Application Of Model Predictive Control Research Articles

Continuous Model Predictive Control for the Direct Torque Control of a PM Synchronous Motor

This paper presents the application of the continuous model predictive control (C-MPC) for the direct torque control (DTC) of a permanent magnet synchronous motor (PMSM). The C-MPC is designed for the direct regulation of the components of the stator flux and the electromagnetic torque of the PMSM. A linear state-space model based on a feedback linearization of the PMSM is used to formulate the controller. The control signals are applied to the PMSM via a power inverter through a pulse width modulator (PWM). The proposed controller is formulated as a constrained optimization problem, where the voltage applied to the motor is selected as the control input and restricted to the power inverter's linear region. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

High-throughput screening of optimal process conditions using model predictive control.

Modern biotechnological laboratories are equipped with advanced parallel mini-bioreactor facilities that can perform sophisticated cultivation strategies (e.g., fed-batch or continuous) and generate significant amounts of measurement data. These systems require not only optimal experimental designs that find the best conditions in very large design spaces, but also algorithms that manage to operate a large number of different cultivations in parallel within a well-defined and tightly constrained operating regime. Existing advanced process control algorithms have to be tailored to tackle the specific issues of such facilities such as: a very complex biological system, constant changes in the metabolic activity and phenotypes, shifts of pH and/or temperature, and metabolic switches, to name a few. In this study we implement a model predictive control (MPC) framework to demonstrate: (1) the challenges in terms of mathematical model structure, state, and parameter estimation, and optimization under highly nonlinear and stiff dynamics in biological systems, (2) the adaptations required to enable the application of MPC in high throughput bioprocess development, and (3) the added value of MPC implementations when operating parallel mini-bioreactors aiming to maximize the biomass concentration while coping with hard constrains on the dissolved oxygen tension profile.

Assessing mixed-integer-based heat pump modeling approaches for model predictive control applications in buildings

Model predictive control can reduce heating systems’ operating costs and energy consumption. This especially applies to heat pumps, whose operation efficiency highly depends on the heating system’s source and sink temperatures. In literature, process models for heat pumps mostly introduce the coefficient of performance as a parameter thus assuming constant supply temperatures. However, supply temperature adjustment is crucial to overcharge buffer storages and hence shift energy to more favorable times, which is the basis of model predictive control concepts. We close this gap by developing two different air-source heat pump modeling approaches using the supply temperature as control variable: a piecewise linear model based on simulation results and a quadratic modeling approach. Both methods are benchmarked with a simplified linear model representing the state of research. The simplified linear model underestimates the cost of storage charging as it neglects the supply temperature’s influence on the coefficient of performance and results in 8.7% higher operating costs and 12.1% higher energy demand than the piecewise linear approach. However, the simplified linear model yields the lowest average computation time compared to the piecewise and quadratic approaches. The quadratic approach results in both lower operating costs (-1.9%) and energy demand (-1.8%), as well as lower computation times than the piecewise approach, consequently representing the best trade-off between performance and computational effort. We recommend future work to apply the method to a ground- or water-source heat pump and to a coupled building energy system to investigate the transferability of our findings.

A predictive fault tolerant control method for qLPV systems subject to input faults and constraints

In this paper, we investigate the use of Model Predictive Control (MPC) applications for quasi-Linear Parameter Varying (qLPV) systems subject to faults along the input channels. We propose a Fault Tolerant Control (FTC) mechanism based on a robust state-feedback MPC synthesis, considering polytopic inclusions. In order to alleviate the numerical burden of the robust min-max procedure, we use small prediction horizons, in such a way that the solution becomes viable for real-time systems. The FTC system is able to tolerate time-varying saturation of the actuator, which may happen due to malfunctions. Recursive feasibility and poly-quadratic stability guarantees are ensured through the synthesis of adequate terminal ingredients. Accordingly, we present a catalogue of three different LMI remedies, considering: (a) parameter-independent ingredients, (b) a parameter-dependent terms and (c) a parameter-dependent maps that take into account bounded rates of parameter variation. An autonomous driving car example is used to illustrate the performances of the proposed technique, which is compared to other MPCs from the literature. The proposed FTC method is able to ensure good performances, obtained with reduced computational demand.

Model Predictive Control for Stabilization of DC Microgrids in Island Mode Operation

DC microgrid (DCMG) is a promising technology for integrating distributed resources, such as solar generation and energy storage devices, that are intrinsically DC. Recently, model predictive control (MPC) is one of the control techniques that has been widely used in microgrid applications due to its advantages, such as transient response and flexibility to nonlinearity inclusion. MPC applications can be centralized, distributed, or decentralized based on the communication architecture. A major disadvantage of the centralized model predictive control (CMPC) is the high computational effort. This paper proposes a CMPC for DCMG stabilization that uses the admittance matrix of a reduced DCMG in the prediction equation and the one-step prediction horizon to decrease the computational effort. The proposed CMPC also replaces the hierarchical architecture primary and secondary controls, achieving voltage or power regulation. A hardware-in-the-loop (HIL) tool, known as RT-Box 2, has been used to emulate an 8-node DC microgrid with versatile buck–boost converters at the supply and power consumption nodes. The proposed predictive control exhibited better performance in comparison with the averaged voltage control in the HIL experiments.

Sequential Model Predictive Fault-Tolerance Control for T-Type Three-Level Grid-Connected Converters With LCL Filters

Recently, fault-tolerance control (FTC) methods have been proposed to further improve the reliability of T-type three-level converters. However, the application of model predictive control in FTCs is rare, and the neutral point (NP) voltage imbalance before an open-switch fault occurs has not been solved. This paper focuses on these two problems and proposes two FTC control methods: sequential model predictive control (SMPC) and sequential model predictive tolerance control (SMPTC). Both methods are implemented by designing a sequential predictive control that first considers the NP voltage balance and then grid current tracking. Through the staggered operation of the two methods, a T-type grid-connected converter with an LCL filter can operate normally after an open-switch fault occurs. Moreover, both methods can achieve the NP voltage balance and excellent grid current outputs, eliminate the time-consuming selection of weighting factors, and reduce the number of calculation loops compared with the traditional model predictive tolerance control, thereby improving the control flexibility. The performance and comparison of the open-switch FTC with respect to the NP voltage oscillations, total harmonic distortion of the grid current, and pole voltage were demonstrated using numerous simulation and experimental results.

Neural Network Model Predictive Motion Control Applied to Automated Driving With Unknown Friction

Many innovative applications of vehicle control involve trajectory following while avoiding collisions, respecting actuator and dynamic limits, and using complex nonlinear dynamics. Additionally, these vehicle controllers must operate in the presence of difficult-to-model and uncertain dynamic forces which are often a function of the environment. To solve these problems, we present a design and experimental validation of neural network model predictive control (NNMPC), a method that uses vehicle operation data to construct a neural network model which is efficiently implemented in MPC. By learning a neural network model with a history of states and controls, NNMPC is capable of predicting vehicle dynamics in changing and complex operating conditions. We challenge NNMPC with the difficult task of automated racing near the friction limits without prior knowledge of the road-tire friction coefficient. The experimental results on an automated test vehicle demonstrate the capability of NNMPC to follow a trajectory near the limits on both high- and low-friction test courses. Furthermore, NNMPC outperforms a physics-based benchmark MPC on both the courses where the environmental latent state of road-tire friction is explicitly considered.

The State of the Art in Model Predictive Control Application for Demand Response

The State of the Art in Model Predictive Control Application for Demand Response

Review of Model Predictive Control of Distributed Energy Resources in Microgrids

In recent years, in response to increasing environmental concerns, advances in renewable energy technology and reduced costs have caused a significant increase in the penetration of distributed generation resources in distribution networks. Nonetheless, the connection of distributed generation resources to distribution networks has created new challenges in the control, operation, and management of network reliability. This article is a review on the model predictive control (MPC) for distributed energy resources (DER) in microgrids. The solutions of MPC for energy conversion of solar photovoltaic, wind, and energy storage systems are covered in detail. MPC’s applications for increasing reliability of grid-connected converters under (a)symmetrical grid faults are also discussed. The promising potentials of the applications of MPC to the stable multi-variable control performance of DERs are highlighted. This work reflects strong symmetry on MPC control strategies and provides guidance map for readers to facilitate future research works in these exciting fields.

Home energy management under realistic and uncertain conditions: A comparison of heuristic, deterministic, and stochastic control methods

Home energy management systems (HEMS) have been shown to reduce energy bills and to provide grid services including peak demand reduction and demand flexibility. However, uncertainty in residential energy systems is a significant issue and can reduce the benefits of a HEMS to the homeowner or grid operator. Sources of uncertainty include weather forecasts, predictions of energy-related occupant activities (e.g., hot water draws), and parameter estimation for the building envelope and energy-consuming equipment.This paper tackles the problem of uncertainty by developing a framework that simulates HEMS in uncertain conditions and evaluates the performance of multiple control strategies. A linear, reduced-order residential building model for model predictive control applications is derived and compared to a full-order model. Stochastic model predictive control is shown to perform better than deterministic and heuristic methods when considering realistic forecasts with uncertainty. The framework can evaluate the performance of HEMS in real-world applications, which can help de-risk HEMS deployment.

Energy management of domestic hot water systems with model predictive control and demand forecast based on machine learning

• Predictive control can improve performance of domestic hot water systems. • Model predictive controllers with imperfect or perfect forecasts perform similarly. • Decreasing accuracy increases the amount of temperature constraints violations. • Optimal control shows controllers should produce hot water shortly before peaks. • Inability to prepare for peak demand is the main impact of forecasting inaccuracies. While domestic hot water (DHW) production represents approximately 17% of the energy consumption of the Canadian residential sector, studies aiming to reduce this proportion are scarce. Model predictive control (MPC) can improve the performance of DHW systems by relying on demand forecasting models to operate the system. However, DHW predictions are often inaccurate for single-family or small systems, which makes the viability of MPC uncertain in these cases. To validate the applicability of MPCs relying on machine-learning forecasting models, we assessed their performance through the simulation of 40 different DHW profiles, based on measurements in the same number of single-family residential units. For the sake of comparison, four controllers are developed and tested in this paper: a rule-based controller (current situation), an optimal controller, an MPC with perfect demand forecast, and an MPC with real forecast from machine-learning models (i.e., MPC with neural networks). Furthermore, we considered 100 combinations of relative importance allocated to energy saving versus to the respect of temperature constraints in the controllers. We found that MPCs with perfect and imperfect forecasts could reach a similar performance in most cases, validating the applicability of the machine-learning models. The MPC relying on a machine-learning forecasting model yielded energy savings from 4% to 8% when compared with traditional control. The ability of the MPC to keep the stored DHW temperature closer to its minimal temperature constraint reduced heat losses and caused these savings. In comparison with optimal control, the inability to act sufficiently in advance of imperfect-forecast MPCs was identified as the main impact of forecasting inaccuracies and resulted in more temperature constraint violations.

A Dual Active-Set Solver for Embedded Quadratic Programming Using Recursive LDL$^{T}$ Updates

In this technical article, we present a dual active-set solver for quadratic programming that has properties suitable for use in embedded model predictive control applications. In particular, the solver is efficient, can easily be warm started, and is simple to code. Moreover, the exact worst-case computational complexity of the solver can be determined offline and, by using outer proximal-point iterations, ill-conditioned problems can be handled in a robust manner.

Impact of occupant related data on identification and model predictive control for buildings

Model predictive control (MPC) has shown potential in improving building performance but is bottlenecked by the difficulty in constructing control-oriented models. The challenge lies in evaluating the sufficiency of the model and the data usage beforehand. This paper bridges the knowledge gaps in the interactions between data requirements, model quality, and control performance by integrating real-world measurements and simulation-based experiments. The data usage related to occupancy and Internal heat gain (IHG) was studied considering its importance and the absence of consensus in the literature. Varying occupant-related data sources were tested as RC model inputs, including none, schedule, electricity consumption, CO2 ppm, and ideal measurement. Combinations of model inputs and complexities were examined for prediction and control in an office, a classroom, and multi-zone offices on one floor. The results indicated that the usefulness of data is jointly affected by three factors: measurement suitability, model complexity, and modeling purpose. Given the adequate model structure, satisfying prediction and control performance was achieved in offices with no detailed measurement. Meanwhile, electricity and CO2 were needed together to capture the IHG influence and realize the good performance for classrooms. The experiments also uncovered the heterogeneous requirements on models from traditional prediction tests and the control tasks. Lower prediction error did not always mean better control. More importantly, we provided the first quantitative demonstration of the complementary relationship between model adequacy and data informativeness with respect to different purposes. This study advocates the pioneering idea of sparse data usage and parsimonious modeling, which promotes the actual application of MPC in buildings by guiding control-oriented model development.

An implementable zone NMPC applied to an ESP-lifted oil well system: Handling the lack of measurements with nonlinear state estimator coupling

This paper addresses the first application of a nonlinear model predictive control (NMPC) scheme, coupled with an extended Kalman filter (EKF), for oil production processes with electric submersible pump (ESP) installations. The resulting nonlinear program of the proposed NMPC is most computationally tractable by the use of zone control scheme to deal with the downthrust and upthrust operational envelope-type ESP time-varying constraints, making it implementable in practice. The coupling of EKF seeks to solve the challenger problem of lack of measurements in ESP-lifted oil wells, besides is strategically used to compute downthrust and upthrust ESP head bounds from the estimated average flow rate in the production column. The implementable zone NMPC+EKF scheme is also flexible to incorporate optimizing targets in its formulation aiming at economic performance improvements. The effectiveness of the proposal is carried out in noisy plant-model mismatch scenarios for fairly different operating condition tracking, including cases of rejection of significant disturbances on manifold pressure. Analysis on the inclusion (or not) of optimizing target on the choke valve opening also demonstrates the economic gains in terms of ESP power savings, in addition, the computational effort evaluation corroborates with the feasibility of the controller in real-time implementation purposes.

Dynamic power management based on model predictive control for hybrid-energy-storage-based grid-connected microgrids

In this study, an efficient and reliable dynamic power management system (PMS) is proposed for microgrids (μGs) based on hybrid energy storage systems. Owing tothe differences in the response times of the different components (i.e., the battery, supercapacitor, and fuel cell) of the μG, efficiently allocating the power between the different devices is a challenging task for system designers. To address this issue and optimize the operation of the μG, a dynamic PMS is necessary. The contribution of this study is the application of model predictive control (MPC) for operating and controlling such μGs. The MPC method is used to manage power electronic devices such as DC-DC converters (for the battery, fuel cell, and supercapacitor) and grid-connected inverters. After the proposed PMS is successfully implemented in simulations, itsreal-time performance is validatedunder different operating scenarios that could occur in actual situations. The proposed PMS is found to be effective in controlling the control and operation of the μG.It can regulate the DC bus voltage during transient power imbalances, control the fuel cell current slope as per the requirement, and smoothen the operation between isolated and grid-connected modes.

Latest Advances of Model Predictive Control in Electrical Drives—Part II: Applications and Benchmarking With Classical Control Methods

This article presents the application of model predictive control (MPC) in high-performance drives. A wide variety of machines have been considered: Induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the article is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics, such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise.

PENERAPAN MODEL PREDICTIVE CONTROL (MPC) PADA FLOW LINE SISTEM PRODUKSI MENGGUNAKAN ALJABAR MAX-PLUS

Model Predictive Control (MPC) is a controller design method is popularly used in the industrial world. This research will be presented at the MPC application of flow line production system modeled using max-plus linear systems. The main advantage of the MPC is its ability to provide certain constraints or limits on the control signal input and output. Flow line of production systems is a diagram illustrating the process sequence in the production system, ranging from the input, processing and output. Of flow line production system, be applicable MPC as time optimization control of the production process. Furthermore, the results of the input and output MPC construction of a flow line production system can be obtained by using matlab program

Real-time tunable approximated explicit MPC

Tunability is a major obstacle in the creation and subsequent application of the explicit model predictive control (MPC). The main bottleneck lies in the need to reconstruct the parametric solution each time weighting factors changes. Such an operation makes the implementation of the explicit MPC impractical. This manuscript addresses the problem of producing a suboptimal parametric solution to the optimal control problem, where the change of the weighting factor does not warrant the reconstruction of the explicit MPC. The solution is achieved by interpolating between two boundary explicit solutions for a range of values in weighing factors. Furthermore, we show that the suboptimal solution enforces the closed-loop stability and recursive feasibility. The stability and recursive feasibility are maintained by carefully choosing the terminal penalty and terminal set in those boundary explicit solutions.

Maximum Power Point Tracking-Based Model Predictive Control for Photovoltaic Systems: Investigation and New Perspective.

In this paper, a comparative review for maximum power point tracking (MPPT) techniques based on model predictive control (MPC) is presented in the first part. Generally, the implementation methods of MPPT-based MPC can be categorized into the fixed switching technique and the variable switching one. On one side, the fixed switching method uses a digital observer for the photovoltaic (PV) model to predict the optimal control parameter (voltage or current). Later, this parameter is compared with the measured value, and a proportional–integral (PI) controller is employed to get the duty cycle command. On the other side, the variable switching algorithm relies on the discrete-time model of the utilized converter to generate the switching signal without the need for modulators. In this regard, new perspectives are inspired by the MPC technique to implement both methods (fixed and variable switching), where a simple procedure is used to eliminate the PI controller in the fixed switching method. Furthermore, a direct realization technique for the variable switching method is suggested, in which the discretization of the converter’s model is not required. This, in turn, simplifies the application of MPPT-based MPC to other converters. Furthermore, a reduced sensor count is accomplished. All conventional and proposed methods are compared using experimental results under different static and dynamic operating conditions.

Simplified data-driven models for model predictive control of residential buildings

Owing to recent advancements in Internet of Things technologies, data-driven model predictive control (MPC) has received significant research interest as a promising strategy to optimize building operation. As the MPC performance relies on the model prediction accuracy, complex building prediction models have been used in MPC applications, despite their high computational cost for optimization. This study examines whether linear-form prediction models are reliable to support the MPC of residential buildings equipped with single types of heating systems. This study developed linear-form models, namely an autoregressive with exogenous inputs (ARX) for predicting the indoor temperature and threshold-piecewise models for the return and supply water temperatures. The MPC performance on the basis of the linear models was evaluated under varying prediction horizons and weights associated with objective attributes. A case study of a residential unit through the simulated virtual building showed that the proposed models achieved the high goodness-of fit values greater than 0.9. The resulting MPC framework achieved heating energy savings up to approximately 12% relative to a simple on/off thermostat or reduction of comfort violation magnitude less than 0.5 °C. The influences of weight and prediction horizon on MPC performance were also investigated.