Model-based Predictive Control Strategy Research Articles

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

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

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.

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.

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.

Data-driven predictive based load frequency robust control of power system with renewables

The uncertainties regarding the generations and key electromechanical parameters due to the high penetration of renewable energy bring significant challenges to frequency control. To address this problem, a robust data-driven predictive load frequency control (LFC) scheme for system with rich wind generation is proposed in this paper. To weaken the effects of the uncertainty of the key electromechanical parameters, the data-driven LFC scheme is accomplished through learning recursively from the input–output trajectory by employing matrix zonotopes, instead of accurate predictive model. Since the process uncertainties is well considered in generating a robustly optimal control law, the robustness to the uncertainties caused by renewables could be guaranteed in proposed methodology at each time step. The performance of the proposed LFC scheme is verified through comparison with the model-based predictive control scheme, AI-based controller and traditional PI controller.© 2017 Elsevier Inc. All rights reserved.

LSTM-CNN Network-Based State-Dependent ARX Modeling and Predictive Control with Application to Water Tank System

Industrial process control systems commonly exhibit features of time-varying behavior, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of a smooth nonlinear system’s spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We use a fusion of the long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent AutoRegressive with the eXogenous variable (ARX) model to establish the LSTM-CNN-ARX model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system’s spatiotemporal characteristics due to its incorporation of the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model’s local linear and global nonlinear features. The control comparison experiments conducted on a water tank system show the effectiveness of the developed models and MPC methods.

Macro Fiber Composite-Actuated Soft Robotic Fish: A Gray Box Model-Predictive Motion Planning Strategy Under Limited Actuation.

This work experimentally investigates a model-predictive motion planning strategy to impose oscillatory and undulation movements in a macro fiber composite (MFC)-actuated robotic fish. Most of the results in this field exploit sinusoidal input signals at the resonance frequency, which reduces the device's maneuverability. Differently, this work uses body/caudal fin locomotion patterns as references in a motion planning strategy formulated as a model-based predictive control (MPC) scheme. This open-loop scheme requires the modeling of the device, which is accomplished by deriving a gray box state-space model using experimental modal data. This state-space model considers the electromechanical coupling of the actuators. Based on the references and the model, the MPC scheme derives the input signals for the MFC actuators. An experimental campaign is carried out to verify two references for mimicking the locomotion patterns of a fish under limited actuation. The experimental results confirm the motion planning scheme's capability to impose oscillatory and undulation movements.

Model-based predictive control of multi-stage air-source heat pumps integrated with phase change material-embedded ceilings

This paper presents a model-based predictive control strategy to optimize the operations of phase change material (PCM) ceiling panels coupled with a multi-stage air-source heat pump. A three-stage prototype heat pump unit has been built and tested in the laboratory, with the low and medium stages designed for space heating/cooling and the high compression stage dedicated to charging of the PCM energy storage. To facilitate optimal control of the integrated heat pump system, a mixed-integer linear programming formulation is derived through linearization of the heat pump model and a mixed-integer reformulation of the PCM dynamic governing equations. A predictive control strategy is synthesized based on the resultant control formulation and implemented in a receding horizon scheme that optimizes the PCM charging and the zone temperature schedules simultaneously to leverage both the passive (associated with building construction materials) and active (PCM) storage capacities of a building. The control strategy has been tested along with three benchmarking control scenarios using a co-simulation platform for a prototypical detached house in Atlanta, GA. Test results showed that application of the proposed control strategy to the PCM-integrated heat pump could provide 27.1% electricity cost savings while a fine tuned rule-based control strategy could achieve cost savings of 20.4%, compared to a baseline case without PCM storage, under a time-of-use rate tariff.

Static Model-Based Optimization and Multi-Input Optimal Control of Automatic Transmission Upshift during Inertia Phase

Step-ratio automatic transmission upshift performance can be improved by modulating the off-going (OFG) clutch during the inertia phase. In this paper, a static powertrain performance model is derived and applied for the purpose of numerically efficient, multi-objective shift control parameter optimization for the inertia phase. The optimization is aimed at finding the optimal node parameters for simplified, piecewise linear, open-loop profiles of oncoming (ONC) clutch, OFG clutch, and engine torque reduction control variables. The performance indices, i.e., the optimization objectives, include shift comfort, clutch thermal loss, and shift time. The optimization results in 3D Pareto optimal frontiers, which are then analyzed and compared with those obtained by using the previously developed, nonlinear model-based, genetic algorithm optimization tool. The derived method is employed in order to develop a static model-based predictive control (S-MPC) strategy, which commands ONC clutch torque control input while retaining open-loop controls for engine and OFG clutch control inputs. The S-MPC strategy aims at providing the prespecified shift time, while the shift time accuracy is relaxed to some extent by using a control input dead zone element to avoid chattering effect. The S-MPC system performance is verified through simulation and compared with the genetic algorithm benchmark. The simulation results demonstrate that the S-MPC strategy approaches the benchmark performance.

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control.

Piezoelectric actuators (PEA) are high-precision devices used in applications requiring micrometric displacements. However, PEAs present non-linearity phenomena that introduce drawbacks at high precision applications. One of these phenomena is hysteresis, which considerably reduces their performance. The introduction of appropriate control strategies may improve the accuracy of the PEAs. This paper presents a high precision control scheme to be used at PEAs based on the model-based predictive control (MPC) scheme. In this work, the model used to feed the MPC controller has been achieved by means of artificial neural networks (ANN). This approach simplifies the obtaining of the model, since the achievement of a precise mathematical model that reproduces the dynamics of the PEA is a complex task. The presented approach has been embedded over the dSPACE control platform and has been tested over a commercial PEA, supplied by Thorlabs, conducting experiments to demonstrate improvements of the MPC. In addition, the results of the MPC controller have been compared with a proportional-integral-derivative (PID) controller. The experimental results show that the MPC control strategy achieves higher accuracy at high precision PEA applications such as tracking periodic reference signals and sudden reference change.

An hour-ahead predictive control strategy for maximizing natural ventilation in passive buildings based on weather forecasting

Due to the increasing demands of energy conservation and emission reduction, passive buildings with extra-low energy consumption and near zero energy consumption have attracted more attentions as a promising sustainable building type. Due to passive techniques such as air-tightness, passive buildings have a special demand of mechanical fresh air systems after closing windows. So it is indispensable to explore elaborate control strategies for maximizing natural ventilation, for the purpose of postponing the use of fossil energy. By taking meteorological forecast parameters as the input, an hour-head model-based predictive control (MPC) strategy of natural ventilation was proposed in this paper. Specifically, simulated multi-dimensional datasets obtained by setting different control actions under the same disturbances were obtained as the database for data-driven prediction models, which take weather forecast parameters as the input and labels of thermal comfort levels and air volumes as the output. It has been verified that although both with extended working hours and more stable comfort levels, the MPC strategy with priority to thermal comfort is superior to that with priority to fresh air volume. Furthermore, when compared with the traditional control strategy, ventilation hours via this MPC strategy could be increased by 56.3% from 366 h to 572 h. With this MPC model, the self-adaptive adjustment of window openness could be achieved by taking hour-ahead weather forecasting parameters as the input, thus to facilitate the climatic self-adaptive intelligent operation of passive buildings in future.

Deep Learning-Based State-Dependent ARX Modeling and Predictive Control of Nonlinear Systems

For many practical industrial objects with time-varying operating points, strong nonlinearity, and difficulty in obtaining analytical models, the data-driven identification method is usually used to model such nonlinear systems. However, it is difficult for traditional modeling algorithms to effectively extract the dynamic characteristics of nonlinear systems from data and obtain accurate mathematical models. In this paper, we consider using the deep learning network combined with the state-dependent exogenous variable autoregressive (SD-ARX) model framework to build the nonlinear system model, so as to effectively and accurately learn the space-time characteristics of the nonlinear system from the sample data. Based on the idea, the hybrid models, i.e., the RNN-ARX model, CNN-ARX model, and RNN-CNN-ARX model are built, which use recurrent neural networks (RNN), convolutional neural networks (CNN) and their combination to fit the function-type coefficients of SD-ARX model, respectively. SD-ARX model based on deep learning has the advantages of local linearity and global nonlinearity. Compared with the other two models, the RNN-CNN-ARX model has a stronger ability to extract the multidimensional spatiotemporal dynamic characteristics of nonlinear systems, because it combines the advantages of RNN in mining temporal features and CNN in extracting spatial features. According to the structural characteristics of these models, three model-based predictive control (MPC) strategies are designed, i.e., RNN-ARX-MPC, CNN-ARX-MPC, and RNN-CNN-ARX-MPC. The real-time control comparative experiment on an actual multi-water-tank object shows that the proposed modeling and MPC method is feasible and effective for the modeling and predictive control of the nonlinear system.

Enhancing control systems of higher plant culture chambers via multilevel structural mechanistic modelling.

Modelling higher plant growth is of strategic interest for modern agriculture as well as for the development of bioregenerative life support systems for space applications, where crop growth is expected to play an essential role. The capability of constraint-based metabolic models to cope the diel dynamics of plants growth is integrated into a multilevel modelling approach including mass and energy transfer and enzyme kinetics. Lactuca sativa is used as an exemplary crop to validate, with experimental data, the approach presented as well as to design a novel model-based predictive control strategy embedding metabolic information. The proposed modelling strategy predicts with high accuracy the dynamics of gas exchange and the distribution of fluxes in the metabolic network whereas the control architecture presented can be useful to manage higher plants chambers and open new ways of merging metabolome and control algorithms.

Model Predictive Control for Photovoltaic Plants with Non-Ideal Energy Storage Using Mixed Integer Linear Programming

This paper proposes a model-based predictive control strategy based on mixed-integer linear programming for a photovoltaic power plant with battery energy storage. The control objective is to maximize the revenues from energy delivered from both photovoltaic panels and batteries to the grid in a deregulated electricity market. For each control interval, the proposed algorithm incorporates information on solar radiation, market prices, and the state of charge of the batteries to determine the intervals of energy injection into the grid to maximize the economic benefits. The proposed strategy considers the rate-based variable efficiency in the battery model and time-varying energies prices, thus providing a more general implementation than previous schemes proposed in the literature for the same purpose. Simulations considering the operational procedures of the Spanish market as a case study show that, by integrating the battery efficiency in the model, the proposed control strategy increments the economic benefits in 21% compared to previous results reported in the literature for the same operational conditions. Additionally, the proposed approach reduces the number of charge and discharge cycles, potentially extending the lifespan of batteries.

Fault tolerant load frequency sharing of a multi‐area power system using model predictive control

This paper focuses on the load frequency control (LFC) problem in a four-area interconnected power system with tie-line failures. As opposed to the traditional robust-based configuration techniques, which consider the operating parameters modify within a small range, physical failures may lead to critical structural or parametric changes in the LFC system. Consequently, a system parameter-free fault-tolerant model-based predictive control (FTMPC) strategy for LFC is proposed to address the problem. Multiple steps are taken to achieve load frequency control as follows. First, the system is designed to operate normally with each of the three model-based predictive controls (MPCs), namely centralized, decentralized, and distributed in the same conditions. Next, a tie-line disconnection fault is added to this system, which changes the configuration of the previously distributed power system. The new configuration depends on the tie-line that is disconnected. Furthermore, the performance of centralized, decentralized, and distributed MPCs have been compared to each other in different tie-line disconnections. A new controller is required to maintain reliability and performance in the presence of the fault. Thus, in the final step, an FTMPC is proposed, which has shown acceptable performance in various tie-line disconnections. The efficacy of the proposed method is verified via numerical simulations.

Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers

The aim of this work is to schedule the charging of electric vehicles (EVs) at a single charging station such that the temporal availability of each EV as well as the maximum available power at the station are considered. The total costs for charging the vehicles should be minimized w.r.t. time-dependent electricity costs. A particular challenge investigated in this work is that the maximum power at which a vehicle can be charged is dependent on the current state of charge (SOC) of the vehicle. Such a consideration is particularly relevant in the case of fast charging. Considering this aspect for a discretized time horizon is not trivial, as the maximum charging power of an EV may also change in between time steps. To deal with this issue, we instead consider the energy by which an EV can be charged within a time step. For this purpose, we show how to derive the maximum charging energy in an exact as well as an approximate way. Moreover, we propose two methods for solving the scheduling problem. The first is a cutting plane method utilizing a convex hull of the, in general, nonconcave SOC–power curves. The second method is based on a piecewise linearization of the SOC–energy curve and is effectively solved by branch-and-cut. The proposed approaches are evaluated on benchmark instances, which are partly based on real-world data. To deal with EVs arriving at different times as well as charging costs changing over time, a model-based predictive control strategy is usually applied in such cases. Hence, we also experimentally evaluate the performance of our approaches for such a strategy. The results show that optimally solving problems with general piecewise linear maximum power functions requires high computation times. However, problems with concave, piecewise linear maximum charging power functions can efficiently be dealt with by means of linear programming. Approximating an EV’s maximum charging power with a concave function may result in practically infeasible solutions, due to vehicles potentially not reaching their specified target SOC. However, our results show that this error is negligible in practice.

Motion planning of a fish-like piezoelectric actuated robot using model-based predictive control

This work proposes a novel methodology for planning the motion of fish-like soft robots actuated by macro-fiber composite (MFC) pairs. These structures should mimic oscillatory and undulation movements, which can be accomplished if the amplitude of the tail motion is larger than that of the head motion. Design strategies, such as the use of concentrated and distributed masses, are addressed to mimic fish-like motion since they guarantee suitable mode shapes for the structure. The motion planning proposal explores a model-based predictive control (MPC) strategy for deriving the input signals for the MFC actuators. This model-based control strategy requires the use of reasonably small-sized models. This is accomplished by extracting modal state-space models based on the free–free Euler–Bernoulli beam theory considering the electro-mechanical coupling of the MFC actuator pairs. Numerical results demonstrate the capability of the proposal for deriving bounded input signals that generate oscillatory and undulation movements even in the presence of disturbances. This general approach can be further extended for other applications.

Resilient Predictive Control Coupled with a Worst-Case Scenario Approach for a Distributed-Generation-Rich Power Distribution Grid

In a context of accelerating deployment of distributed generation in power distribution grid, this work proposes an answer to an important and urgent need for better management tools in order to ‘intelligently’ operate these grids and maintain quality of service. To this aim, a model-based predictive control (MPC) strategy is proposed, allowing efficient re-routing of power flows using flexible assets, while respecting operational constraints as well as the voltage constraints prescribed by ENEDIS, the French distribution grid operator. The flexible assets used in the case study—a low-voltage power distribution grid in southern France—are a biogas plant and a water tower. Non-parametric machine-learning-based models, i.e., Gaussian process regression (GPR) models, are developed for intraday forecasting of global horizontal irradiance (GHI), grid load, and water demand, to better anticipate emerging constraints. The forecasts’ quality decreases as the forecast horizon grows longer, but quickly stabilizes around a constant error value. Then, the impact of forecasting errors on the performance of the control strategy is evaluated, revealing a resilient behaviour where little degradation is observed in terms of performance and computation cost. To enhance the strategy’s resilience and minimise voltage overflow, a worst-case scenario approach is proposed for the next time step and its contribution is examined. This is the main contribution of the paper. The purpose of the min–max problem added upstream of the main optimisation problem is to both anticipate and minimise the voltage overshooting resulting from forecasting errors. In this min–max problem, the feasible space defined by the confidence intervals of the forecasts is searched, in order to determine the worst-case scenario in terms of constraint violation, over the next time step. Then, such information is incorporated into the decision-making process of the main optimisation problem. Results show that these incidents are indeed reduced thanks to the min–max problem, both in terms of frequency of their occurrence and the total surface area of overshooting.