Advanced Model Predictive Control Research Articles

Innovative model predictive control for HVDC: circulating current mitigation and fault resilience in Modular Multilevel converters

This study presents an advanced Model Predictive Control (MPC) technique designed to mitigate Circulating Current (CC) in HVDC systems equipped with Modular Multilevel Converters (MMC). This MPC strategy eliminates the need for traditional PI regulators and pulse width modulation, improving system dynamics and control accuracy. It excels in managing output currents and mitigating voltage fluctuations in sub-module capacitors. Moreover, the paper introduces a novel communication-free Fault Ride-Through (FRT) method that makes a DC chopper redundant, enabling rapid recovery from disturbances. To reduce the computational burden of standard MPC algorithms, an aggregated MMC model is proposed, significantly decreasing the computational complexity. Simulation studies validate the new MPC algorithm’s capability in regulating AC side current, reducing CC, and ensuring capacitor voltage stability under varying conditions. The findings indicate that the proposed MPC controller outperforms traditional PI and PR-based methods, offering enhanced dynamic response, decreased steady-state error, and lowered converter losses, which contribute to smoother DC link voltages. Future research will focus on system scalability, renewable energy integration, and empirical validation through hardware-in-the-loop testing.

Enhanced Autonomous Speed Control System for Integrated Cars

The Automated Vehicle Speed Control System (AVSCS) is an embedded framework that utilizes specialized hardware and software components to automatically regulate a vehicle's speed. This system is designed to be implemented in a variety of vehicles, including cars, trucks, and autonomous vehicles, to enhance safe and efficient driving. The AVSCS comprises several essential elements: sensors, a processor or microcontroller, a control calculation algorithm, and a user interface. The sensors gather real-time data about the vehicle's current speed, weather conditions, and surrounding road environment, including wheel speed, GPS, radar, LIDAR, and cameras. The microcontroller or processor processes the sensor data and employs control algorithms, such as PID (Proportional-Integral-Derivative) controllers or advanced Model Predictive Control (MPC) techniques, to calculate the optimal vehicle speed. This information is then used to adjust the throttle or braking mechanisms accordingly. Additionally, the user interface allows drivers to customize the system or set their desired speed preferences, providing a seamless and personalized driving experience. The integration of the AVSCS into various vehicle types can significantly increase security, streamline traffic,, and reduce energy consumption, making it a crucial component of the connected and autonomous vehicle ecosystem. Keywords: vehicle speed regulator, GPS, radar, LIDAR, and cameras

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

An advanced control strategy of hybrid cooling system with cold water storage system in data center

The inefficient operation of cooling equipment is a significant impact factor to the high energy consumption of cooling system in data center. This study proposes an advanced model predictive control (MPC) strategy for a hybrid cooling with water storage system to improve energy efficiency and reduce the accumulation of cold storage losses. Mixed integer linear programming (MILP) in MPC strategy is used to optimize the operating parameters under free cooling, hybrid cooling, and mechanical cooling modes, further solving the problem of precise optimization for different modes. Taking Guangzhou city as an example, the equipment scheduling and the appropriate volume of cold water storage tank for MPC strategy are analyzed. The results indicate that, the emergency cold water storage tank 500 m3 only supports the efficient operation of cooling system under the maximum 60 % IT load rate, meanwhile, the optimal tank volume 1400 m3 could meet the 60–100 % IT load rate. Compared to Baseline strategy, the biggest reduction of annual energy consumption using MPC strategy would be attained by 12.19 % under free cooling mode, by 4.04 % under hybrid cooling mode, and by 22.15 % under mechanical cooling conditions at the 55 % IT load rate. Therefore, the MPC strategy proposed in this paper has important guiding significance for energy conservation and carbon reduction in global data centers.

An advanced observer-based nonlinear model predictive controller design for distribution networks in bushfire prone areas

An advanced observer-based nonlinear model predictive controller design for distribution networks in bushfire prone areas

Optimal multi-layer economical schedule for coordinated multiple mode operation of wind–solar microgrids with hybrid energy storage systems

The aim of this paper is the design and implementation of an advanced model predictive control (MPC) strategy for the management of a wind–solar microgrid (MG) both in the islanded and grid-connected modes. The MG includes energy storage systems (ESSs) and interacts with external hydrogen and electricity consumers as an extra feature. The system participates in two different electricity markets, i.e., the daily and real-time markets, characterized by different time-scales. Thus, a high-layer control (HLC) and a low-layer control (LLC) are developed for the daily market and the real-time market, respectively. The sporadic characteristics of renewable energy sources and the variations in load demand are also briefly discussed by proposing a controller based on the stochastic MPC approach. Numerical simulations with real wind and solar generation profiles and spot prices show that the proposed controller optimally manages the ESSs, even when there is a deviation between the predicted scenario determined at the HLC and the real-time one managed by the LLC. Finally, the strategy is tested on a lab-scale MG set up at Khalifa University, Abu Dhabi, UAE.

An advanced control strategy for optimizing the operation state of chillers with cold storage technology in data center

Data centers facing huge energy consumption challenges, the chiller is one of the main energy expenditure equipment. This study proposed a novel efficient operation strategy for chillers integrated with cold water storage technology. An advanced model predictive control (MPC) was developed to regulate the running parameters of the chillers and cold water storage device, targeting the maximum energy efficiency of cooling system. Specifically, the mixed integer linear programming (MILP) algorithm was constructed in MPC with low computer calculation cost to solve the optimization problem. The performance of the MPC strategy was validated through an actual data center test located at Guangzhou city and further comprehensively assessed through annual simulations. The relative error in terms of refrigeration capacity and cooling capacity of cold water storage device between simulation and field test was less than 5 %. During the on-site testing, compared with a Baseline strategy, the coefficient of performance (COP) of the MPC strategy increased by 1.96 on average with the cooling system energy consumption reduced by 5.8 %, and the power usage effectiveness (PUE) was reduced by 0.013. The annual PUE decreased by 0.018 and the annual electricity cost decreased by 21 % when the IT power was 4570 kW based on the simulation. In addition, the effect of model mismatch was quantified by setting the deviation degree of the chiller partial load rate (PLR).

Do-mpc: Towards FAIR nonlinear and robust model predictive control

Over the last decades, model predictive control (MPC) has shown outstanding performance for control tasks from various domains. This performance has further improved in recent years with advanced MPC schemes for nonlinear systems under uncertainty including economic control objectives. These recent improvements often fail to bridge the gap between MPC researchers and control practitioners in academia and industry, where classical control approaches and traditional linear MPC still dominate most applications. This is despite the fact that advanced MPC controllers can lead to significant energy savings, yield improvements, safer operation and other benefits.In this work, we identify four main obstacles hindering the widespread adoption of advanced MPC methods. These are the unavailability of models, the challenges associated with deploying complex controllers on physical systems, the scarcity of rapid prototyping tools for advanced methods and the limited reproducibility and reusability of advanced MPC controllers and their results. We find that the FAIR principles (findable, accessible, interoperable, reusable) for scientific data-management and research software can play an important role in tackling these obstacles. Following these guidelines, we discuss FAIR solutions and present the open-source software do-mpc as a concrete implementation. The presented solutions include interoperability with neural network toolboxes to simplify nonlinear system identification, interoperability with the OPC UA communication protocol for deployment, and a reproducible data-sampling framework for transparent controller validation, system identification and approximate MPC. The potential of the proposed solutions is illustrated with several simulation studies.

A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience

Data-driven intelligent energy management strategy (EMS) helps to further improve the performance and efficiency of fuel cell hybrid electric bus (FCHEB). However, most deep reinforcement learning (DRL) algorithms suffer from disadvantages such as overestimation and poor training stability, which limit the optimization effectiveness of the strategy. In addition, DRL-based EMSs tend to achieve good control only for the set optimization objectives and cannot be generalized to optimization objectives beyond the reward function. To solve the above problems, a novel health-aware DRL energy management for FCHEB is proposed in this paper. Firstly, based on the actual collected city bus driving cycles, a large amount of high-quality learning experience containing health-aware information is obtained through an advanced model predictive control strategy. Secondly, the state-of-the-art Twin Delayed Deep Deterministic policy gradient (TD3) algorithm is combined with offline high-quality learning experience to address the inherent shortcomings during the “cold-start phase” and to enhance the generalization capability of the proposed strategy. Finally, validation results showed that the proposed EMS improves training efficiency by 61.85% and fuel economy by 7.45%, extends fuel cell life by 4% and battery life by 19.4% compared to the conventional TD3-based EMS.

Efficiency and dynamic characteristics of improved dual‐stage power converter setup with advanced model predictive controller for electric vehicle battery charging

SummaryThis paper proposes an advanced power converter setup to charge an electric vehicle battery from a renewable energy source, such as solar photovoltaic. An improved Z‐source DC–DC boost converter (IZSBC) is utilized in the first stage. A synchronous rectifier (SR) of a phase‐shifted full‐bridge converter (PSFBC) with continuous conduction mode (CCM) is employed in the second stage using the zero voltage switching (ZVS) technique to provide isolation between the source and the load. To address the disadvantage of undesirable dynamic characteristics of the PSFBC‐SR, an advanced voltage control using the advanced model predictive controller (AMPC) is suggested to improve the dynamic characteristic of the PSFBC‐SR converter and prevent output voltage overshoot. An AMPC‐based new control method is preferred to eliminate body‐diode conduction for improved efficiency at any load conditions. Simulation and experimental designs of 0.5 kW with a 25 V DC input from the SPV, a boosted output of 100 V for a 60 V, 16 Ah battery bank and switching frequencies of 100 and 250 kHz for IZSBC and PSFBC‐SR, respectively, are produced and evaluated. Results show an efficiency of 93.7% with 31.44 W loss in simulations and 93.94% with 30.25 W loss in hardware tests. The preferred system has fast dynamics and is robust to sudden load impedance changes, thereby improving current tracking and reducing converter losses. The AMPC strategy proposed in this paper can increase conversion efficiency and control the output voltage without additional gain tuning.

Formula omitted]-gap metric based multi-model predictive control of an ocean current turbine system with blade pitch failures

In this article, a method of combining multiple linear models is developed and used by an advanced model predictive control algorithm for ocean current turbine flight control. The developed model bank consists of faulty and healthy linear models created for the ocean current turbine system. Each linear model is used to compute individual control actions using the model predictive control framework, which are combined to generate the aggregate control action that is applied to the system. A weighted average defined by the ν-gap distance between the current linear approximation of system dynamics and each individual linear system is used to compute the aggregate control action. The multi-model predictive control strategy is applied to control the ocean current turbine to mitigate performance degradation that may occur due to failures in the system. The proposed control framework’s effectiveness is shown with the renewable power generation improvement during a faulty case.

Novel enhancement of energy distribution for marine hybrid propulsion systems by an advanced variable weight decision model predictive control

Marine hybrid technology is attracting increasing research interest thanks to its power versatility and potential economic advantages. However, its overall quality performance is determined by the energy management strategy. This research deals with a variable weighted decision model predictive control (VWDMPC) for marine hybrid energy management, where the main task is to dynamically weight-tuning and optimally allocate energy to obtain the optimal trade-off in terms of fuel consumption, power performance, and energy allocation. For the sake of enhancing the real-time and adaptability of VWDMPC, we utilize the KKT condition to compress the optimal energy management problem and the weight tuning process in one, which cleverly simplifies the optimization process. Performance experiments are performed on a test bench and a real-time hardware execution platform. Different weighting decision comparison scenarios were designed to evaluate the power performance improvement and equivalent fuel consumption of the proposed VWDMPC and give the best proportional weights (0.33,0.27,0.2,0.2) that take both aspects into account. Moreover, this paper also provides validation and feedback based on the evaluation results, which show that the optimal combination of weights that take into account economy and dynamics ranges from (0.3,0.3,0.2,0.2)-(0.33,0.27,0.2,0.2) based on the radar plot drawn from the performance index parameters.

Advanced model predictive control strategies for evaporation processes in the pharmaceutical industries

In this paper we present a framework to design control systems for an evaporation process in the pharmaceutical industry with the aim to deliver guaranteed operability for different molecules and under different thermodynamic scenarios. Based on a mathematical model developed within the gPROMS platform calibrated and validated with real data from experiments, three control methods are implemented and compared, (i) Proportional Integrative Derivative control (PID), (ii) Model Predictive Control (MPC) and (iii) explicit/multi-parametric Model Predictive Control (mp-MPC). The performance and limits of the derived control schemes are then established and tested for reference tracking as well as disturbances rejection.

Model predictive control of power plant cycling using Industry 4.0 infrastructure

This work involves the Industry 4.0 infrastructure developed at West Virginia University (WVU) for process systems applications. This infrastructure emulates an interconnected environment, enabling communication and data sharing among different components for use in academic and industrial settings. The current infrastructure encompasses a power plant model interacting with online load demand, distributed control systems, and data analytics components. The developed model of a sub-critical coal-fired power plant is employed to evaluate classical and advanced control strategies using this infrastructure under different operating conditions. Specifically, the control strategies evaluated include classical proportional–integral–derivative (PID) and advanced model predictive control (MPC) structures, focusing on the dynamic matrix control (DMC) approach with an in-house modified sequential quadratic programming (SQP) solver. The MPC approach is developed and simulated in closed loop to address setpoint tracking and load-following scenarios under power plant cycling conditions. In this infrastructure, the PI System centralizes all the information received from the power plant model and the online power demand and sends the control actions calculated by the MPC back to the power plant model for implementation. Results of the implementation of these control strategies are discussed focusing on power plant operating regions associated with cycling.

An advanced control method for fuel cells - Metal hydrides thermal management on the first Italian hydrogen propulsion ship

As International Maritime Organization set 2030 and 2050 targets to reduce CO2 emissions in maritime sector, the investigation of innovative clean solution as hydrogen fuel cells for clean energy generation onboard is gaining more and more value.The present study investigates the thermal integration between PEM fuel cells and metal hydrides (for hydrogen storage) on board the first Italian zero-emissions ship with hydrogen and batteries propulsion, named ZEUS, built by Fincantieri Yard and launched in 2022. A model-based approach is developed to ensure the system's control at different load demands for the vessel, including transient conditions. The study focuses on the most critical conditions for fuel cells during navigation, namely from maximum to minimum power and vice-versa. Load reduction does not imply particular issues, while load maximum increase may cause some problems in terms of stability, negatively affect fuel cell stacks lifetime. Three solutions are investigated and compared to solve the problem to achieve a safe and robust control system: (i) decrease the current ramp for fuel cells from 50 to 10 A/s; (ii) introduce an intermediate load step; (iii) adopt an advanced Model Predictive Control strategy.

Common-Mode Voltage Mitigation of Dual T-Type Inverter Drives Using Fast MPC Approach

This article proposes an intensive study for common-mode voltage (CMV) elimination/reduction in a five-level dual T-type multilevel converter (MLC) drive. An advanced and fast model predictive control (MPC) approach is developed for CMV reduction (CMVR) and CMV elimination based on the preselection of the switching states (SS). In addition, the proposed approach considers the torque control and the capacitor balancing of the proposed drive as essential aspects of MLC drives. A detailed study of the impact of the converter SS on the CMV is presented. The main features of the proposed MPC approach are reducing the complexity of the cost-function, the tuning burden of the weighting factors, and the computation time by more than 80% compared to conventional MPC techniques. A hardware implementation based on Silicon Carbide T-type MLC is built. The experimental results demonstrate the effectiveness of the proposed MPC approach to mitigate/eliminate the CMV along with achieving the torque command and balancing the dc-link capacitors. Unlike conventional CMVR techniques, the proposed approach reduces the CMV by 75% of its normal values without jeopardizing the harmonic contents, drive efficiency, and flux as well as torque ripples.

Novel enhancement of energy management in fuel cell hybrid electric vehicle by an advanced dynamic model predictive control

In this paper, an Advanced Dynamic Model Predictive Control (AMPC) based on a Nonlinear Model Predictive Control (NMPC) framework with a multi-objective cost function driven by dynamic weights is proposed to improve the energy performance of fuel cell hybrid electric vehicles whilst prolonging their component lifetime. By the use of dynamic weights, the cost function is effectively formulated as the combination of fuel consumption, rate of change of fuel cell power, battery power, the fuel cell efficiency, state of charge of the battery, and their temperatures. In order to enhance the adaptability of the AMPC, a Fuzzy Cognitive Map (FCM) is then newly designed to regulate online the dynamic weights to adjust the importance of each cost component according to the conditions prevailing during driving. A comparative study between the proposed AMPC, a constant weight based NMPC and a conventional NMPC having cost function with fewer objectives has been carried out by means of simulation using a FCHEV model from the simulation tool ADVISOR to illustrate the efficacy of the proposed AMPC.

1005-P: A Multimodel Model Predictive Controller Can Improve Glucose Control in Children with Type 1 Diabetes (T1D)

Background: Many youth with type 1 diabetes do not achieve glycemic targets motivating the development of new therapeutic options for this population. We tested an automated insulin delivery system (AID) that uses a multi-model model predictive controller in children with T1D. Methods: We conducted a 40-hour free-living clinical trial to assess our automated insulin delivery algorithm in children aged 7-13 years old with T1D (mean age 10.5±2.0 years, HbA1c 7.9±0.7%, duration of T1D 5.2±2.8 years) . Participants underwent a 7-day run-in period on sensor-augmented pump therapy followed by two 40-hour intervention periods composed of four hours of open-loop and 36 hours of AID use. Controller settings were identical in both intervention periods. Mean time-in-ranges were compared by paired T-test. Results: Time in target (3.9-10mmol/L) and time in hyperglycemia (>10mmol/L) improved during intervention periods vs. run-in (52.2% vs. 65.6%; p<0.and 45.1% vs. 30.8%; p=0.respectively) while time in hypoglycemia (<3.9mmol/L) during intervention periods was unchanged from the run-in (1.8 vs. 2.0%, p=0.96) . Conclusion: Early results suggest our advanced model predictive controller improves glucose control in a 7-13-year-old population with T1D. Larger and longer trials are warranted. Disclosure E.Palisaitis: Other Relationship; Eli Lilly and Company. R.Jones: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. M.Katz: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. H.Wolpert: Employee; Lilly Diabetes. A.El fathi: Other Relationship; Eli Lilly and Company. A.Haidar: Consultant; Eli Lilly and Company, Research Support; ADOCIA, Dexcom, Inc., Eli Lilly and Company, Tandem Diabetes Care, Inc. J.E.Von oettingen: None. L.Legault: Advisory Panel; Abbott Diabetes, Insulet Corporation, Novo Nordisk A/S, Other Relationship; Eli Lilly and Company, Research Support; AstraZeneca, Merck & Co., Inc. Funding Eli Lilly

An Eco-Driving Controller Based on Intelligent Connected Vehicles for Sustainable Transportation

The rapid increase in the number of vehicles has brought significant challenges to energy conservation and environmental sustainability. To solve these problems, various frameworks and models based on intelligent connected vehicles (ICVs) have been identified for road capacity improvement and fuel consumption reduction. In this paper, an eco-driving controller with ICVs was first proposed by combining vehicular dynamics with wireless communication technologies, where the nodes that can implement perception and control in a simulated complex traffic environment have been deployed. Then, the information of the surrounding environment, including the preceding vehicles, was obtained through a wireless communication module based on the technology of vehicle to everything (V2X). Besides, the advanced model predictive control (MPC) strategy was integrated into the ICV controller with the objectives of minimizing the driving spacing and improving environmental sustainability. Finally, a co-simulation platform for ICVs based on a robot operating system (ROS) and PreScan software was constructed, and the dynamic characteristics of the controller were verified in three aspects, including car-following behaviors, fuel efficiency improvement, and carbon dioxide emission reduction. The proposed controller showed that it can reduce fuel consumption by 3.71% and reduce carbon dioxide emissions by 3.42%, in the scenarios of a preceding vehicle with constant velocity, and by 6.77% and 7.91%, respectively, in a preceding vehicle with variable velocity scenario. The demonstrated experimental results show that the proposed controller can effectively reduce fuel consumption and emissions during car-following.

Advanced Temperature Control Applied on An Industrial Box Furnace

Abstract The current control system of an industrial box furnace usually uses a proportional-integral-derivative (PID) controller. Typically, this control system requires on-site tuning and specific heuristic knowledge, such that the furnace can have acceptable performance but not optimal. However, by using a proper model in the operating range of the furnace from the designing phase, it can be designed a more efficient control system capable to reach better performance in this industrial process. In this sense, the main objective of this paper is the design and validation of an advanced control strategy in the early design stage of an industrial box furnace. The goals are to decrease the controller tuning time during the furnace commissioning process, improve the furnace performance with respect to classical industrial controllers, and decrease the system energy expenses. Thus, a comparative analysis in a simulated environment has been carried out between the performance of a PID controller by its typical industrial use, a model predictive control (MPC) due to the optimal results in similar industrial processes, and a virtual reference feedback tuning (VRFT) control by its feasibility of implementation. Results show that the advanced MPC and VRFT controllers are more efficient in the used fuel/gas than the PID. MPC shows the best balance between performance and actuation, it improves the control performance up to 87.2% with respect to the VRFT controller.