Search

In the transition from fossil fuel to electrified heating, a concerning trend is emerging in certain regions of the US. Owners of buildings with gas-based systems leave them in place after adding heat pumps (HPs). Existing control solutions for these hybrid (dual fuel) systems are rudimentary and fall short of realizing the full carbon reduction potential of these systems. Model predictive control (MPC) is often regarded as the benchmark for achieving optimal control in integrated systems. However, in the case of small-medium commercial buildings (SMCBs), the control and communication infrastructure required to facilitate the implementation of such advanced controls is often lacking. This paper presents a field implementation of easy-to-deploy MPC for a dual fuel heating system consisting of HPs and a gas-fired furnace (GF) for SMCBs. The control system is deployed on an open-source middleware platform and utilizes low-cost sensor devices to be used for real SMCBs without major retrofits. We demonstrated this MPC in a real office building with 5 HPs and 1 GF for 2 months. The test results showed that MPC reduced 27% of cost while completely eliminating GF usage by shifting 23% of the thermal load from occupied-peak time to non-occupied-non-peak times.

The study of approximate matching in the Massively Parallel Computations (MPC) model has recently seen a burst of breakthroughs. Despite this progress, we still have a limited understanding of maximal matching which is one of the central problems of parallel and distributed computing. All known MPC algorithms for maximalmatching either take polylogarithmic time which is considered inefficient, or require a strictly superlinear space of n1+Ω(1) per machine. In this work, we close this gap by providing a novel analysis of an extremely simple algorithm, which is a variant of an algorithm conjectured to work by Czumaj, Lacki, Madry, Mitrovic, Onak, and Sankowski [15]. The algorithm edge-samples the graph, randomly partitions the vertices, and finds a random greedy maximal matching within each partition.We show that this algorithm drastically reduces the vertex degrees. This, among other results, leads to an O(log log Δ) round algorithm for maximal matching with O(n) space (or even mildly sublinear in n using standard techniques). As an immediate corollary, we get a 2 approximate minimum vertex cover in essentially the same rounds and space, which is the optimal approximation factor under standard assumptions. We also get an improved O(log log Δ) round algorithm for 1 + ε approximate matching. All these results can also be implemented in the congested clique model in the same number of rounds. [ABSTRACT FROM AUTHOR]

The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces an improved model predictive current control (MPCC) method to address these problems. Firstly, 12 virtual voltage vectors are synthesized to improve torque output performance while suppressing harmonic currents. Then, to generate symmetrical switching signals and reduce switching loss, the largest basic vector used to synthesize the virtual vector is replaced by two medium vectors. Secondly, to solve the problem of the increased computational burden caused by the increase in discrete virtual vectors, a two-step vector selection method is proposed. In this method, each part is divided into several parts according to N, and the traditional cost function is also replaced by two-step functions. Different control performances can be achieved according to different values of N. Experimental results show that the proposed control scheme not only achieves stable current quality but also significantly improves steady-state performance throughout the entire speed range. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method for hybrid energy systems based on Model Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov–Arnold Networks (KANs). The proposed method is applied to a high-altitude wind energy work umbrella control system, where it aims to enhance the stability and efficiency of energy utilization. The work umbrella system integrates wind and solar energy sources, with energy stored in a battery and used to control the umbrella's operations. The MPC framework is employed to optimize control actions by solving a finite-horizon optimization problem, ensuring the battery State of Charge (SOC) remains within an optimal range. The LSTM network provides accurate predictions of environmental conditions, including wind speed and solar irradiance, which are essential for MPC's decision-making process. To address complex nonlinearities in the system, the KAN is utilized to model and approximate these dynamics, refining the LSTM predictions. The integration of these advanced control strategies enables the system to handle varying operational conditions and maintain optimal performance. The case study demonstrates the effectiveness of the MPC-LSTM-KAN approach, revealing improvements in the SOC stability, energy efficiency, and operational endurance of the high-altitude wind energy work umbrella system. The results indicate that this hybrid optimization method offers a robust solution for managing hybrid energy systems in dynamic environments. [ABSTRACT FROM AUTHOR]

A modular system of cascaded converters based on model predictive control (MPC) is proposed to meet the application requirements of multiple voltage levels and electrical isolation in renewable energy generation systems. The system consists of a Buck/Boost + CLLLC cascaded converter as a submodule, which is combined in series and parallel on the input and output sides to achieve direct-current (DC) voltage transformation, bidirectional energy flow, and electrical isolation. The CLLLC converter operates in DC transformer mode in the submodule, while the Buck/Boost converter participates in voltage regulation. This article establishes a suitable mathematical model for the proposed system topology, and uses MPC to control the system based on this mathematical model. Module parameters are designed and calculated, and simulation is built in MATLAB/Simulink to complete the simulation comparison experiment between MPC and traditional proportional integral (PI) control. Finally, a physical experimental platform is built to complete the physical comparison experiment. The simulation and physical experimental results prove that the control accuracy and response speed of MPC are better than traditional PI control strategy. [ABSTRACT FROM AUTHOR]

In this study, a data-driven model predictive control (MPC) method is proposed for the optimal control of a doubly-fed variable-speed pumped storage unit. This method combines modern control theory with the dynamic characteristics of the pumped storage unit to establish an accurate dynamic model based on actual operating data. In each control cycle, the MPC uses the system model to predict future system behavior and determines the optimal control input sequence by solving the constrained optimization problem, thereby effectively dealing with the nonlinearity, time-varying characteristics, and multivariable coupling problems of the system. When compared with a traditional PID control, this method significantly improves control accuracy, response speed, and system stability. The simulation results show that the proposed MPC method exhibits better steady-state error, overshoot, adjustment time, and control energy under various operating conditions, demonstrating its advantages in complex multivariable systems. This study provides an innovative solution for the efficient control of doubly-fed variable-speed pumped storage units and lays a solid foundation for the efficient utilization of new energy sources. [ABSTRACT FROM AUTHOR]

Wheel-ground interactions during operation cause the robot to deviate from the reference trajectory, affecting the stability and safety of the robot. An event-triggered model predictive control based on the observation disturbance triggering mechanism is proposed for the four-wheel independent steering and four-wheel independent driving wheeled mobile robot to handle this issue. The discrete error model is established based on the kinematic model, and its prediction model is further developed. During the robot's movement, a discrete-time disturbance observer is used to estimate the bounded disturbances suffered by the robot. Then, a rolling optimization method based on the triggering mechanism of disturbance observation is proposed. By setting the disturbance threshold to determine if the optimal control problem should be solving within the sampling period, the optimization frequency of model prediction controller is reduced, which in turn saves computational resource consumption. Through simulation, it has been demonstrated that the controller design significantly decreases the robot's computational resource consumption while tracking trajectories. [ABSTRACT FROM AUTHOR]

The clean transformation of building energy consumption is a critical pathway for achieving deep decarbonization. In light of the diversity of building energy needs, this paper proposes an integrated zero-carbon energy supply building architecture that involves the coupling of electrical, hydrogen, heat, and cooling energy sources. To address challenges in real-time supply-demand balancing and reliability of supply under extreme or boundary conditions due to complex energy couplings, the paper introduces an energy management framework with two control layers comprising mixed-integer linear programming (MILP) and model predictive control (MPC). This framework conducts hierarchical optimization to synergize economic and technical objectives. At the economic optimization layer, optimal economic parameters are formulated based on MILP; while at the MPC optimization layer, these parameters serve as reference states. By incorporating state relaxation method, the framework accommodates the uncertainties associated with system operation. Simulation analyses of typical weeks during summer and winter seasons demonstrate that the energy optimization management approach proposed in this paper can achieve highly reliable and economically stable operation of the building energy supply system. • A zero-carbon energy supply building energy structure is constructed. • Dynamic evolution model of electricity-hydrogen-heat-cooling multi-energy coupling system is established. • A double-layer energy management method including economic optimization and MPC optimization is proposed. [ABSTRACT FROM AUTHOR]

Magnetic suspension balance systems (MSBSs) need to allow vehicle models to levitate stably in different attitudes, so it is difficult to ensure the stable performance of the system under different levitation gaps using a controller designed with single balance point linearization. In this paper, a levitation controller based on linear model predictive control and a back-propagation neural network (LMPC-BP) is proposed and simulated for single-point magnetic levitation. The deviation of the BP network is observed and compensated by an expansion state observer (ESO). The iterative BP neural network model is further updated using current data and feedback data from the ESO, and then the performance of the LMPC-BP controller is evaluated before and after the update. The simulation results show that the LMPC-BP controller can achieve stable levitation at different gaps of the single-point magnetic levitation system. With further updating and iteration of the BP network, the controller anti-jamming performance is improved. [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.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

In the context of frequency stability in a two-area microgrid, it is crucial to address the fluctuations in frequency caused by load disturbances. To achieve this, an effective load-frequency control (LFC) system, which serves as the secondary control, must be implemented. However, the presence of renewable energy sources such as wind turbines and photovoltaic systems adds complexity to the operation of the LFC system due to their inherent uncertainty. To enhance the performance of the LFC system in the two-area microgrid, this paper proposes a reduction in the number of controllers employed, aiming for a less complex structure. Specifically, Model Predictive Control (MPC) is utilized for LFC, and the weight parameters of the MPC controller are determined using Craziness-based Particle Swarm Optimization (CRPSO). The proposed method is compared with alternative approaches, including PID controller optimized with Social Spider Optimization (SSO), Fractional Order Fuzzy PI (FOFPI), and conventional MPC. The effectiveness of the proposed method is evaluated in various scenarios, considering load variations and the presence of distributed microgrid generation resources. The results demonstrate that the proposed method outperforms the other controllers in terms of speed of response, reduction of overshoot and undershoot, and overall complexity. Importantly, the proposed method significantly improves the frequency stability of the two-area microgrid. The simulation and analysis are conducted using MATLAB software, providing a comprehensive understanding of the system dynamics and the performance of the proposed controller.

In PMSM motor drive systems, control accuracy and resistance have always been the most important challenges for researchers. This paper addresses these two challenges and presents a control structure that makes the system performance robust against model uncertainties of system disturbances as well as sudden load changes. In other words, in this paper, an improved MPC controller has been presented, in which model uncertainties and possible disturbances on the system are considered during design. In addition, in order to apply the physical limitations of the electric motor during the design of this improved controller, the input limitations of the system have also been considered. Therefore, the desired MPC controller is a resistant and constrained MPC, it is resistant to uncertainties and disturbances. In addition, in order to further strengthen the control loop against sudden load changes, a sliding estimator has also been designed to estimate the load torque. This estimator itself is also resistant to the uncertainties of the model, which makes the entire control structure have a very robust performance. The performance of the method presented in this article has been compared with a conventional sliding method during a series of practical tests. The results showed that the proposed method has a higher accuracy of 3 rpm and a shorter convergence time of 2 seconds.

In software-defined networking (SDN), where software assumes a progressively prominent position in the network administration, it becomes imperative to emphasize the enhancement of data authentication and non-repudiation methodologies. The study presents a novel approach that incorporates Multi-Party Computation (MPC) into symmetric systems to enhance data authentication inside SDN settings. The use of MPC ensures the timely authentication of identities, prevention of repudiation, and maintenance of data accuracy, while simultaneously protecting the confidentiality of sensitive data and parameters. The evaluation of our technique's performance is accomplished through rigorous simulations utilising NS-3. The simulations yielded positive results in terms of packet delivery ratio, end-to-end delay, and throughput. The findings of the research illustrate that the proposed approach demonstrates consistent efficacy and reliability, even under conditions of heightened network node density. This study presents a complete methodology aimed at enhancing data authentication in SDN. This paper examines the increasing security challenges associated with cloud-based SDN solutions. This research demonstrates a significant progress in the quest for a secure and efficient SDN environment, setting new standards for data authentication and non-repudiation. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a robust control method is introduced for autonomous vehicle control in different scenarios. Dual controllers have been used in this method to ensure high performance and low errors during the vehicle’s trip. The new control system is called Model Predictive and Stanley based controller (MPS), which is an integration of a model predictive controller and a Stanley controller. Each of these two controllers has its drawbacks and weaknesses. The proposed method tries to overcome these points and come up with a high-performance control system. This hybrid way of combining two of the famous controllers has the benefit of using the best part of each one and trying to enhance the other part. The MPS is tested for both path-following and vehicle control in different scenarios and on both straight and curved roads. This controller has shown high performance and flexibility to deal with different scenarios of autonomous driving. The results are compared to previous types of controllers, and the proposed system outperformed these types.

Introduction: Traditional adaptive cruise control systems ignore the impact of the driver's intentions and driving behavior on system performance. Methods: In response to this issue, this study designs a new adaptive cruise control system by combining personalized driving style recognition, dynamic distance control, prospective energy management, and a model predictive control framework that integrates long short-term memory neural networks and ensemble learning. Results: It was verified that the accuracy of the algorithm was 96.2%. In addition, experts had average ratings of 95, 96, and 98 for the economy, safety, and comfort of the system, respectively. Discussion: This model is expected to achieve comprehensive performance optimization and improvement of EREV in complex driving environments, injecting new vitality and power into the intelligent development of electric vehicles. [ABSTRACT FROM AUTHOR]

In this paper, a robust control method is introduced for autonomous vehicle control in different scenarios. Dual controllers have been used in this method to ensure high performance and low errors during the vehicle’s trip. The new control system is called Model Predictive and Stanley based controller (MPS), which is an integration of a model predictive controller and a Stanley controller. Each of these two controllers has its drawbacks and weaknesses. The proposed method tries to overcome these points and come up with a high-performance control system. This hybrid way of combining two of the famous controllers has the benefit of using the best part of each one and trying to enhance the other part. The MPS is tested for both path-following and vehicle control in different scenarios and on both straight and curved roads. This controller has shown high performance and flexibility to deal with different scenarios of autonomous driving. The results are compared to previous types of controllers, and the proposed system outperformed these types. [ABSTRACT FROM AUTHOR]

Despite the urgent need for new antibiotics, very few innovative antibiotics have recently entered clinics or clinical trials. To provide a constant supply of new drug candidates optimized in terms of their potential to select for resistance in natural settings, in vitro resistance-predicting studies need to be improved and scaled up. In this review, the following in vitro parameters are presented: frequency of spontaneous mutant selection (FSMS), mutant prevention concentration (MPC), dominant mutant prevention concentration (MPC-D), inferior-mutant prevention concentration (MPC-F), and minimal selective concentration (MSC). The utility of various adaptive laboratory evolution (ALE) approaches (serial transfer, continuous culture, and evolution in spatiotemporal microenvironments) for comparing hits in terms of the level and time required for multistep resistance to emerge is discussed. We also consider how the hit-to-lead stage can benefit from high-throughput ALE setups based on robotic workstations, do-it-yourself (DIY) continuous cultivation systems, microbial evolution and growth arena (MEGA) plates, soft agar gradient evolution (SAGE) plates, microfluidic chips, or microdroplet technology. Finally, approaches for evaluating the fitness of in vitro-generated resistant mutants are presented. This review aims to draw attention to newly emerged ideas on how to improve the in vitro forecasting of the potential of compounds to select for resistance in natural settings. [ABSTRACT FROM AUTHOR]

In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid acceleration and deceleration can pose safety hazards. This paper proposes a velocity-adaptive model predictive control (MPC)-based path tracking method for heavy-duty forklift AGVs. The movement of heavy-duty forklift-type AGVs is categorized into straight-line and curve-turning motions, corresponding to the straight and curved sections of the reference path, respectively. These sections are segmented based on their curvature. The best driving speeds for straight and curved sections were 1.5 m/s and 0.3 m/s, respectively, while the optimal acceleration rates were 0.2 m/s2 for acceleration and −0.2 m/s2 for deceleration in straight paths and 0.3 m/s2 for acceleration with −0.15 m/s2 for deceleration in curves. Moreover, preferred sampling times, prediction domain, and control domain were determined through simulations at various speeds. Four path tracking methods, including pure tracking, Linear Quadratic Regulator (LQR), MPC, and the velocity-adaptive MPC, were simulated and evaluated under straight-line, turning, and complex double lane change conditions. Field experiments conducted in a warehouse environment demonstrated the effectiveness of the proposed path tracking method. Findings have implications for advancing path tracking control in narrow aisles. [ABSTRACT FROM AUTHOR]

Visual servoing enables tracking and grasping of static and moving objects and locating regions of interest. Its implementation requires the coordination of low-level control tasks with high-level supervision and planning. The use of the PID-based manipulation control addresses only the dynamical response and requires the use of optimization-based techniques at the supervisory level. On the contrary, model predictive control (MPC) simplifies the design as it combines both tasks within a single algorithm. However, successful MPC implementation depends on the quality of the internal model utilized by the algorithm. Robots are intrinsically and significantly nonlinear control objects, so their models are inherently complex. This leads to computational problems. Standard solutions are to shorten the MPC horizons or to parallelize calculations. The approach suggested here returns to the problem roots, i.e. to the model. This study proposes simplified models that allow the use of the MPC. General Hammerstein–Wiener-like model is applied to an arm, which is further simplified into the linear dynamics used as the predictive supervisory control over the torque control. The head uses linear dynamics used by the supervisory MPC working with the base servomotors. It is shown that they are computationally efficient and sufficiently accurate. It is shown that the linear MPC controllers, which use the suggested simplified dynamic robot models, can successfully support visual servoing with accurate control. They are compared with standard PID-based structures and show their superiority. The proposed approach is successfully validated using Matlab and Gazebo robot simulator and is ultimately confirmed by experiments on a real robot. [ABSTRACT FROM AUTHOR]

Conical tanks have a high application rate in industrial processes, especially in colloidal mills, chemical processes, and food processing. The use of conical tanks presents significant benefits because they contribute to sedimentation and reduce the accumulation of impurities compared to conventional cylindrical tanks. However, level control of a conical tank due to its shape requires advanced strategies to guarantee efficient control. In this research, a model predictive control (MPC) method was designed and implemented for the level control of a conical tank on a laboratory scale. To evaluate the performance of the controller, it was compared with a traditional proportional–integral (PI) controller, and the rise time, settling time, overshoot, and error in the steady state were analyzed when different set point changes were tested. In addition, the system was subjected to disturbances, and the MPC demonstrated better performance in a transient state, as well as smooth and stable action controls that allowed for an increase in the useful life of the actuator. In addition, an interactive graphical interface was developed that allowed a dynamic response in a real plant to be experienced; this provides an academic tool for designing control strategies before implementation in a real process. [ABSTRACT FROM AUTHOR]

This study proposes a fault-tolerant method for controlling multilevel inverters using predictive control strategies to tackle semiconductor valve open circuit problems, making a substantial step towards ensuring smooth functionality and sustained performance. The proactive error detection mechanism, based on analyzing differences between the output voltage and the H-bridge control signals, offers a sophisticated approach to fault management. With an advanced SVM voltage modulation algorithm, the system efficiently handles potential faults by optimizing switching combinations to achieve standard voltage vectors. This method ensures maximum output voltage and maintains balanced operation across three phases, resulting in an optimal operational state. The viability and effectiveness of the proposed solution are conclusively established through a comprehensive analysis and rigorous testing. MATLAB simulations confirmed the integrity of the proposed method, demonstrating its ability to accurately address current, torque, and speed requirements. The findings highlight the competence of multilevel inverters in practice, presenting them as user-friendly, secure, and capable of meeting diverse quality standards. [ABSTRACT FROM AUTHOR]

In this work we introduce PERK a compact digital signature scheme based on the hardness of a new variant of the permuted kernel problem (PKP). PERK achieves the smallest signature sizes for any PKP-based scheme for NIST category I security with 6 kB, while obtaining competitive signing and verification timings. PERK also compares well with the general state-of-the-art. To substantiate those claims we provide an optimized constant-time AVX2 implementation, a detailed performance analysis and different size-performance trade-offs. Technically our scheme is based on a Zero-Knowledge Proof of Knowledge following the MPC-in-the-Head paradigm and employing the Fiat–Shamir transform. We provide comprehensive security proofs, ensuring EUF-CMA security for PERK in the random oracle model. The efficiency of PERK greatly stems from our particular choice of PKP variant which allows for an application of the challenge-space amplification technique due to Bidoux–Gaborit (C2SI 2023). Our second main contribution is an in-depth study of the hardness of the introduced problem variant. First, we establish a link between the hardness of our problem variant and the hardness of standard PKP. Then, we initiate an in-depth study of the concrete complexity to solve our variant. We present a novel algorithm which outperforms previous approaches for certain parameter regimes. However, the proximity of our problem variant to the standard variant can be controlled via a specific parameter. This enables us to effectively safeguard against our new attack and potential future extensions by a choice of parameters that ensures only a slight variation from standard PKP. [ABSTRACT FROM AUTHOR]

Ensuring efficiency and safety is critical when developing charging strategies for lithium-ion batteries. This paper introduces a novel method to optimize fast charging for cylindrical Li-ion NMC 3Ah cells, enhancing both their charging efficiency and thermal safety. Using Model Predictive Control (MPC), this study presents a cost function that estimates the thermal safety boundary of Li-ion batteries, emphasizing the relationship between the temperature gradient and the state of charge (SoC) at different temperatures. The charging control framework combines an equivalent circuit model (ECM) with minimal electro-thermal equations to estimate battery state and temperature. Optimization results indicate that at ambient temperatures, the optimal charging allows the cell's temperature to self-regulate within a safe operating range, requiring only one additional minute to reach 80% SoC compared to a typical fast-charging protocol (high current profile). Validation through numerical simulations and real experimental data from an NMC 3Ah cylindrical cell demonstrates that the simple approach adheres to the battery's electrical and thermal limitations during the charging process. [ABSTRACT FROM AUTHOR]

Introduction: Actinobacillus pleuropneumoniae (APP) is a serious pathogen that affects the development of livestock breeding. Due to excessive use of antimicrobial drugs, many multidrug-resistant bacteria have emerged and spread, which have threatened the livestock industry. Therefore, we established a peristaltic pump infection model (PPIM) to evaluate the susceptibility change and pharmacokinetic/pharmacodynamic (PK/PD) integration of tulathromycin against APP during the mutant selection window (MSW) for preventing the emergence of mutant-resistant bacteria. Methods: The 99% minimum inhibitory concentration (MIC99) and mutant prevention concentration (MPC) of tulathromycin against APP were measured using the agar-plate method. After the model of dynamic infection had been established based on tulathromycin data in lungs, different dosages were administered to make the drug concentrations located in different parts of the MSW. The population and sensitivity of APP were monitored. Tulathromycin concentrations were measured by high-performance liquid chromatographytandem mass spectrometry. Finally, a sigmoid Emax model was used to analyze the relationships between PK/PD parameters and antibacterial effects. Results and discussion: The values of MIC, MIC99, and MPC of tulathromycin against APP were 2, 1.4, and 44.8 µg/mL, respectively. The PPIM was stable. An elimination effect without regrowth was observed at 5.6 to 44.8 µg/mL (-4.48 to -7.05 Log10 CFU/mL, respectively). The MIC of APP increased 32-fold at 8 MIC99. AUC168 h/MIC99 had the best fit with the antibacterial effect (R2 = 0.9867). The AUC168 h/MIC99 required to achieve bacteriostatic, bactericidal, and clearance effects were 1.80, 87.42, and 198 h, respectively. Our results could provide guidance for the clinical application of tulathromycin to treat APP infection and avoid the generation of drug-resistant bacteria. [ABSTRACT FROM AUTHOR]

Cargo e-bikes are expected to convey heavy loads in all aspects of daily life. Also, these vehicles are expected to maintain a consistent speed to meet mobility needs while optimizing the battery design. In this paper, a control model is developed to improve rotational speed motor control via the battery model predictive controller (MPC) thermal model designed based on experimental field test data. Experimental field tests are performed to provide the relation between battery surface and ambient temperatures in different road types and weight conditions. For this purpose, in different slope ranges, the pedal load/activity and voltage-current data are logged to use as experimental input in an MPCintegrated 1D model. To obtain the desired thermal conditions in the Li-Ion battery, the MPC battery thermal model is defined based on the thermal lumped model approach. In the next step, the generated MPC model is used as a function for longitudinal speed control in the MPC motor torque control model subjected to uncertain road disturbances. Then, the outputs of the control models are compared using the MPC parameters oc weight factors and prediction horizon. Thus, the speed control model for cargo e-bikes is presented with increased robustness using the MPC battery thermal lumped model approach considering energy and Li-Ion battery life-cycle efficiency methods regardless of driving performance needs. [ABSTRACT FROM AUTHOR]

This paper studies the consensus problem of Multi-Agent Systems (MASs) with input-delay using the Model Predictive Control (MPC) approach. Due to challenges such as input delay and communication graph, the traditional MPC is not efficient for the considered systems. In this regard, a novel MPC is developed for MASs with input delay. The main advantage of this paper is to design distributed controllers based on minimizing given cost functions which result in the improvement of the transient responses. For this purpose, first, distributed observers are derived to estimate the leader's states, perfectly. Then, distributed controllers are achieved through the MPC approach. Finally, numerical and practical examples are simulated to affirm the efficiency and applicability of the presented scheme. [ABSTRACT FROM AUTHOR]

The rising electricity costs, cost of space heating, and domestic hot water end up driving consumers toward reducing expenses by generating their electricity through devices like photovoltaic systems and efficient combined heat and power plants. When coupled with thermal systems via an energy management system (EMS) in a Multi-Energy System (MES), this self-produced electricity can effectively lower electricity and heating bills. However, MESs with EMSs can serve various purposes beyond cost reduction via self-consumption, such as reacting to variable electricity prices, meeting special grid connection conditions, or minimizing CO2 emissions. These diverse strategies create unique prosumer profiles, deviating significantly from standard load profiles. The potential threat to the power grid arises as grid operators lack visibility into which consumers employ which control strategies. This paper investigates the impact of controlled MESs on the power grid compared to average households and answers whether new control strategies affect the planning strategies of low voltage grids. It proposes a comprehensive four-step toolchain for the detailed simulation of thermal–electrical load profiles, MES control strategies, and grid dynamics. It includes a new method for the grid impact analysis of extreme and average bulk values. As a result, this study identifies three primary factors influencing distribution power grids by MESs. Firstly, the presence and scale of photovoltaic (PV) systems significantly affect extreme values in the grid. Secondly, MESs incorporating combined heat and power (CHP) and heat pump (HP) units impact the overall grid performance, mainly reflected in bulk values. Thirdly, the placement of an MES with heating systems, especially when concentrated in one feeder, plays a crucial role in grid dynamics. Despite the three distinct factors identified as impactful on the power grid, this study reveals that the various control strategies, despite leading to vastly different grid profiles, do not exhibit divergent impacts on buses, lines, or transformers. Remarkably, the impact of MESs remains consistently similar across the range of control strategies studied. Therefore, different control strategies do not pose an additional challenge to the grid integration of MESs. [ABSTRACT FROM AUTHOR]

Ethereum, one of the most prominent and widely deployed blockchain systems, is undergoing a significant upgrade that adopts sharding for capacity expansion and secure multi-party computation (MPC) to enable distributed validator technology (DVT). However, it faces a data availability problem, where an adversary can cheat honest-but-lazy validators to propagate invalid blocks, thereby exposing Ethereum Sharding to vulnerabilities. To address this issue, proof of custody (PoC) was proposed. Regrettably, no practically effective MPC protocol is designed to securely and distributively compute PoC. In this paper, we present a concrete and efficient MPC protocol that enables the secure computation of PoC in the dishonest-majority malicious setting. First, we construct an efficient conversion protocol to convert secret sharings in two different prime fields using the doubly authenticated bits (daBits) technique. Second, an efficient MPC protocol is designed to compute scalar multiplication over an elliptic-curve group without requiring MPC to compute branching programs. Furthermore, we employ affine coordinates to compute group operations over an elliptic curve, leading to significant performance boosts compared to other coordinate systems. Finally, a concrete end-to-end implementation of the protocol is built, and its performance is evaluated. When the operations of a validator are collaboratively performed by seven parties, the online (resp., total) running time to generate one proof of custody is 0.02 s (resp., 559.6 s) in LAN and 0.39 s (resp., 7904.2 s) in WAN. The results demonstrate the practicality of the proposed protocol for Ethereum Sharding over a long period of time, such as 73 days. [ABSTRACT FROM AUTHOR]

In recent years, path-tracking controllers for autonomous passenger vehicles and Control Allocation (CA) methods for handling and stability control have both received extensive discussion in the literature. However, the integration of the path-tracking control with CA methods for autonomous racing vehicles has not attracted much attention. In this study, we design an integrated path-tracking and CA method for a prototype autonomous racing electric vehicle with a particular focus on the maximising the turning speed in tight cornering. The proposed control strategy has a hierarchical structure to improve the computational efficiency: the high-level path-tracking Model Predictive Control (MPC) based on a rigid body model is designed to determine the virtual control forces according to the desired path and desired maximum velocity profile, while the low-level CA method uses a Quadratically Constrained Quadratic Programming (QCQP) formulation to distribute the individual control actuator according to the desired virtual control values. The proposed controller is validated in a high-fidelity simulation vehicle model with the computational time of the optimisation controller presented to demonstrate the real-time control performance. [ABSTRACT FROM AUTHOR]

The magnetic levitation (maglev) ball system is a prototypical Single-Input-Single-Output (SISO) system, characterized by its pronounced nonlinearity, rapid response, and open-loop instability. It serves as the basis for many industrial devices. For describing the dynamics of the maglev ball system precisely in the pseudo linear model, the long short-term memory (LSTM) based auto-regressive model with exogenous input variables (LSTM-ARX) is proposed. Firstly, the LSTM network is modified by incorporating the auto-regressive structure with respect to sequence input, allowing it to deduce a locally linearized model without the need for Taylor expansion. Then, the LSTM-ARX model is transformed into a linear parameter varying (LPV) state space model, and upon this foundation, a model predictive controller (MPC) is proposed. Specifically, when deducing the MPC, the deep learning-based model is linearized by fixing its state input at the current state, so that the nonlinear, non-convex optimization problem can be converted to a finite-horizon quadratic programming problem, thereby deriving the explicit form of MPC. To further enhance the efficiency of the controller in real-time control tasks, a predictive functional controller (PFC) is proposed. It employs multiple nonlinear functions to fit the control sequence, thereby reducing the number of decision variables of the on-line optimization problem in MPC. The proposed controller was successfully applied to the real-time control of the maglev ball system. Simulation and real-time control experiments have validated the improvement in transient performance and efficiency of the LSTM-ARX model-based PFC (LSTM-ARX-PFC). • The LSTM-ARX model is proposed, it describes global nonlinearity dynamics and bears pseudo linear structure. • The explicit form of a deep learning based computational efficient MPC is proposed and its stability is proved. • A predictive functional control method is proposed to furtherly facilitate computational efficiency in real-time control. [ABSTRACT FROM AUTHOR]

This paper presents Multiple Traffic Light Advisor (MTLA), a novel Green Light Optimal Speed Advisory (GLOSA) system that leverages 5G communication technology. GLOSA systems are emerging as a key component in intelligent transportation systems, thanks to the development of effective communication technologies. At its core, MTLA serves as a guidance system for drivers, providing real-time instructions to adjust vehicle speed to optimize the utilization of current and future states of traffic lights along their route.The work addresses several limitations in the current state-of-the-art approaches, including the use of an overly simplified velocity profile, the omission of potential grip and jerk in problem formulation, and the absence of a detailed description of the algorithm’s implementation aspects. Initially, we comprehensively present an optimization-free implementation of the overall control architecture based on an unconventional speed profile. Subsequently, MTLA is improved within a non-linear Model Predictive Control (MPC) framework which uses the latter nonoptimal solution as an initial guess and considers potential grip and jerk in the problem formulation. The developed systems are numerically tested and compared within a high-fidelity simulation environment using the IPG CarMaker simulator. The results demonstrate promising performance in terms of energy savings, with a significant reduction of 37% in energy usage, as well as improved overall comfort with respect to the case where no guidance is given to the driver. These findings suggest a high potential for future developments in this domain. [ABSTRACT FROM AUTHOR]

As part of the study of the ecological state of soils and vegetation in the zone of impact of the Sredneuralsky Copper Smelter (SNCS), the following tasks were solved: the content of heavy metals in the soils of the test sites laid within the zone of impact of the SNCS was determined; macrokinetic patterns of seasonal dynamics of the normalized relative vegetation index (NDVI) at the test sites in the zone of impact of the SNCS were revealed; macrokinetic patterns of vegetation response in the form of NDVI, calculated according to MODIS and Landsat 8 data, to soil pollution by a complex of heavy metals in the zone of impact of the SNCS were revealed; soil quality was ranked according to the patterns of vegetation response in the form of NDVI to soil pollution by a complex of heavy metals within the boundaries of the natural protection zone (NPZ) of the SNCS according to 2023. The intraseasonal dynamics of photosynthetically active biomass in the form of NDVI was modeled using a theoretical growth equation based on MODIS satellite data. The seasonal maximum of the vegetation index at the trial sites in 2012 fell on weeks 25‒28. In 2023, it occurred on weeks 27‒33, depending on the location of the sampling point. The rate of maximum achievement is characterized by weak intraseasonal and interannual variation. The patterns of changes in the concentration of photosynthetically active biomass in the form of maximum NDVI for the 2023 season in response to the gross content of a complex of heavy metals (Cu, Pb, Cd, and Zn) in the soil of the test sites were modeled using the theoretical equation of dose dependence. The maximum permissible level of the heavy-metal complex in the soil in the form of a geometric mean, which does not cause a decrease in the values of the vegetation index calculated according to the data of the MODIS and Landsat 8 satellites, was 101 and 106 mg kg–1, respectively. An analysis of histograms of the distribution of NDVI values within the NPZ was carried out, which showed how the true frequencies of occurrence of the values of the vegetation index correspond to the empirical boundary of the NPZ associated with the maximum dose dependence point identified by the study of the 2012. The nature of the distribution of true NDVI values for the NPZ exclusively fully supports the used zoning theory based on the analysis of dose dependence. The modern boundaries of the SNCS natural protection zone are 4‒7 km away from the center of the sanitary protection zone. [ABSTRACT FROM AUTHOR]

This article explores novel in-orbit drag-free technology that can be utilized for deep space detection scientific missions. In this study, we considered a two-test-mass drag-free method and analyzed the design of the drag-free and attitude control system for the TianQin mission. The entire control system was comprehensively designed, including an actuator allocation design and controllers for two test masses and one spacecraft, with a total of 18 degrees of freedom. Furthermore, stability analysis was conducted. Based on our design, numerical analysis and simulations were performed assuming geocentric orbit conditions in the TianQin mission, confirming the feasibility of this aerospace engineering concept. The versatility of the design allows for its application to scientific observations across various disciplines by modifying the structure of the simulation environment, and consequently, the approach discussed in this study holds significant practical implications for effectively accomplishing deep space observation tasks. [ABSTRACT FROM AUTHOR]

This paper is based on the work presented at EVS36 in Sacramento. The core of the work deals with the cabin climate control of battery electric vehicles (BEV) using model predictive control (MPC) approaches. These aim to reduce the energy demand for cabin air conditioning while maintaining comfort and air quality. The first step briefly overviews model predictive control approaches and the respective fundamentals. Afterward, the modeling for the system dynamics is explained. The challenge for the system model considering humid air is discussed, and the first implementation method is presented. With the added equations for the air quality and humidity, a logic to prevent window fogging was developed to improve safety. Ultimately, model-in-the-loop (MiL) investigations identified an energy-saving potential of up to 15.4% for cold and 39.7% for hot conditions compared to a rule-based strategy. In addition, the investigations carried out showed that it was also possible to improve indoor comfort by specifically influencing the air quality and humidity. Together with the safety criteria introduced to prevent window fogging, it was possible to present a strategy that can significantly improve thermal management for the cabin in modern BEVs. [ABSTRACT FROM AUTHOR]

To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is characterized by an accumulation of pathologic amyloid-beta (Aβ) and Tau proteins, neuroinflammation, metabolic changes and neuronal death. Reactive astrocytes participate in these pathophysiological processes by releasing pro-inflammatory molecules and recruiting the immune system, which further reinforces inflammation and contributes to neuronal death. Besides these neurotoxic effects, astrocytes can protect neurons by providing them with high amounts of lactate as energy fuel. Astrocytes rely on aerobic glycolysis to generate lactate by reducing pyruvate, the end product of glycolysis, through lactate dehydrogenase. Consequently, limited amounts of pyruvate enter astrocytic mitochondria through the Mitochondrial Pyruvate Carrier (MPC) to be oxidized. The MPC is a heterodimer composed of two subunits MPC1 and MPC2, the function of which in astrocytes has been poorly investigated. Here, we analyzed the role of the MPC in the pathogeny of AD, knowing that a reduction in overall glucose metabolism has been associated with a drop in cognitive performances and an accumulation of Aβ and Tau. We generated 3xTgAD mice in which MPC1 was knocked-out in astrocytes specifically and focused our study on the biochemical hallmarks of the disease, mainly Aβ and neurofibrillary tangle production. We show that inhibition of the MPC before the onset of the disease significantly reduces the quantity of Aβ and Tau aggregates in the brain of 3xTgAD mice, suggesting that acting on astrocytic glucose metabolism early on could hinder the progression of the disease.