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Aiming at the current control steady‐state error caused by sampling delay, parameters mismatch of direct‐drive permanent magnet synchronous motor (PMSM). This paper proposed a method of dual model predictive control (MPC) of direct‐drive PMSM based on sliding mode disturbance observer (SMDO). Deadbeat predictive current control (DPCC) is used for current loop, a SMDO is proposed to observe the system disturbance, the estimation is compensated to DPCC by feedback to decrease the current control steady‐state error. For speed loop, MPC with an internal model control observer (IMCO) can observe and compensate the load disturbance feedforward. The experimental results show that, the dynamic response speed of the proposed strategy is fast, the speed recovery time after sudden load torque is short, and the anti‐load disturbance performance and current tracking performance are effectively improved. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Generally, windows in greenhouses are automatically opened and closed to regulate the internal temperature. However, because the air outside during the winter in Japan is dry, opening windows to reduce the temperature causes the humidity deficit to increase above 6 g/m3, thereby inhibiting plant growth. Therefore, in this study, we developed a model that considers the effects of weather and the sampling period using a subspace (N4SID) method based on environmental data from inside and outside a greenhouse during winter. By adopting a data-driven model, models for greenhouse temperature and humidity deficits can be constructed conveniently. First, four models incorporating weather conditions were constructed over a 28-day modelling period. Moreover, the average root mean square error values from 8:00 to 16:00 during the 10-day model evaluation period were examined. Subsequently, model predictive controllers were developed from the four models with sampling periods of 1, 2, 4, and 8 min, and their performances were compared over the model evaluation period. The model predictive controller with a sampling period of 4 min was the most energy-efficient, achieving control of the humidity deficit of up to at most 6 g/m3 (close to the target value of 4.5 g/m3) while maintaining the target temperature of 26 °C. [ABSTRACT FROM AUTHOR]

We devised a data-driven framework for uncovering hidden control strategies used by an evolutionary system described by an evolutionary probability distribution. This innovative framework enables deciphering of the concealed mechanisms that contribute to the progression or mitigation of such situations as the spread of COVID-19. Novel algorithms are used to estimate the optimal control in tandem with the parameters for evolution in general dynamical systems, thereby extending the concept of model predictive control. This marks a significant departure from conventional control methods, which require knowledge of the system to manipulate its evolution and of the controller's strategy or parameters. We use a generalized additive model, supplemented by extensive statistical testing, to identify a set of predictor covariates closely linked to the control. Using real-world COVID-19 data, we delineate the descriptive behaviors of the COVID-19 epidemics in five prefectures in Japan and nine countries. We compare these nine countries and group them on the basis of shared profiles, providing valuable insights into their pandemic responses. Our findings underscore the potential of our framework as a powerful tool for understanding and managing complex evolutionary processes. [ABSTRACT FROM AUTHOR]

Research on global harmonic mitigation against distributed and high‐proportioned nonlinear loads in modern distribution networks has received extensive attention. In this paper, a distributed compensation method for active power filters (APFs) is proposed, which utilizes a small number of APFs with adjustable reference to alleviate the global voltage distortion caused by a large number of distributed nonlinear loads. The model predictive control (MPC) is employed to calculate the optimal current reference for compensators at each optimization moment by predicting the system's performance in a certain future time period. Furthermore, the Kalman filter (KF) algorithm is used to estimate the initial harmonic voltage for the MPC, reducing the unfavorable effects of the model uncertainty and measurement noise. The test results based on a seven‐bus distribution network in MATLAB are provided to verify the effectiveness of the proposed method. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Model predictive control has been wildly used in the modern power electronic converter with the advantages of the multi‐objective optimization, no additional modulator is required and small grid current harmonic. However, the switching operation is not constant and higher sampling frequency must be required. A novel direct power control using model predictive control (DPC‐MPC) strategy is presented in this paper, which achieve the optimal vector prediction scheme with constant switching frequency operation. The calculation process is greatly simplified due to the optimal voltage vector generated directly, then, it applied to the control of a three‐phase three‐level T‐type grid converter. A cost function is first built to balance the neutral‐point‐potential voltage. After then, the cost function is updated by considering the delay caused by the calculation, sampling and gate driver. Finally, a comprehensive comparative study with the conventional DPC‐MPC strategy is introduced to verify the superiority of the presented DPC‐MPC. The simulation and experimental results show that the steady and the transient performance of the grid current with the proposed DPC‐MPC control strategy are effectively improved. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Virtual vector is applied in neutral‐point‐clamped (NPC) three‐level converter finite control set model predictive control (FCS‐MPC) strategy due to its good performance of less current ripples and high power efficiency. However, in the process of adding virtual vectors, some redundant virtual vectors will be selected. Excessive virtual vector will increase the computational burden and affect grid‐tied current performance. A virtual vector optimal selection strategy for model predictive control to determine the vectors is proposed. The strategy calculates the fluctuation of grid‐tied current through theoretical analysis. Based on the analysis of amplitude fluctuation of grid‐tied current, the graphics of coverage angle visualization can be obtained under different virtual vector. The angle of the required virtual vectors is obtained through the graphics of coverage angle visualization. The number of virtual vector can be determined when the error current angle range covers the entire vector graph. The proposed control strategy was compared with the conventional control strategies, including excessive virtual vectors or with no any virtual vector. Experimental results show that the proposed control strategy can significantly reduce THD, grid‐tied current error and computational burden. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With increase in the number of photovoltaic system installations, the prospect of voltage deviation from the acceptable range in a distribution system is increasing. Voltage distribution can be controlled using step voltage regulators (SVRs) and static var compensators (SVCs); however, for their coordination, it is necessary to stringently adjust the set value for each distribution line. In this study, a method of applying model predictive control to the coordination between an SVR and an SVC is investigated. In this paper, we first describe the proposed method and subsequently present the results of the performance comparison of the proposed and conventional methods to validate the effectiveness of the proposed method. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This work presents a load frequency control scheme in Renewable Energy Sources(RESs) power system by applying Model Predictive Control(MPC). The MPC is designed depending on the first model parameter and then investigate its performance on the second model to confirm its robustness and effectiveness over a wide range of operating conditions. The first model is 100% RESs system with Photovoltaic generation(PV), wind generation(WG), fuel cell, seawater electrolyzer, and storage battery. From the simulation results of the first case, it shows the control scheme is efficiency. And base on the good results of the first case study, to propose a second case using a 10-bus power system of Okinawa island, Japan, to verify the efficiency of proposed MPC control scheme again. In addition, in the second case, there also applied storage devices, demand-response technique and RESs output control to compensate the system frequency balance. Last, there have a detailed results analysis to compare the two cases simulation results, and then to Prospects for future research. All the simulations of this work are performed in Matlabr/Simulinkr. [ABSTRACT FROM AUTHOR]

The multilevel inverter is the most advanced power conversion system, whose main advantage is to improve power quality and efficiency. Cascaded H‐bridge (CHB) multilevel inverter is a kind of inverter that can be selected in the field of motor drive systems, electric vehicles and photovoltaic grid‐connected power generations. However, the CHB multilevel inverters require a great deal of independent direct current (dc) power supplies, which makes the realization of these inverters very complicated in practical applications. In this article, model predictive control for CHB inverter with flying capacitors is proposed to improve the above drawbacks. Simulation and experimental results show that voltage balance of the flying capacitor can be maintained and simultaneously the output voltage of the inverter normally. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In order to improve the dynamic response performance and robustness of the permanent magnet synchronous motor control system and optimize the parameter adjustment of the control system, a motor control method based on single‐loop model prediction and moment of inertia parameter identification with forgetting factor is proposed. First, the motor state equation in the prediction model is designed, and the single‐loop model predictive control method is proposed. Then, comparing the effects of various parameters of the motor on the running stability of the motor, a least‐squares identification method based on forgetting factor is added to identify the moment of inertia online and optimize the prediction model in real time. The simulation and experimental results show that the proposed method has good dynamic performance for the permanent magnet synchronous motor adapting to the rotational speed and load variation. At the same time, the addition of the moment of inertia identification enhances the accuracy and system robustness of the motor model parameters during the model prediction process. This method provides an effective way of engineering control. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Considering solar photovoltaic (PV) usage in building energy systems (BES), energy systems that combine batteries and solar PV (PVB) have been widely used in buildings. Ensuring the safety of the battery and the operation requirements of other types of equipment to the extent possible while achieving the optimization goal has become an important challenge in the implementation of this system. In this case, model predictive control (MPC) has gained increasing attention in the operation control of BES owing to its strong fitting ability. However, the prediction models at the core of MPC are not adequately designed for the BES optimization problem, and numerous studies have not yet been modeled and tested in real buildings. In this study, we used a long- and short-term hybrid prediction model in the MPC framework. The proposed prediction models and MPC framework were validated using a measured dataset from a real office building in Japan. The results show that, compared with the currently used baseline control logic, the proposed hybrid prediction MPC framework can improve the battery safety index by 81.6%, while the combined heat and power continuous operation index can also be improved by 36.4%. Compared with the conventional MPC framework, the proposed hybrid prediction MPC framework improves the optimization of off-grid operation by approximately 69% while maintaining the remaining optimization objectives. • Off-grid oriented operation and equipment safety are considered at the same time. • Hybrid prediction model is designed for model predictive control. • Modeling and algorithm are based on the measured data of real existing buildings. [ABSTRACT FROM AUTHOR]

Existing optimal control systems for vehicles that consider the effect of road slopes use a cost function with fixed weights related to speed deviation, regardless of driving states on slopes. As a result, gravitational potential energy is not efficiently exploited and braking at down-slopes (which wastes energy) becomes unavoidable. Thus, there is still significant scope to improve fuel saving behavior on slopes. To address this opportunity, in this paper, we present a dynamic eco-driving system (EDS) for a (host) vehicle based on model predictive control (MPC) with fuzzy-tuned weights, which helps efficiently utilize the gravitational potential energy. In the proposed EDS, we formulate a nonlinear optimization problem with an appropriate prediction horizon and an objective function based on the factors affecting vehicle fuel consumption. The objective function's weight is tuned via fuzzy inference techniques using information of the vehicle's instantaneous velocity and the road slope angle. By considering the vehicle longitudinal dynamics, preceding vehicle's state, and road slope information (obtained from the digital road map), the optimization generates velocity trajectories for the host vehicle that minimizes fuel consumption and CO 2 emission. We also investigate the traffic flow performance of following vehicles (behind the host vehicle) in dense traffic; this was not considered in existing works on hilly roads. The effectiveness of the proposed EDS is evaluated using microscopic traffic simulations on a real road stretch in Fukuoka City, Japan, and the results demonstrate that the fuzzy-tuned MPC EDS significantly reduces fuel consumption and CO 2 emission of the host vehicle compared to the traditional driving (human-based) system (TDS) for the same travel time. In dense traffic, the fuel consumption and CO 2 emission of following vehicles are noticeably reduced. • A fast fuzzy-tuned MPC framework is developed for eco-driving on hilly roads. • A dynamically tunable cost function is introduced to optimize the use of gravity. • The framework is tested in dense traffic using data of a road in Fukuoka, Japan. • The controlled and following cars show improved fuel consumption and CO 2 emission. [ABSTRACT FROM AUTHOR]