Model Predictive Control Control Strategy Research Articles

Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm

The deployment of model-predictive control (MPC) for a building’s energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm.

Event-triggered model predictive control for series–series resonant ICPT systems in electric vehicles: A data-driven modeling method

This paper investigates the event-triggered model predictive control (MPC) for the series–series (SS) resonant inductive coupling power transfer (ICPT) system in electric vehicles (EVs). Different from most existing literature in ICPT systems, a data-driven modeling approach based on input–output data is proposed to describe the system dynamics and achieve constant voltage output in the presence of load variations. In the traditional MPC control strategies, the optimal control input should be calculated at each time instant to achieve the desired output voltage, which causes great computational burden. To tackle this issue, an event-triggered MPC mechanism is therefore developed to effectively alleviate the computational burden, which will generate the optimal control input only when the norm of the state error exceeds a predefined threshold. The effectiveness and reliability of the proposed event-triggered MPC control strategy are successfully verified by the experimental results.

A Comparative Study of Optimal Individual Pitch Control Methods

Wind turbines are subjected to asymmetric loads and fatigue with subsequent increases in their dimension and capacity, leading to a reduction in their lifetime. To address this problem, the individual pitch control (IPC) technique is quite familiar in the control of wind turbines. IPC is used to reduce the tilt and yaw moments, simultaneously alleviating the turbine blade-root bending moments (BRBMs). This study discusses the performance of model predictive control (MPC), H-infinity (H∞), and proportional and integral (PI)-based IPC strategies integrated with collective pitch control. The performance of the reported controllers has been validated using the National Renewable Energy Laboratory (NREL) 5 MW full nonlinear reference wind turbine. Simulation studies are conducted at varying wind speeds and turbulent intensities as per international electrotechnical commission (IEC) norms. Comparative results in the time and frequency domains indicate that the H∞ based IPC achieves enhanced control performance in terms of reduction in BRBMs and damage equivalent load compared to MPC and PI-based control strategies.

A novel engine and battery coupled thermal management strategy for connected HEVs based on switched model predictive control under low temperature

Under a low-temperature environment, electric vehicles face serious environmental adaptability problems, and efficient vehicle thermal management strategies are urgently needed. This paper presents a novel engine–battery coupled thermal management strategy for connected hybrid electric vehicles (HEVs). An improved system structure for an engine–battery coupled thermal management system (engine–battery CTMS) is designed to avoid unnecessary heat loss. The control requirements of the engine–battery CTMS include minimum engine fuel consumption, minimum power battery aging damage and minimum system energy consumption, which constitutes a multi-objective optimal control problem in a finite time domain. Based on model predictive control (MPC) theory, a switched nonlinear MPC (NMPC) control strategy is proposed to solve the optimal control problem of the complex coupled multi-input multi-output system. To verify the effectiveness of the proposed strategy, three comparative experiments of the centralized NMPC-based and rule-based methods combined with the improved system structure and the unimproved system structure are designed. The results of the cosimulation experiment between MATLAB/Simulink and AMEsim under various driving cycles and different ambient temperatures show that the improved structure and switched control strategy confer great advantages in reducing the controller computation burden, engine fuel consumption, and power battery aging damage.

Koopman Model Predictive Control of an Integrated Thermal Management System for Electric Vehicles

AbstractThis paper is concerned with energy efficient operation of an integral thermal management system (ITMS) for electric vehicles using a nonlinear model predictive control (MPC). Driven by a heat pump (HP), this ITMS can handle battery thermal management (BTM) while serving the need for cabin cooling or heating need. The objectives of the ITMS MPC control strategy include minimization of power consumption and achieving temperature setpoint regulation for the battery and cabin space based on predictive information of traction power and cabin thermal load. The control design is facilitated by a gray-box modeling framework, in which the nonlinear dynamics of HP subsystem are characterized with a data-driven Koopman subspace model, while the BTM subsystem dynamic is a bilinear physics-based model. The computational efficiency of the proposed MPC framework is improved with two aspects of convexification for the underlying receding-horizon constrained optimization problem: the Koopman-operator lifting and the McCormick envelopes implemented for handling the bilinear dynamics. The proposed control method is evaluated with simulation study, by developing a Modelica-Python cosimulation platform via the functional mockup interface (FMI), where the electric vehicle (EV)-ITMS plant is modeled in Modelica with Dymola and the MPC design is implemented in Python. By benchmarking against a recurrent-neural-networks (RNN) model based nonlinear MPC, the simulation results validate the effectiveness and improved computational efficiency of the proposed method.

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.

Model Predictive Control Strategies for Optimized mHealth Interventions for Physical Activity.

Many individuals fail to engage in sufficient physical activity (PA), despite its well-known health benefits. This paper examines Model Predictive Control (MPC) as a means to deliver optimized, personalized behavioral interventions to improve PA, as reflected by the number of steps walked per day. Using a health behavior fluid analogy model representing Social Cognitive Theory, a series of diverse strategies are evaluated in simulated scenarios that provide insights into the most effective means for implementing MPC in PA behavioral interventions. The interplay of measurement, information, and decision is explored, with the results illustrating MPC's potential to deliver feasible, personalized, and user-friendly behavioral interventions, even under circumstances involving limited measurements. Our analysis demonstrates the effectiveness of sensibly formulated constrained MPC controllers for optimizing PA interventions, which is a preliminary though essential step to experimental evaluation of constrained MPC control strategies under real-life conditions.

Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2 Capture Plants

The purpose of this paper is to investigate the possible implementation of the Fast model predictive control (MPC) scheme for chemical systems. Due to the difficulties associated with complicated dynamic behavior and model sensitivity, which results in considerable offsets, the Fast MPC controller has not been implemented on the CO2 capture plant based on the absorption/stripping system. The main objective of this work is to evaluate the most appropriate model for implementing the Fast MPC control strategy, which results in fast output responses, negligible offsets, and minimum errors. The steady-state and dynamic simulation models of the CO2 capture plant are designed in Aspen PLUS. In the System Identification Toolbox, multiple state-space models are identified to achieve a highly accurate model for the Fast MPC controller. The Fast MPC controller is then implemented to evaluate the performance under a setpoint tracking mode with ±5 and ±15% step changes. The results showed that the Fast MPC based on the state-space prediction focus model has on average 7.9 times lower offset than the simulation focus model and 10.4 times lower integral absolute error values. The comparison study concluded that the Fast MPC control strategy performs efficiently using prediction-based focus state-space models for CO2 capture plants using the absorption/stripping system with minimum offsets and errors.

Economic Model Predictive Control for Post-Combustion CO2 Capture System Based on MEA

For the post-combustion CO2 capture (PCC) system, the time variability of the economic performance is key to the production process of such an actual industrial process. However, the performance index used by the conventional model predictive control (MPC) does not reflect the economy of the production process, so the economic cost function is used instead of the traditional performance index to measure the economy of the production process. In this paper, a complete dynamic model of the PCC system is constructed in Aspen Plus Dynamics. The effectiveness of the model is verified by dynamic testing; subspace identification is carried out using experimental data, a state-space equation between flue gas flow and lean solvent flow; the CO2 capture rate is obtained; and dynamic models and control algorithm models of accused objects are established in Matlab/Simulink. Under the background of the environmental protection policy, an economic model predictive control (EMPC) strategy is proposed to manipulate the PCC system through seeking the optimal function of the economic performance, and the system is guaranteed to operate under the economic optimal and excellent quality of the MPC control strategy. The simulation results verify the effectiveness of the proposed method.

Pressure control for pneumatic electric braking system of commercial vehicle based on model predictive control

Precise braking pressure control of a pneumatic electric braking system (PEBS) poses challenging non-linear control problems, since it can operate in several distinct discrete processes (the pressure increasing, holding and decreasing) by switching the on-off solenoid valves and the piston of relay valve moves irregularly. This article describes the development and experimental validation of a viable controller for an electronically controlled braking pressure based on the theory of model predictive control (MPC). The controller design consists of two parts. The non-linear characteristic is first studied to ensure the pressure response of PEBS when the opening time is 4–10 ms in a fixed period, followed by calculation and prediction of the optimal on-off time of inlet valve and release valve based on the current braking pressure to realize steady state. A logic rule is designed to ensure that the MPC controller and feedback control work coordinate to guarantee engineering reliability. A comprehensive system model is derived to help characterize system non-linearities and design the MPC predictive model. Simulations and experimental results are presented finally to show how the MPC and feedback control strategy can be successfully applied to solve the braking pressure control of a PEBS in a systematic way.

Bayesian Neural Network Modeling and Hierarchical MPC for a Tendon-Driven Surgical Robot With Uncertainty Minimization

In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. Model Predictive Control (MPC) is a reliable control strategy to ensure optimality and satisfaction of safety constraints. In this work we propose the use of BNN to build the highly nonlinear kinematic and dynamic models of a tendon-driven surgical robot, and exploit the information about the epistemic uncertainties by means of a Hierarchical MPC (Hi-MPC) control strategy. Simulation and real world experiments show that the method is capable of ensuring accurate tip positioning, while satisfying imposed safety bounds on the kinematics and dynamics of the robot.

Passive Model Predictive Control on a Two-Body Self-Referenced Point Absorber Wave Energy Converter

Wave energy is nowadays one of the most promising renewable energy sources; however, wave energy technology has not reached the fully-commercial stage, yet. One key aspect to achieve this goal is to identify an effective control strategy for each selected Wave Energy Converter (WEC), in order to extract the maximum energy from the waves, while respecting the physical constraints of the device. Model Predictive Control (MPC) can inherently satisfy these requirements. Generally, MPC is formulated as a quadratic programming problem with linear constraints (e.g., on position, speed and Power Take-Off (PTO) force). Since, in the most general case, this control technique requires bidirectional power flow between the PTO system and the grid, it has similar characteristics as reactive control. This means that, under some operating conditions, the energy losses may be equivalent, or even larger, than the energy yielded. As many WECs are designed to only allow unidirectional power flow, it is necessary to set nonlinear constraints. This makes the optimization problem significantly more expensive in terms of computational time. This work proposes two MPC control strategies applied to a two-body point absorber that address this issue from two different perspectives: (a) adapting the MPC formulation to passive loading strategy; and (b) adapting linear constraints in the MPC in order to only allow an unidirectional power flow. The results show that the two alternative proposals have similar performance in terms of computational time compared to the regular MPC and obtain considerably more power than the linear passive control, thus proving to be a good option for unidirectional PTO systems.

Plant-wide MPC control scheme for CO2 absorption/stripping system

Abstract The high contents of CO2 in natural gas processing industries cause various issues in the operation, and it is essential to reduce the amount of CO2 using amine-based absorption processes. An efficient and flexible control strategy is highly desirable for CO2 removal in CO2 absorption/stripping system. The excellent performance of Model Predictive Control (MPC) in setpoint tracking and disturbance rejection scenarios could make it a better option for the flexible controllability of a CO2 absorption/stripping plant. MPC performance depends significantly on the accuracy of the identified mathematical model of the plant. The state-space model is believed as the better option for MPC as it represents the plant model with true dynamics. Therefore, this study is focused on the design of the 2 × 2 MPC control strategy in MATLAB® MPC Designer Toolbox using 2nd order continuous-time state-space model. The main aim of this study is to develop a plant-wide control scheme based on MPC for the natural gas absorption/striping system. Step changes in the CO2 composition of sweet gas (±5%) and stripper temperature (±15%) have been introduced in the absorption/stripping simulation model. The results show that the MPC controller has achieved the new setpoint of CO2 composition within 0.5 sec in the setpoint tracking scenario. Similarly, the MPC controller has been able to reject the disturbances successfully introduced as ± 15% step change in stripper temperature within 7.5 sec. Hence, the performance of the MPC controller using the state-space model at higher step changes is adequate with no peak, closer to the setpoint, and no overshoot in the output.

Battery independent regenerative braking using model predictive control with auxiliary power consumers

Electric vehicles (EV) are able to regenerate the energy of approximately 85% of all braking procedures. In rare cases, e.g. if the battery is fully charged, the EV is not able to regenerate. Therefore, the regenerative braking system is not part of the service brake. Due to its heavy battery, the EV brakes are even larger than those of a conventional vehicle. This paper analyzes the potential of downsizing the friction brake by using auxiliary power consumers like the high-voltage heater (HVH), the air-conditioning system (AC) and low-voltage auxiliaries, taking into account their dynamic behavior. The cabin temperature has to be nearly constant for driver comfort. To ensure the assumptions made regarding the auxiliary consumers, measurements were taken from an EV (VW e‑Golf). Further, measurements of an air- and water-heating system were conducted. A model predictive control (MPC) strategy was used to distribute the power in an optimal way in consideration of the system dynamics. The MPC uses quadratic programming to optimize a cost function containing the dissipated brake energy and the cabin temperature deviation for a linearized model. The overall upshot of this feasibility study is the general capability of battery-independent recuperative braking in EVs and its effect on friction brake downsizing. The importance of the systems transient response was evaluated with a MPC control strategy.

Optimization Control of CVT Clutch Engagement Based on MPC

As an important part of continuously variable transmission (CVT) vehicle power transmission system, drive, neutral and reverse (DNR) wet clutch has the function of transmitting or interrupting vehicle power. However, due to the complex and variable working conditions of the clutch, it is difficult to achieve precise control of the clutch by the traditional control strategy. To solve this problem, a clutch control optimization algorithm based on model predictive control (MPC) is proposed. In order to identify and track the driver's launching intentions, a driver's launching intentions recognition system based on fuzzy neural network (FNN) is designed. The impact degree and friction work are taken as the evaluation standard of clutch control. The clutch controller is designed by using MPC control strategy, and the control effect is compared with the adaptive fuzzy neural network (AFNN) strategy. Finally, the validity of the control strategy is verified by simulation model and vehicle test. The results show that compared with the AFNN control strategy, the MPC control strategy can effectively control the clutch engagement and improve the vehicle launching quality.

Renewable source penetration and microgrids: Effects of MILP – Based control strategies

The implementation of the Distributed Generation (DG) concept requires to face with technical issues regarding integration and control of energy fluxes at the grid nodes. A predictive control strategy integrating renewable and non-renewable sources, as well as energy storage systems, is a potential solution to face with the aforementioned problems. The behavior of a smart building consisting of 30 apartments has been considered in this work. The Hybrid Renewable System (HRS) has been controlled by a Model Predictive Control (MPC) strategy. The HRS includes both sub-systems for the conversion of renewable energy sources and non-renewable ones, connected to the main grid. Several scenarios have been tested under different weather conditions and renewable sources penetration quotas. Results obtained with the MPC control strategy have been compared with a Rule Based Control (RBC) one, turning out that the use of MPC improves the integration of the residential microgrid with the renewable sources available at the grid thanks to the predictive system smoothing out the energy demand profile and absorbing the peak of production from the photovoltaic and wind farms, even in cases of high RES penetration.

Design of Model Predictive Controller for Pasteurization Process

This research paper is about developing a better type of controller, known as MPC (Model Predictive Control) for pasteurization process plant. MPC is an advanced control strategy that uses the internal dynamic model of the process and a history of past control moves and a combination of many different technologies to predict the future plant output.. The dynamics of the pasteurization process was estimated by using system identification from the experimental data. The quality of model structures like ARX, ARMAX, BJ and CT model structures was checked based on best fit with validation data, residual analysis and stability analysis. Auto-regressive with exogenous input (ARX322) model was chosen as a model structure of the pasteurization process dynamics and fits about 79.75% with validation data. Finally MPC control strategies were designed using ARX322 model structure.

Design of Model Predictive Controller for Pasteurization Process

This research paper is about developing a better type of controller, known as MPC (Model Predictive Control) for pasteurization process plant. MPC is an advanced control strategy that uses the internal dynamic model of the process and a history of past control moves and a combination of many different technologies to predict the future plant output.. The dynamics of the pasteurization process was estimated by using system identification from the experimental data. The quality of model structures like ARX, ARMAX, BJ and CT model structures was checked based on best fit with validation data, residual analysis and stability analysis. Auto-regressive with exogenous input (ARX322) model was chosen as a model structure of the pasteurization process dynamics and fits about 79.75% with validation data. Finally MPC control strategies were designed using ARX322 model structure.

Comparison of PID Controller with Model Predictive Controller for Milk Pasteurization Process

Proportional–Integral–Derivative (PID) controllers are used in many of the Industries for various process control applications. PID controller yields a long settling time and overshoot which is not good for the process control applications. PID is not suitable for many of the complex process control applications. This research paper is about developing a better type of controller, known as MPC (Model Predictive Control). The aim of the paper is to design MPC and PID for a pasteurization process. In this manuscript comparison of PID controller with MPC is made and the responses are presented. MPC is an advanced control strategy that uses the internal dynamic model of the process and a history of past control moves and a combination of many different technologies to predict the future plant output. The dynamics of the pasteurization process was estimated by using system identification from the experimental data. The quality of different model structures was checked using best fit with data validation, residual and stability analysis. Auto-regressive with exogenous input (ARX322) model was chosen as a model structure of the pasteurization process and fits about 80.37% with datavalidation. MPC and PID control strategies were designed using ARX322 model structure. The controller performance was compared based on settling time, percent of overshoot and stability analysis and the results are presented.

Model predictive control for batch processes: Ensuring validity of predictions

The intuitive and simple ideas that support model predictive control (MPC) along with its capabilities have been the key to its success both in industry and academia. The contribution this paper makes is to further enhance the capabilities of MPC by easing its application to industrial batch processes. Specifically, this paper addresses the problem of ensuring the validity of predictions when applying MPC to such processes. Validity of predictions can be ensured by constraining the decision space of the MPC problem. The performance of the MPC control strategy relies on the ability of the model to predict the behaviour of the process. Using the model in the region in which it is valid improves the resulting performance. In the proposed approach four validity indicators on predictions are defined: two of them consider all the variables in the model, and the other two consider the degrees of freedom of the controller. The validity indicators are defined from the latent variable model of the process. Further to this, these are incorporated as constraints in the MPC optimization problem to bound the decision space and ensure the proper use of the model. Finally, the MPC cost function is modified to enable fine case-specific tuning if desired. Provided the indicators are quadratic, the controller yields a quadratic constrained quadratic programming problem for which efficient solvers are commercially available. A fed-batch fermentation example shows how MPC ensuring validity of predictions improves performance and eases tuning of the controller. The target in the example provided is end-point control accounting for variations in the initial measurable conditions of the batch.