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In order to regulate work in process (WIP) to the desired value in the job shop production control system, capacity adjustment as an effective and efficient measure, which is typically achieved by flexible staffs and working time. In this paper, instead of traditional labour-oriented approaches, we consider a machinery-based capacity adjustment via reconfigurable machine tools (RMTs) to compensate for unpredictable events. To this end, we employ model predictive control (MPC) in combination with genetic algorithm (GA) to explicitly consider complex reconfiguration strategies and address the related integer assignment optimisation problems. To further reduce energy consumption and avoid frequent and unnecessary reconfigurations while keeping a certain level of performance, we adopt an event-triggered MPC scheme with the proposed 'Double-layer event-triggering conditions'. Through extensively illustrated simulations, we demonstrate the effectiveness and plug-and-play availability of the proposed method for a six-workstation four-product job shop system and compare it to a state-of-the-art method. [ABSTRACT FROM AUTHOR]

The escalating prevalence of formation flights in space missions has led researchers to intensify their focus on designing optimal control systems for satellite formation motion along reference orbits, with the aim of reducing tracking error and energy consumption. However, conventional controllers typically excel at optimizing only one of these objectives, and the manual parameter tuning of such controllers proves to be a challenging task. In this paper, we introduce a novel approach, the twin delayed deep deterministic policy gradient-based model predictive control (TD3-MPC) method. To tackle the multiobjective formation-keeping challenge, a linear model predictive controller based on the satellite's dynamics had been developed. Subsequently, a cost function is formulated to facilitate the optimization of multiple objectives, specifically tracking error and fuel consumption. In addressing the intricate issue of controller parameter tuning, we employ reinforcement learning and design a reward function reflective of the TD3 algorithm's controller performance. Simulation results underscore the superior performance of the proposed TD3-MPC algorithm compared to the linear model predictive controller, achieving a notable 27.83% reduction in tracking error and a substantial 48.30% decrease in fuel consumption under large error condition and 3.67% reduction in tracking error and a substantial 22.27% decrease in fuel consumption under small error condition. By effectively combining the strengths of reinforcement learning and model predictive control, TD3-MPC enhances the satellite's ability to adhere more precisely to its intended trajectory, thereby ensuring the stability and desired operational performance of the satellite formation. [ABSTRACT FROM AUTHOR]

Autonomous and Remotely Operated Vehicle (ARV) are widely used for underwater detection and typically require ARVs to track predefined trajectories. Manual operation is not very accurate and stable, and intelligent control methods are popularly devised. This paper firstly introduces the line of sight (LOS) method for desired heading guidance. Later, a cascaded model predictive control (MPC) is proposed. In the cascaded MPC, a quantum-behaved particle swarm optimisation algorithm based on a non-linear decreasing contraction-expansion coefficient (QPSO-CE-nonlinear) is applied to optimise the controller's output. Several validation and comparative simulations are carried out. The results show that the controller has advantages in accuracy, convergence, and stability. The LOS is applicable to holonomic ARV and accelerates convergence. The QPSO-CE-nonlinear algorithm can improve the rate of convergence when solving optimisation problems. In addition, compared with MPC-SMC (sliding mode control), the cascaded MPC is more robust. [ABSTRACT FROM AUTHOR]

In networked control systems, the communication load is a main concern for implementing model predictive control (MPC). This paper introduces a self-triggered MPC algorithm under network environments to reduce communication load, for discrete-time linear systems with bounded state and output disturbances, when only the system output can be measured at triggering instants. The proposed algorithm mainly based on a state estimator whose estimation error is bounded by an invariant set, and on the self-triggered model predictive control of a nominal system. Moreover, the new cost function explicitly adopts communication load is in consideration. The proposed algorithm is proved to drive the system to an invariant set with respect to the bounded disturbances. Finally, simulation results is provided to verify the effectiveness of the proposed robust output feedback MPC algorithm. [ABSTRACT FROM AUTHOR]

This paper studies a safe model predictive control (MPC)-based disturbance rejection control for a broad range of uncertain nonlinear systems subject to complex state safety constraints. The system under study is composed of a nominal model and an uncertain term that encapsulates modeling uncertainty, control mismatch, and external disturbances. In order to estimate the system state and total uncertainty, an extended state observer (ESO) is first designed. Utilizing the output of the ESO, the control compensates for the total uncertainty in real time and concurrently implements a control barrier function (CBF)-based MPC for the compensated system. The proposed control framework guarantees both safety and disturbance rejection. Compared to the baseline algorithm CBF-MPC, the proposed method significantly enhances system stability with a smaller root mean square (RMS) error of the system state from the equilibrium point. Rigorous theoretical analysis and simulation experiments are provided to validate the effectiveness of the proposed scheme. • A safe control scheme for uncertain nonlinear systems subject to complex state safety constraints. • Extended state observer (ESO) for estimating the system state and disturbance. • Control barrier function (CBF)-based model predictive control (MPC) for guaranteeing the safety constraints. • Sufficient conditions are established for maintaining stability, feasibility and safety of the closed-loop system. • The scheme manifested high performance in the application of adaptive cruise control. [ABSTRACT FROM AUTHOR]

Microgrid (MG) control is crucial for efficient, reliable, and sustainable energy management in distributed energy systems. Genetic Algorithm-based energy management systems (GA-EMS) can optimally control MGs by solving complex, non-linear, and non-convex problems but may struggle with real-time application due to their computational demands. Model Predictive Control (MPC)-based EMS, which predicts future behaviour to ensure optimal performance, usually depends on linear models. This paper introduces a novel Genetic Predictive Control (GPC) method that combines a GA and MPC to enhance resource allocation, balance multiple objectives, and adapt dynamically to changing conditions. Integrating GAs with MPC improves the handling of non-linearities and non-convexity, resulting in more accurate and effective control. Comparative analysis reveals that GPC significantly reduces excess power production, improves resource allocation, and balances cost, emissions, and power efficiency. For example, in the Mutation–Random Selection scenario, GPC reduced excess power to 76.0 W compared to 87.0 W with GA; in the Crossover-Elitism scenario, GPC achieved a lower daily cost of USD 113.94 versus the GA's USD 127.80 and reduced carbon emissions to 52.83 kg CO2e compared to the GA's 69.71 kg CO2e. While MPC optimises a weighted sum of objectives, setting appropriate weights can be difficult and may lead to non-convex problems. GAs offer multi-objective optimisation, providing Pareto-optimal solutions. GPC maintains optimal performance by forecasting future load demands and adjusting control actions dynamically. Although GPC can sometimes result in higher costs, such as USD 113.94 compared to USD 131.90 in the Crossover–Random Selection scenario, it achieves a better balance among various metrics, proving cost-effective in the long term. By reducing excess power and emissions, GPC promotes economic savings and sustainability. These findings highlight GPC's potential as a versatile, efficient, and environmentally beneficial tool for power generation systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate mechanical system models are crucial for safe and stable control. Unlike linear systems, Lagrangian systems are highly nonlinear and difficult to optimize because of their unknown system model. Recent research thus used deep neural networks to generate linear models of original systems by mapping nonlinear dynamic systems into a linear space with a Koopman observable function encoder. The controller then relies on the Koopman linear model. However, without physical information constraints, ensuring control consistency between the original nonlinear system and the Koopman system is tough, as the learning process of the Koopman observation function is unsupervised. This paper thus proposes a two-stage learning algorithm that uses structural subnetworks to build a physics-informed network topology to simultaneously learn the Koopman observable functions and the system energy representation. In the Koopman matrix learning session, a quadratic-constrained optimization problem is solved to ensure that the Koopman representation satisfies the energy difference matching hard constraint. The proposed energy-preserving deep Lagrangian Koopman (EPDLK) framework effectively represents the dynamics of the Lagrangian system while ensuring control consistency. The effectiveness of EPDLK is compared with those of various Koopman observable function construction methods in multistep prediction and trajectory tracking tasks. EPDLK achieves better control consistency by guaranteeing energy difference matching, which facilitates the application of the control law generated on the Koopman system directly to the original nonlinear Lagrangian system. [ABSTRACT FROM AUTHOR]

This paper presents a novel integrated control architecture for automotive battery management systems (BMSs). The primary focus is on estimating the state of charge (SoC) and the state of health (SoH) of a battery pack made of sixteen parallel-connected modules (PCMs), while actively balancing the system. A key challenge in this architecture lies in the interdependence of the three algorithms, where the output of one influences the others. To address this control problem and obtain a solution suitable for embedded applications, the proposed algorithms rely on an equivalent circuit model. Specifically, the SoCs of each module are computed by a bank of extended Kalman filters (EKFs); with respect to the SoH functionality, the internal resistances of the modules are estimated via a linear filtering approach, while the capacities are computed through a total least squares algorithm. Finally, a model predictive control (MPC) was employed for the active balancing. The proposed controller was calibrated with Samsung INR18650-20R lithium-ion cells data. The control system was validated in a simulation environment through typical automotive dynamic scenarios, in the presence of measurement noise, modeling uncertainties, and battery degradation. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most existing model predictive control (MPC) methods overlook the network resource limitations of autonomous underwater vehicles (AUVs), limiting their applicability in real systems. This article addresses this gap by introducing an adaptive transmission, interval-based, and self-triggered model predictive control for AUVs operating under ocean disturbances. This approach enhances system stability while reducing resource consumption by optimizing MPC update frequencies and communication resource usage. Firstly, the method evaluates the discrepancy between system states at sampling instants and their optimal predictions. This significantly reduces the conservatism in the state-tracking errors caused by ocean disturbances compared to traditional approaches. Secondly, a self-triggering mechanism was employed, limiting information exchange to specified triggering instants to conserve communication resources more effectively. Lastly, by designing a robust terminal region and optimizing parameters, the recursive feasibility of the optimization problem is ensured, thereby maintaining the stability of the closed-loop system. The simulation results illustrate the efficacy of the controller. [ABSTRACT FROM AUTHOR]

This paper explores the application of Model Predictive Control (MPC) to enhance safety and efficiency in autonomous vehicle (AV) navigation through optimized path planning. The evolution of AV technology has progressed rapidly, moving from basic driver-assistance systems (Level 1) to fully autonomous capabilities (Level 5). Central to this advancement are two key functionalities: Lane-Change Maneuvers (LCM) and Adaptive Cruise Control (ACC). In this study, a detailed simulation environment is created to replicate the road network between Nantun and Wuri on National Freeway No. 1 in Taiwan. The MPC controller is deployed to optimize vehicle trajectories, ensuring safe and efficient navigation. Simulated onboard sensors, including vehicle cameras and millimeter-wave radar, are used to detect and respond to dynamic changes in the surrounding environment, enabling real-time decision-making for LCM and ACC. The simulation results highlight the superiority of the MPC-based approach in maintaining safe distances, executing controlled lane changes, and optimizing fuel efficiency. Specifically, the MPC controller effectively manages collision avoidance, reduces travel time, and contributes to smoother traffic flow compared to traditional path planning methods. These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios. Future research will focus on validating these results through real-world testing, addressing computational challenges for real-time implementation, and exploring the adaptability of MPC under various environmental conditions. This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation, paving the way for broader adoption of MPC in AV systems. [ABSTRACT FROM AUTHOR]

Precise control of furnace temperature (FT) is crucial for the stable, efficient operation and pollution control of the municipal solid waste incineration (MSWI) process. To address the inherent nonlinearity and uncertainty of the incineration process, a FT control strategy is proposed. Firstly, by analyzing the process characteristics of the MSWI process in terms of FT control, the secondary air flow is selected as the manipulated variable to control the FT. Secondly, an FT prediction model based on the Interval Type-2 Fuzzy Broad Learning System (IT2FBLS) is developed, incorporating online parameter learning and structural learning algorithms to enhance prediction accuracy. Next, particle swarm rolling optimization (PSRO) is used to solve the optimal control law sequence to ensure optimization efficiency. Finally, the stability of the proposed method is validated using Lyapunov theory, confirming the controller's reliability in practical applications. Experiments based on actual operational data confirm the method's effectiveness. [ABSTRACT FROM AUTHOR]

The hybrid photovoltaic (PV) with energy storage system (ESS) has become a highly preferred solution to replace traditional fossil-fuel sources, support weak grids, and mitigate the effects of fluctuated PV power. The control of hybrid PV-power systems as generation-storage and their injected active/reactive power for the grid side present critical challenges in optimizing their performance. Therefore, this paper introduces hybrid PV-battery parallel inverters employing a finite control set model predictive control (FCSMPC) method. The proposed FCSMPC-based controller and inverter system achieves multiple functionalities, including maximum power extraction from PV, proper charging/discharging commands for ESS, support for weak grid conditions, support during low-voltage ride-through (LVRT) by increasing reactive power injection to counteract the drop in grid voltage, and economic management based on feed-in-tariff (FiT). The controller significantly improves the performance of the PV-battery system under faulty LVRT conditions and unbalanced grid voltages, satisfying grid code requirements while continuously supplying the microgrid's delicate local load. A real-time simulation hardware-in-the-loop (HiL) setup, utilizing the OPAL-RT platform, is employed to implement the proposed hybrid PV–ESS with its controller. The results affirm the superior ability of FCSMPC in weak-grid conditions and its capability to achieve multiple objectives simultaneously. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract This research introduces an advanced finite control set model predictive current control (FCS-MPCC) specifically tailored for three-phase grid-connected inverters, with a primary focus on the suppression of common mode voltage (CMV). CMV is known for causing a range of issues, including leakage currents, electromagnetic interference (EMI), and accelerated system degradation. The proposed control strategy employs a system model that predicts the inverter’s future states, enabling the selection of optimal switching states from a finite set to achieve dual objectives: precise current control and effective CMV reduction, a meticulously designed cost function evaluates the potential switching states, balancing the accuracy of current tracking against the necessity to minimize CMV. The approach is grounded in a comprehensive mathematical model that captures the dynamics of CMV within the system, and it utilizes an optimization process that functions in real-time to determine the most suitable control action at each interval, Experimental validations of the proposed FCS-MPCC scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters, Experimental validations of the proposed (MPC with CMV) scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters. The proposed method achieved substantial reductions in CMV, notable improvements in current tracking accuracy, and extended system lifespan compared to conventional control methods.

This research introduces an advanced finite control set model predictive current control (FCS-MPCC) specifically tailored for three-phase grid-connected inverters, with a primary focus on the suppression of common mode voltage (CMV). CMV is known for causing a range of issues, including leakage currents, electromagnetic interference (EMI), and accelerated system degradation. The proposed control strategy employs a system model that predicts the inverter’s future states, enabling the selection of optimal switching states from a finite set to achieve dual objectives: precise current control and effective CMV reduction, a meticulously designed cost function evaluates the potential switching states, balancing the accuracy of current tracking against the necessity to minimize CMV. The approach is grounded in a comprehensive mathematical model that captures the dynamics of CMV within the system, and it utilizes an optimization process that functions in real-time to determine the most suitable control action at each interval, Experimental validations of the proposed FCS-MPCC scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters, Experimental validations of the proposed (MPC with CMV) scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters. The proposed method achieved substantial reductions in CMV, notable improvements in current tracking accuracy, and extended system lifespan compared to conventional control methods. [ABSTRACT FROM AUTHOR]

Effective blood glucose regulation is critical for managing type 1 diabetes mellitus (T1DM). This narrative review explores various control strategies for blood glucose regulation, incorporating simulations of the Bergman minimal model. We provide an in-depth review of both single and dual hormone systems, evaluating their respective advantages and limitations. The review focuses on the most widely adopted controllers, including proportional-integral-derivative (PID), model predictive control (MPC), and fuzzy logic controllers, highlighting their design principles and performances. Additionally, we discuss coordinated control strategies that integrate multiple controllers to enhance overall glucose regulation. Through this comprehensive analysis, we aim to identify the most effective control strategies for artificial pancreas systems, contributing to improved diabetes management. [ABSTRACT FROM AUTHOR]

In this article, a model predictive control (MPC) with common-mode voltage (CMV) suppression is proposed for single-phase cascaded H-bridge (CHB) inverters, which can also simultaneously achieve control objectives of gridconnected current tracking, voltages balancing of different H-bridge submodules on the DC-side and switching frequency reduction. To suppress high-frequency components of the common-mode voltage without additional switching devices, the algorithm proposed designs the predicted and reference values of the CMV and incorporates them in the cost function. At the same time, the capacitor voltages balancing control is integrated in the calculation of the optimal modulation function of the H-bridge, which reduces the complexity of control effectively. Besides, switching times of the MOSFETs are compared in two cycles. The cost function is constructed to represent comprehensive effect of the control. Finally, an experiment is performed on the hardware-in-the-loop experimental platform. The experimental results show that the proposed algorithm can offer a better voltage THD and reduce the times of switch action by nearly half while maintaining high-precision current tracking and maximum power point of photovoltaic modules, which alleviate the potential electromagnetic interference and cabling problem. [ABSTRACT FROM AUTHOR]

The wake effect reduces the wind speed at downstream wind turbines (WTs), making it necessary for the central controller to collect wind power generation data from each WT. However, wind farms (WFs) face a more complex problem in maintaining the voltage stability at the WT terminal while following the transmission system operator (TSO) instructions due to the information collection as well as the possible data loss during transmission. Therefore, this study proposes a coordinated control method for WTs and multiple power sources based on model predictive control under wake disturbance conditions, aiming to reduce the average voltage deviation in WT terminals and go close to the rated voltage and ensure effective compliance with TSO commands in large-scale WFs. Accordingly, the Jensen wake model was utilized to accurately calculate the available active and reactive power limits for each WT. Energy storage systems and static Var generators were modeled to coordinate and maintain the voltage in all WT terminals within the feasible range, providing peak shaving and valley filling support to reduce wind energy waste and shortfall, thereby enhancing the economic and operational reliability of WF. Further, the effectiveness of the proposed method was validated in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

The traditional ip-iq harmonic detection method is widely used in APF, but it sacrifices the response speed and detection accuracy of the APF system due to the limitations of the traditional phase-locked loop and LPF. Therefore, in order to make APF both higher detection accuracy and faster response speed, a harmonic detection method based on dual second-order sliding-window discrete Fourier transform (DS-SDFT) is proposed, which can accurately detect harmonics with DSOGI-PLL and greatly reduce the computational effort by using half-cycle sliding-window iterative discrete Fourier transform, solving the inherent defects of traditional phase-locked loops and LPFs. Meanwhile, in order to compensate the harmonics better, the model predictive control with stronger dynamic response capability is used in the current compensation control. Finally, the APF based on this harmonic detection method and current control strategy is fast in response and high in detection accuracy, which is proved by the simulation and experimental results, and the effectiveness of the detection method is verified. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a new Electric Vehicle (EV) system that combines a fuel cell, electrolyzer, onboard PV cell, and a quadratic bidirectional buck-boost converter. The system is managed by Model Predictive Control (MPC) with support from an Artificial Neural Network (ANN). The PV cell enhances the fuel cell's power output during operation and, during idle periods, powers the electrolyzer to produce hydrogen for storage. This stored hydrogen is used to fuel the vehicle, improving its energy efficiency and reducing reliance on traditional fuel sources. A quadratic bidirectional buck-boost converter is used to control voltage, improving power management between the motor and energy sources. This ensures efficient execution even when fluctuating irradiance and vehicle speeds vary. An enhanced Maximum Power Point Tracking (MPPT) algorithm, utilizing an incremental conductance method, is used to maximize energy extraction from the PV system. Meanwhile, the converter's dualloop control, featuring outer voltage and inner current control, ensures a stable DC output for motor operation. The vehicle drive system directs an indirect vector-controlled induction motor, which is set by the ANN-based MPC. This system employs a novel predictive torque control strategy that eliminates the need for weighting factors, simplifying the control process while improving motor speed regulation. Additionally, an ANN s used to accurately predict motor speed based on real-time stator current data, ensuring optimal performance and system stability. Simulations conducted in MATLAB/SIMULINK evaluate the system's performance under varying solar irradiance and speed conditions. The results determine the feasible of the proposed EV configuration to enhance energy efficiency, improve power quality, and provide a more sustainable and economically viable solution for electric vehicles by integrating renewable energy sources with advanced control techniques. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative Hybrid Electric Vehicle (HEV) configuration that employs a fuel cell as the basic energy source and an onboard Photovoltaic (PV) array as the supplementary source. The system is controlled using an advanced Model Predictive Control (MPC) system enhanced by a Recurrent Neural Network (RNN), specifically future to manage the induction motor of the EV. The HEV system includes three major components: a PV array, fuel cell and an electrolyzer, each with a specific role in powering the vehicle. The fuel cell can be assisted by PV array by supplying extra power when sunlight conditions are optimal. When the vehicle is idle, any excess power generated by the PV array is switched into hydrogen by the electrolyzer and kept in an aboard hydrogen tank for later use. To meet the voltage requirements of both the energy sources and the motor, a quadratic bidirectional buck-boost converter (QBBC) is used. It effectively manages the voltage output and ensures compatibility across different energy sources. The system's performance and efficacy are assessed under various sunlight levels and speed conditions. The RNN-based MPC system is benchmarked against traditional control methods, with an Artificial Neural Network-based MPC (ANN-MPC) and a Proportional-Integral (PI) controller. An incremental conductance algorithm for is adopted as Maximum Power Point Tracking (MPPT) algorithm, which adjusts power extraction from the PV array. The traction system of HEV includes an induction motor with indirect vector control, with the RNN-based MPC system providing precise speed control and enhanced efficiency. The RNN model predicts motor speed, contributing to the finer execution of the control system. Comparative analysis using MATLAB/SIMULINK demonstrates the efficacy of the RNN-based MPC system, showing its advantages over ANN-MPC and PI controllers in real-world conditions. This paper significantly advances the expansion of intelligent and efficient electric vehicle systems. [ABSTRACT FROM AUTHOR]

An improved model predictive control (MPC) with Luenberger state observer is proposed in this article for LC‐coupling hybrid active power filter (LC‐HAPF). Traditionally, the implementation of MPC for LC‐HAPF requires additional sensors to measure coupling capacitor voltage, thereby increases the system cost and reduces reliability. The existence of one‐step sampling delay in practice will also affect the final compensation performance. To relax these problems, both continuous and discrete models are deduced first. Then, the Luenberger observer is utilized based on the discrete model to mitigate the steady‐state error without additional sensors. In addition, the sampling delay compensation is provided by improving the prediction horizon. Finally, the effectiveness of the improved MPC for LC‐HAPF is verified by experiment results, indicating that the proposed method has fast dynamic response and low steady‐state error. [ABSTRACT FROM AUTHOR]

Background and Objectives: Model predictive control (MPC) is a practical and attractive control methodology for the control of power electronic converters and electrical motor drives. MPC has a simple structure and enables the simultaneous consideration of different objectives and constraints. However, when applying MPC for multilevel inverters (MLIs), especially at higher voltage levels, the number of switching states dramatically increases. This issue becomes more severe when MLIs are used to supply electrical motor drives. Methods: This paper proposes three different MPC strategies that reduce the number of iterations and computation burden in a 3-phase 4-level flying capacitor inverter (FCI). Traditional MPC with a reduced number of switching conditions, split MPC, and hybrid MPC-PWM control are investigated in this work. Results: In all methods, the capacitor voltages of the FCI are balanced during different operational conditions. The number of iterations is reduced from 512 in traditional MPC to at least 192 in the split MPC. Moreover, the split MPC strategy eliminates the usage and optimization of weighting factors for capacitors voltage balance. However, in the hybrid MPC-PWM control method in comparison to other methods, the voltage balancing time is much lower, the phase current tracks the reference more accurately, the transient time is lower, and the efficiency is higher. In addition, the capacitors voltage ripple is negligible in the hybrid MPCPWM control method. Conclusion: Simulation results manifest the effectiveness of the suggested hybrid MPC-PWM methodology. Results manifest that the hybrid MPC-PWM control offers perfect dynamic characteristics and succeeds in maintaining the voltage balance during different operational conditions. [ABSTRACT FROM AUTHOR]

In this paper, we discuss a nonlinear disturbance observer-based double closed-loop control system for managing a wheeled mobile robot (WMR) and achieving trajectory tracking control. The focus of our study is on the topic of trajectory tracking control for WMR in the presence of external disturbances. Our proposed control strategy addresses the two main issues of velocity constraint and external disturbances. Specifically, we employ a kinematic tracking error model to produce the constrained virtual velocity in the outer loop of a neural-dynamic optimization-based model predictive control (MPC). Additionally, we create a nonlinear disturbance observer (NDO) based on the dynamic model to monitor external disturbances. To achieve accurate trajectory tracking for disturbances compensation, we utilize a robust controller. We confirm the stability of our proposed controller using the Lyapunov theory. Finally, two numerical simulation experiments demonstrate the effectiveness and reliability of our proposed controller. [ABSTRACT FROM AUTHOR]

Recent advances in high-power applications employing voltage source converters have been primarily fuelled by the emergence of the modular multilevel converter (MMC) and its derivatives. Model predictive control (MPC) has emerged as an effective way of controlling these converters because of its high response. However, the practical implementation of MPC encounters hurdles, particularly in MMCs featuring many sub-modules per arm. This research introduces an approach termed folding model predictive control (FMPC), coupled with a pre-processing sorting algorithm, tailored for modular multilevel converters. The objective is to alleviate a significant part of the computational burden associated with the control of these converters. The FMPC framework combines multiple control objectives, encompassing AC current, DC current, circulating current, arm energy, and leg energy, within a unified cost function. Both simulation studies and real-time hardware-in-the-loop (HIL) testing are conducted to verify the efficacy of the proposed FMPC. The findings underscore the FMPC's ability to deliver fast response and robust performance under both steady-state and dynamic operating conditions. Moreover, the FMPC adeptly mitigates circulating currents, reduces total harmonic distortion (THD%), and upholds capacitor voltage stability within acceptable thresholds, even in the presence of harmonic distortions in the AC grid. The practical applicability of MMCs, notwithstanding the presence of a large number of sub-modules (SMs) per arm, is facilitated by the significant reduction in switching states and computational overhead achieved through the FMPC approach. [ABSTRACT FROM AUTHOR]

In recent years, extreme focus on renewable energy has intensified due to environmental concerns and the depletion of fossil fuel supplies. In a DC microgrid that includes AC grid, photovoltaic (PV), wind, and battery storage systems, there are some problems such as intermittency and variability, mismatched generation and demand, inefficient energy utilization by storage batteries, and unstable DC link voltage. To overcome these problems, an integrated strategy for better energy management and a new MPPT technique is proposed in this research. The robust model predictive control (RMPC) method is proposed to deal with uncertainties and disturbances while offering the best possible control options. A comparison of the two algorithms reveals that the RMPC performs better than the conventional model predictive control (MPC) method. To harvest the maximum solar electricity from the PV system, a sophisticated MPPT optimization technique called muddled tuna swarm optimization (MTSO) is applied. Drone squadron optimization (DSO) and slime mould optimization (SMO) are outperformed by MTSO in terms of dynamic performance, effectively tracking the maximum power point (MPP) of the PV system even under various irradiance conditions. The suggested RMPC approach and MTSO technique are effective in achieving optimal energy and battery management as well as maximum solar power extraction, according to the simulation findings. On the OPAL-RT platform, real-time simulation is used to test the control strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study explores positioning and obstacle avoidance control for autonomous surface vehicles (ASVs) by considering equivalent elliptical-shaped obstacles. Firstly, compared to most Barrier Lyapunov function (BLF) methods that approximate obstacles as circles, a novel BLF is improved by introducing an elliptical obstacle model. This improvement uses ellipses instead of traditional circles to equivalent obstacles, effectively resolving the issue of excessive conservatism caused by over-expanded areas during the obstacle equivalence process. Secondly, unlike traditional obstacle avoidance approaches based on BLF, to achieve constraint control of angle and angular velocity, a method based on model predictive control (MPC) is introduced to optimize local angle planning. By incorporating angular error constraints, this ensures that the directional error of the ASV remains within a restricted range. Furthermore, an auxiliary function of directional error is introduced into the ASV's linear velocity, ensuring that the ASV parks and adjusts its direction when the deviation in angle becomes too large. This innovation guarantees the linearization of the ASV system, addressing the complexity of traditional MPC methods when dealing with nonlinear second-order ASV systems. Ultimately, the efficacy of our proposed approach is validated through rigorous experimental simulations conducted on the MATLAB platform. [ABSTRACT FROM AUTHOR]

The traditional controllers for performing desired periodic motions are often computationally intensive and can hardly satisfy the constraints of the control system. This article proposes a bionic controller for such motion patterns inspired by the control mechanism of central pattern generators (CPGs) found in vertebrates. The bionic controller combines the advantages of model predictive control (MPC) and CPGs to track arbitrary periodic trajectories, with smooth transitions between different gaits under constraints. The controller consists of three typical components: the CPG component as the inner loop to generate steady-state periodic motions, the MPC-CPG component as the outer loop for gait transitions, and the decision component to determine which loop should be utilized. The stability of the controller is demonstrated through Lyapunov's method. The controller is then used to track a sinusoidal trajectory under various constraints, and its performance is compared to that of the separate CPG and MPC controllers. The results demonstrate the effectiveness of the proposed controller in tracking periodic motions with constraints, providing a promising approach for developing more efficient and robust controllers for periodic motions. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy and stability of unmanned vehicle trajectory tracking, a vehicle motion control method based on weighted variable time domain model predictive control (MPC) was proposed. The grey relational analysis method was used to determine the optimal time domain under different speed conditions, and the Fourier approximation method was used to fit the time domain parameters. Taking the controller calculation time as the weight coefficient, a weighted variable time domain MPC semi-empirical model with time domain parameters varying with vehicle speeds was obtained. The model can select the relative optimal time domain according to the change of vehicle trajectory tracking speed, and improve the tracking accuracy and stability of the controller under different vehicle speeds. Selecting 50 and 100 km/h as representative vehicle speeds, the control effects of the traditional time-domain MPC controller and the weighted time-domain MPC controller were compared by simulation experiments. The results show that the weighted variable time domain controller can effectively improve the tracking performance of the controller, the maximum lateral deviation is reduced by 1.98%, the maximum yaw angle deviation is reduced by 60%, and the controller solution time is reduced by 7.2%. And it also has strong adaptability to different target speed conditions. [ABSTRACT FROM AUTHOR]

As per literature, there is a potential for energy savings of (5% to 20%) using building embedded control systems. This letter aims to control a house air-conditioning system using model predictive control (MPC) with a neural state space prediction (NSSP) Model to maintain interior temperature set-point and reduce energy consumption. Here, we present a high-fidelity model that is validated by an experimental prototype of a house air-conditioning system that is controlled using a nonlinear MPC. The house air-conditioning system models the air-conditioner and the thermal dynamics of the house. The outdoor temperature is modeled by simulated signals and real measurements. The controller problem is to maintain a house temperature within 20 °C to 22 °C and to minimize energy costs. Compared with the generic nonlinear MPC controller, multistage nonlinear MPC provides a more flexible and efficient way to implement MPC with staged costs and constraints. [ABSTRACT FROM AUTHOR]

In this paper is studied the model predictive control (MPC) of an induction machine (IM) drive for wakening applications. The control structure is implemented in Matlab software. The predictive control law is based on finite set combinations imposed by the two-level inverter topology. The study developed for field-wakening regime shows that the speed tracking objective remains stable despite stator resistance variation. Also, the load torque disturbance rejection is efficiently done to evaluate un unrated parameter. Thus, the operation of the IM drive with MPC law is robust on the sensitivity of parameters of the model.

Hydrogen plays a crucial role in improving sustainability and offering a clean and efficient energy carrier that significantly reduces greenhouse gas emissions. However, the primary method of industrial hydrogen production, steam methane reforming (SMR), relies on the combustion of hydrocarbons as the heating source for the reforming reactions, resulting in significant carbon emissions. To address this issue, an experimental setup of an electrically-heated steam methane reformer (e-SMR) has been constructed at UCLA, and a lumped first-principle dynamic process model was built based on parameters estimated from the experimental data in a previous study. Subsequently, the first-principle dynamic process model was implemented into the computational model predictive control (MPC) scheme, successfully driving the hydrogen production rate to the desired setpoint. While these works are important and pave the way for developing MPC for large-scale e-SMR processes, the first-principle process model may not accurately reflect the actual process behavior, particularly as the process behavior changes with time. Therefore, the development and establishment of an adaptive data-driven approach for implementing model predictive control in the e-SMR process is necessary. To address this need, the present work investigates the construction of recurrent neural network (RNN) models for an e-SMR process in-depth, utilizing data from an experimentally-validated first-principle model. Specifically, a long short-term memory (LSTM) layer was utilized in the RNN model to effectively capture the complex correlations present in long-term sequential data. Subsequently, this LSTM-based RNN process model was employed to design an MPC, and its performance was evaluated through comparison with proportional–integral (PI) control. To address potential disturbances and variability in a typical e-SMR process, three distinct approaches were developed: MPC with an integrator, MPC with real-time online retraining (transfer learning), and offset-free MPC. These approaches effectively eliminated the offset caused by disturbances. Overall, this study underscores the effectiveness of utilizing RNN models to capture process dynamics in an experimental e-SMR process. It also outlines strategies for employing RNN-based control and multiple approaches to address disturbances in general processes with partially infrequent and delayed measurement feedback. This approach is particularly valuable in scenarios where developing first-principle models for a new process may be challenging.

For a class of constrained uncertain nonlinear time-delay systems, how to achieve their stability and robustness is a crucial issue. To this end, this paper proposes a tube-based integral sliding mode predictive control scheme (ISMPC). The model predictive control (MPC) is designed as a nominal controller to generate an optimal ‘reference trajectory’ for the actual (controlled) system. The integral sliding mode control (ISMC) acts as an auxiliary controller to combat disturbances and ensure that the actual trajectory is restricted in a robust tube centred on the reference trajectory. In addition, during the design process, the practical constraints are satisfied through the sliding mode boundary layer, without the need for iterative computation of robust invariant sets. The proposed control scheme combines the advantages of model predictive control MPC and ISMC, ensuring the optimal performance and robustness of the controlled system, and effectively reducing the computational burden required for the control scheme. Finally, the stability of the closed-loop system is proved theoretically, and the effectiveness of the proposed method is verified by the simulation results of two examples.

Renewable energy systems, exemplified by solar and wind power, are increasingly integrated into modern power grids to mitigate environmental impact and reduce reliance on fossil fuels. However, the inherent intermittency and unpredictability of renewable energy sources pose challenges to grid stability and reliability. Energy Storage Systems (ESS) offer a promising solution to address these challenges by smoothing out power fluctuations and ensuring a consistent power supply. Among various ESS technologies, Flywheel Energy Storage Systems (FESS) have emerged as a noteworthy contender due to their rapid response times, low operating costs, and extended lifespan. This paper focuses on investigating the operation of a novel unit comprising a solar power system integrated with a Flywheel Energy Storage System (PV-FESS). The aim is to develop an effective control algorithm utilizing adaptive fuzzy neural networks and model predictive control (ANFIS-MPC) to manage power fluctuations stemming from renewable energy sources within the grid. The proposed control strategy aims to optimize the operation of the PV-FESS system by dynamically adjusting the energy absorption or release of the flywheel to maintain grid stability. Simulation studies conducted using Matlab-Simulink/Simcape software validate the efficacy of the ANFIS-MPC algorithm in mitigating abnormal fluctuations from renewable energy sources. The results demonstrate that the PV-FESS system effectively balances power fluctuations, ensuring a stable and reliable power output to the grid. [ABSTRACT FROM AUTHOR]

Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%. [ABSTRACT FROM AUTHOR]

This paper presents the design of a model predictive control (MPC) for the Calais canal, located in the north of France for satisfactory management of the system. To estimate the unknown inputs/outputs arising from the uncontrolled pumps, a digital twin (DT) in the framework of a Matlab- SIC2 is used to reproduce the dynamics of the canal, and the real database corresponding to a period of three days is employed to evaluate the control strategy. The canal is characterized by two operating modes due to high and low tides. As a consequence of this, time-varying constraints on the use of gates must be considered, which leads to the design of two multiobjective control problems, one for the high tide and another for the low tide. Furthermore, a moving horizon estimation (MHE) strategy is used to provide the MPC with unmeasured states. The simulation results show that the different objectives are met satisfactorily. [ABSTRACT FROM AUTHOR]

In predictive control of induction motors, control signals are adjusted by predicting the future behavior of the motor. These predictions are made on important parameters such as motor speed, current, and torque, and are supported by real-time data. In general, in model predictive control (MPC) method, the input and output of the system are optimized using cost functions based on reference values. Although the induction motor operates in continuous time, discretization methods are needed for performing the necessary current switching and feedback control with microprocessors. For reference speed tracking in this study, the stator current of a three-phase induction motor with specifications of 60 Hz, 50 HP, and 460 V was decomposed for the first time using Crank–Nicholson, Verlet integration, and Runge– Kutta Ralston methods, which are finite control set (FCS)-MPC discretization methods. In this paper, we compared the Forward Euler, Runge–Kutta 4, Runge–Kutta Ralston, Taylor series, Crank–Nicolson, and Verlet integration techniques, which are FCS-MPC methods, with the conventional discrete-time indirect field oriented control (IFOC) method for speed control of induction motors. Based on the simulation data obtained from the induction motor, overshoot, settling time, reference speed root mean square value, and total harmonic distortion values were taken into account. The Verlet integration method had the least settling time than other methods, in the range of 0–4 s, including nominal speed transitions. When the response signals were examined, it was seen that the Verlet integration method gave the lowest settling time and overshoot percentage values for the 4–8 s, while the Forward Euler method gave the lowest settling time and overshoot percentage values for the 8–10 s. [ABSTRACT FROM AUTHOR]

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