Platform (FRP) in Oak Ridge National Laboratory (US) are used for estimation and validation purposes. The average root mean squared error (RMSE) of the grey-box and ANN models are 0.89 °C and 1.02 °C, respectively. The results indicate that the grey-box model outperforms the ANN model in the considered validation periods in terms of accuracy and prediction stability.

Methodology for energy management strategies design based on predictive control techniques for smart grids

This article focuses on the development of a general energy management system (EMS) design methodology using on model-based predictive control (MPC) for the control and management of microgrids. Different MPC-based EMS for microgrids have been defined in the literature; however, there is a lack of generality in the proposed that would facilitate adapting to new architectures, energy storage system technology, nature of the bus, application, or purpose. To fill this gap, a novel general formulation that is parameterizable, simple, easily interpretable, and reproducible in different microgrid architectures is presented. This is the result of the development of a novel methodology, which is also presented. It considers the state space formulation of the controller from the initial modelling phase, from the dynamics of the energy storage systems represented by their models to the subsequent definition of the optimisation problem. This is developed through the design of the general cost function and the formulation of constrains, by means of general guidelines and reference values. To evaluate the performance of the developed methodology, simulation tests were carried out for four different microgrid architectures, with different applications and objectives, also considering different generation conditions, demand profiles, and initial conditions. The results showed that, with some simple guidelines and regardless of the case study, the developed MPC controller design methodology can address the technical-economic optimisation problem associated with energy management in microgrids in an easy and intuitive way.

Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication.

A suitable control architecture for connected vehicle platoons may be seen as a promising solution for today's traffic problems, by improving road safety and traffic flow, reducing emissions and fuel consumption, and increasing driver comfort. This paper provides a comprehensive overview concerning the defining levels of a general control architecture for connected vehicle platoons, intending to illustrate the options available in terms of sensor technologies, in-vehicle networks, vehicular communication, and control solutions. Moreover, starting from the proposed control architecture, a solution that implements a Cooperative Adaptive Cruise Control (CACC) functionality for a vehicle platoon is designed. Also, two control algorithms based on the distributed model-based predictive control (DMPC) strategy and the feedback gain matrix method for the control level of the CACC functionality are proposed. The designed architecture was tested in a simulation scenario, and the obtained results show the control performances achieved using the proposed solutions suitable for the longitudinal dynamics of vehicle platoons.