Hybrid Model Predictive Control Strategy Research Articles

Developing cyber-physical system and digital twin for smart manufacturing: Methodology and case study of continuous clarification

Biopharmaceutical production has recently begun transitioning toward the construction of highly advanced, digitalised production facilities, as per Industry Revolution 4.0 or smart manufacturing. Clarification is the bridging step in biotherapeutics continuous production, and this study proposes a Digital twin (DT) and a cyber-physical system (CPS) for the Cadence™ Acoustic Wave Separator (CAS), that uses the novel acoustic wave separation (AWS) technology for clarification of Chinese hamster ovary (CHO) cells. While the CPS - DT serves as the core, a real-time model based on the empirical and mechanistic relationship has been implemented in tandem to present a highly reliable hybrid model so as to improve the adaptability of the model. The framework employs a distributed control system (DCS), the heart of the CPS architecture, that updates the parameters of the physical system in real-time, thereby simplifying process control. To evaluate the hybrid-model predictive control strategy in real time, three case studies were conducted, which involved introducing abrupt turbidity pulses of high, low, and multi values. The sudden deviation in the turbidity of the feed sample was controlled by adjusting the acoustic power of the chamber (the control variable). The use of the proposed controller resulted in a 5 % mean deviation from the setpoint in the first chamber, while a > 33 % mean deviation resulted in the absence of the controller. The study's outcome demonstrates the high effectiveness of hybrid model-based CPS - DT control in consistently achieving high cell separation efficiency (> 90 %) and integrating the unit operation in a continuous processing train to establish a smart manufacturing prototype for the biopharmaceutical industry.

Software-defined control of an emulated hydrogen energy storage for energy internet ecosystems

The increasing heterogeneity and scalability of the Internet of everything, especially, the Energy Internet (EI), is a prompt for novel engineering paradigms. The current infrastructure of hardware technologies besides the available software stack is becoming obsolete for handling the advanced requirements of the smart energy ecosystem. Accordingly, software definitions of the Internet of Things (IoT) stack functions are gaining interest to provide more flexible and scalable implementations. This paper investigates a sophisticated softwarization explicit hybrid model predictive control strategy for energy storage facility systems through the accessor design pattern. This strategy is presumed to support and facilitate the integration of higher-level analytic and control functionalities within the IoT stack at edges and nodes. Simulation is conducted with the Ptolemy II software to evaluate the execution semantics of the proposed strategy. In addition, real-time experiments on a lab-scale hydraulic tank process with both abrupt and gradual commands test scenarios showed the efficiency and reliability of the accessor-based controllers for achieving the control objectives for the benchmark storage facility with an abstract implementation. The analysis shows that this process can serve as a benchmark to mimic the charge and discharge cycles of storage elements in EI ecosystems.

Hybrid Model Predictive Control Scheme for Controlling Temperature in Building Under Uncertainties

Hybrid Model Predictive Control Scheme for Controlling Temperature in Building Under Uncertainties

Model predictive control for a heat booster substation in ultra low temperature district heating systems

District heating systems may support an increased penetration of stochastic renewable energy technologies and a reduction in centralized combined heat and power plants to reduce carbon dioxide emissions. Ultra low temperature district heating minimizes transport heat losses while enabling the utilization of low-grade surplus heat. Local heat booster substations can heat water to useable temperatures using a heat pump and a hot water tank for storage and flexible operation. This paper proposes a hybrid model predictive control strategy in which an existing heat booster substation is modelled and its charging schedule optimized in real-time over a 24-h forecasted prediction horizon. This enables load shifting whereby scheduling of the heat pump minimizes operation costs. The realisation of energy flexibility can support greater utilization of renewable energy sources and surplus heat in energy supply systems to reduce primary energy consumption. The linear hybrid model predictive controller was successfully implemented in a real 22-flat multifamily building in Copenhagen to verify the control strategy. A comparison of the proposed model predictive control scheduling to the standard rule-based control showed average savings of 23 % on the electricity costs.

Enhancing energy efficiency and comfort in buildings through model predictive control for dynamic façades with electrochromic glazing

Reducing building energy consumption while ensuring indoor comfort conditions is driving the building envelope transition from static to responsive . In this context, electrochromic windows represent a promising solution to manage optical and thermal requirements in response to changing boundary conditions or user requirements, and control strategies play a central role in the exploitation of this technology. The present paper proposes a Hybrid Model Predictive Control strategy for the online operation optimization of an electrochromic-based active façade . The controller was implemented and tested on a simulated case study. This was achieved by co-simulating the optimal controller which used reduced data-driven grey-box model, with a high accuracy white-box physical model. This setup enabled to close the control loop and to perform an accurate performance benchmarking. Results showed that the proposed Hybrid Model Predictive Control strategy outperformed two baseline Rule Based Controllers, reducing simultaneously energy consumption, peak power and percentage of discomfort hours by up to 82%, 71% and 51% respectively. In comparison with the two baseline controllers and considering all the scenarios, the overall energy consumption could be reduced by approximately 40%–60% on average. In addition the work demonstrates how tuning the Hybrid Model Predictive Control weights adds flexibility to the controller, significantly changing its behaviour to meet the requirements of the designer. • A test cell equipped with a switchable electrochromic window was modelled. • A grey-box model of the zone and window was developed and calibrated. • A Hybrid Model Predictive Control strategy was formulated for the window control. • A comprehensive controller performance benchmarking was performed.

Economic Nonlinear Predictive Control for Real-Time Optimal Energy Management of Parallel Hybrid Electric Vehicles

This article presents an economic nonlinear hybrid model predictive control strategy for optimal energy management of parallel hybrid electric vehicles. Hybrid electric vehicles are controlled for operation in various driveline modes and the associated optimal control problem involves both continuous and discrete control variables. To solve the resultant mixed-integer nonlinear optimal control problem, we propose a hierarchical supervisory control architecture that consists of demand prediction, driveline mode determination, and real-time optimization. These three modules are designed independently and connected in series to perform computer-aided control. The demand prediction module uses a times series model to forecast the mechanical traction power requests of the driver over a prediction horizon based on vehicle speed, road grade, acceleration pedal scale, brake pedal scale, and past and current power demands. For a given forecasted power demand profile, the mode determination module decides a sequence of driveline modes that are presumed to be operated over the prediction horizon. The model-based real-time optimization corresponding to nonlinear model predictive control computes the optimal motor power over a prediction horizon, and the receding horizon scheme as feedback control is applied to repeat the processes of the three control modules. A dedicated case study with real driving data obtained from Hyundai IONIQ PHEV 2018 is presented to demonstrate the effectiveness in fuel economy and emission reduction offered by the proposed optimal energy management strategy. The proposed hierarchical real-time predictive optimization-based strategy is competitive with any exiting power management strategies such as dynamic programming and equivalent consumption minimization strategy in fuel economy and emission reduction while showing better charge-sustaining capability. This trade-off between fuel economy and charge-sustainability can be further improved by tuning the hyper-parameters in the proposed optimal control problem.

A distributed model predictive control strategy for finite-time synchronization problems in multi-agent double-integrator systems

In this paper, a synchronization problem for a group of agents described by double-integrator dynamics and time-varying communication network is formally addressed. Specifically, the main aim consists in ensuring that the agents, organized as a swarm, are capable to synchronize in finite-time the swarm centroid to a given reference trajectory and to keep this property for all future time instants. To this end, an ad-hoc distributed control architecture is developed by taking advantage from two key ingredients: a novel swarm formation modelling that guarantees finite-time convergence properties of the agent kinematics and a hybrid model predictive control scheme in charge to adequately exploit such a kinematics description. One of the relevant features of the resulting distributed algorithm relies on the fact that both parallel and sequential paradigms are used with the aim to ensure collision avoidance requirements amongst the involved agents.

Hybrid Model Predictive Control Strategy of Supercapacitor Energy Storage System Based on Double Active Bridge

In order to solve the problem of which the dynamic response of a supercapacitor (SC) is limited due to the mismatch dynamic characteristics between the DC/DC converter and supercapacitor in an energy storage system, this paper proposes a hybrid model predictive control strategy based on a dual active bridge (DAB). The hybrid model predictive control model considers the supercapacitor and DAB in a unified way, including the equivalent series resistance and capacitance parameters of the SC. The method can obtain a large charging and discharging current of the SC, thereby not only improving the overall response speed of the system, but also expanding the actual capacity utilization range of the SC. The simulation results show that compared with the model prediction method of the dual active bridge converter, the proposed control method can effectively improve the overall response speed of the system, which can be improved by at least 0.4 ms. In addition, the proposed method increases the actual upper limit of the SC voltage, reduces the actual lower limit of the SC voltage, and then expands the actual capacity utilization range of the SC by 18.63%. The proposed method has good application prospects in improving the dynamic response performance of energy storage systems.

Dynamical Modeling and Predictive Control of Bus Transport Systems: A Hybrid Systems Approach

Abstract Bus operations, due to their unstable nature, are inefficient when left uncontrolled with respect to retaining headways. Irregularities such as bus bunching lead to loss of time and decrease bus service quality. Development of bus transport system management schemes to avoid bus bunching and improve performance are of high importance, and has thus been the focus of many works in the public transport systems literature. Motivated by the importance of developing bus control strategies for improving performance, and specifically by the lack of a detailed but computationally efficient mathematical model describing bus transport system dynamics in the literature (which can facilitate model-based control design), we propose a mixed logical dynamical model of a single loop bus transport system, which involves both continuous (e.g., bus positions) and binary (e.g., the state of a bus regarding whether it is holding at a certain stop or not) states. Furthermore, we develop a hybrid model predictive control scheme with actuation via bus speeds, which can regularize headways and improve bus service quality. Performance of the predictive controller is evaluated via simulation experiments using the proposed model, where the passenger demands and maximum bus speeds are extracted from data collected from the bus network of the city of Fribourg. Results indicate the potential of the proposed controller in avoiding bus bunching and decreasing passenger travel times.

Hybrid Model Predictive Power Management of a Battery‐Supercapacitor Electric Vehicle

AbstractRecently, battery powered electric vehicles (EV), such as the Tesla Model S, have reached commercialization. Future EVs will likely pair the battery with a supercapacitor to extend battery life by minimizing the effects of high power and rapidly fluctuating loads. As such, this paper investigates optimal power management of an EV powertrain with both a battery pack and a supercapacitor as energy sources. The battery‐supercapacitor‐based powertrain has four distinct controllable modes, each with a unique set of dynamics and constraints. Unique power flow expressions for the supercapacitor, vehicle motion, and drive system induction motor are presented. The overall powertrain is represented as a switched interconnected dynamical system having both differential and algebraic constraints in each mode of operation. Constrained by this model, this paper sets forth a hybrid model predictive control strategy for minimizing velocity tracking error and frictional braking (to encourage regenerative braking) while encouraging fast recharge of the supercapacitor. The optimization is performed using a relaxed representation of the control problem (termed the embedding method), collocation for discretization, and traditional nonlinear programming to compute the mode and continuous control inputs. The methodology avoids the computational complexity associated with alternative approaches such as mixed‐integer programming. The developed optimization methodology is shown to be useful as a rapid iterative prototyping tool by varying vehicle mass and electric drive maximum power to find combinations that result in satisfactory tracking performance of a trapezoidal drive profile. Performance of the originally specified powertrain and a first design iteration are compared using simulations of the EPA highway and urban drive profiles and the new European drive cycle to verify the first design iteration gives improvement.

Optimized treatment of fibromyalgia using system identification and hybrid model predictive control

The term adaptive intervention is used in behavioral health to describe individually tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.

Hybrid predictive control of a coaxial aerial robot for physical interaction through contact

This paper deals with the control of an unmanned coaxial helicopter designed towards active physical interaction with its environment. The system design is tailored to robust interaction through contact (e.g. docking and sliding on walls). Due to the rapid change of the dynamics from free-flight to the helicopter subject to the contact forces, a hybrid model is developed. This model captures all modes of the vehicle dynamics and is the basis for the design of a hybrid model predictive control strategy that ensures the stability of the hybrid dynamics and provides optimal maneuvering, docking on walls as well as sliding on them. The proposed control law is evaluated through experimental studies.

A Hybrid Model Predictive Control Strategy for Optimizing a Smoking Cessation Intervention.

The chronic, relapsing nature of tobacco use represents a major challenge in smoking cessation treatment. Recently, novel intervention paradigms have emerged that seek to adjust treatments over time in order to meet a patient's changing needs. This article demonstrates that Hybrid Model Predictive Control (HMPC) offers an appealing framework for designing these optimized, time-varying smoking cessation interventions. HMPC is a particularly appropriate approach as it recognizes that intervention doses must be assigned in predetermined, discrete units while retaining receding-horizon, constraint-handling, and combined feedback and feedforward capabilities. Specifically, an intervention algorithm is developed here in which counseling and two pharmacotherapies are manipulated to reduce daily smoking and craving levels. The potential usefulness of such an intervention is illustrated through simulated treatment of a quit attempt in a hypothetical patient, which highlights that prioritizing reduction in craving over total daily smoking levels significantly reduces craving levels, suppresses relapse, and successfully rejects time-varying disturbances such as stress, all while adhering to several practical operational constraints and resource use considerations.

A Hybrid MPC Strategy Applied to Flotation Process

Several dynamic models are able to represent the phenomenological behaviour of a flotation process. In addition, the interest in developing control strategies for large-scale processes has led to formulate novel methodologies which allow to consider the global performance of a plant, facilitating the design, validation and evaluation of more complex optimizing control strategies. In this work, we first develop a dynamic hybrid model for flotation which is calibrated with industrial data. Subsequently, a hybrid prediction model is obtained by applying identification techniques to the different scenarios and it is used to formulate a hybrid model predictive control (HMPC) strategy for flotation. Our simulation results show that the proposed methodology is suitable for modelling the behaviour of a flotation process and its control stage. This is achieved minimizing the tail grade of a flotation line considering several operating modes and constrains, providing a robust hybrid model predictive control strategy.

Vehicle Yaw Stability Control by Coordinated Active Front Steering and Differential Braking in the Tire Sideslip Angles Domain

Vehicle active safety receives ever increasing attention in the attempt to achieve zero accidents on the road. In this paper, we investigate a control architecture that has the potential of improving yaw stability control by achieving faster convergence and reduced impact on the longitudinal dynamics. We consider a system where active front steering and differential braking are available and propose a model predictive control (MPC) strategy to coordinate the actuators. We formulate the vehicle dynamics with respect to the tire slip angles and use a piecewise affine (PWA) approximation of the tire force characteristics. The resulting PWA system is used as prediction model in a hybrid MPC strategy. After assessing the benefits of the proposed approach, we synthesize the controller by using a switched MPC strategy, where the tire conditions (linear/saturated) are assumed not to change during the prediction horizon. The assessment of the controller computational load and memory requirements indicates that it is capable of real-time execution in automotive-grade electronic control units. Experimental tests in different maneuvers executed on low-friction surfaces demonstrate the high performance of the controller.

A hybrid model predictive control scheme for containment and distributed sensing in multi-agent systems

This paper proposes a control scheme for distributed sensing using a leader/follower multi-agent architecture. The control objective is to make a group of mobile agents cover and sense a sequence of regions of interest. More specifically, when the leaders reach a new target region, they stop until the followers have performed a sensing task. Furthermore, the followers must be contained inside the convex-hull of the leaders’ positions during the motion. Key features of our method, that combines hybrid control with Model Predictive Control (MPC) techniques, are the possibility to take into account input constraints in order to plan the sensing maneuver and the ability of the followers to detect containment violations by simple computation based on the available information about the leaders’ positions.

Hybrid Model Predictive Power Management of A Fuel Cell‐Battery Vehicle

AbstractThis paper considers optimal power management of a fuel cell‐battery hybrid vehicle (FCHV) powertrain having three distinct modal configurations (modes): electric motor propelling/battery discharging, propelling/charging, and generating/charging. Each mode has a distinct set of dynamics and constraints. Using component dynamical/algebraic models appropriate to power flow management, the paper develops a supervisory‐level switched system model as an interconnection of subsystems. Given the model, the paper sets forth a hybrid model predictive control strategy based on a minimization of a performance index (PI) that trades off tracking and fuel economy in each operational mode. Specifically, the PI trades off velocity tracking error, battery state of charge variance, and electric drive and hydrogen fuel usages while penalizing frictional braking to encourage regenerative braking. The optimization is performed using an embedded system model and collocation with matlab's fmincon to compute mode switches and continuous time controls. The methodology avoids the computational complexity of alternate approaches based on, e.g., mixed integer programming. Projection methods for approximating the switched system solution from the embedded solution are empirically evaluated. To demonstrate the methodology, an example of a FCHV is simulated using three standard velocity driving profiles: a sawtooth profile with a hill climb, the EPA urban dynamic driving schedule, and the New European Driving Cycle. Also, drive cycle fuel usage is compared to that from the Equivalent Consumption Minimization Strategy.

Hybrid Model Predictive Control of a Solar Air Conditioning Plant

This paper describes the development and experimental validation of a multi-layer hybrid controller for optimizing energy management in a solar air conditioning plant. The hybrid nature of the process is due to its multi-mode structure: the refrigeration circuit can be fed by flat solar collectors, a storage system, by an auxiliary gas heater, or by a combination of them. The selection of the operating mode is obtained by switching electrovalves, pumps, and three-way mixing valves. The proposed multi-layer hybrid controller consists of a high-level supervisor that decides on-line the optimal operating mode through a hybrid model predictive control strategy, a static lower-level controller defining proper set-points for the chosen mode, and existing standard low-level controllers that ensure robust tracking of such set-points. The overall controller was designed in Matlab/Simulink using the Hybrid Toolbox, and then tested experimentally a real process, showing the effectiveness of the approach.

A hybrid model predictive control strategy for nonlinear plant-wide control

Abstract A plant-wide control strategy based on integrating linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The hybrid method is applicable to plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems that interact via mass and energy flows. LMPC is applied to the linear subsystems and NMPC is applied to the nonlinear subsystems. A simple controller coordination strategy that counteracts interaction effects is proposed for the case of one linear subsystem and one nonlinear subsystem. A reactor/separator process with recycle is used to compare the hybrid method to conventional LMPC and NMPC techniques.