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This paper presents a lateral control scheme based on event‐triggered model predictive control for trajectory tracking of autonomous vehicles. Firstly, the augmentation system is constructed based on the known road curvature information, and the model predictive controller is utilized to obtain the optimal control sequence. Then, an event‐triggered mechanism is introduced to improve the real‐time performance of the control system. The strategy targets to reduce the computational complexity and solving frequency of the optimization problem. In addition, a contraction constraint is structured using the backstepping control strategy to ensure the stability of the control system. Finally, experiments are conducted through the CarSim/Simulink joint simulation platform, and compared with the traditional model predictive control, the method proposed in this paper has better tracking accuracy and improves the real‐time performance of the control system. [ABSTRACT FROM AUTHOR]

In order to solve the problem of voltage fluctuation caused by the grid integration of wind power cluster, a multitime scale reactive power and voltage optimal regulation method based on model predictive control (MPC) is proposed in this paper. In the day‐ahead stage, the reduction of network active power loss is mainly achieved through the regulation of discrete reactive power compensation devices and generator units, focusing on the economic operation of the power system. In the intraday stage, considering the rapid response characteristics of continuous reactive power compensation devices, the optimal regulation aiming to minimize voltage control deviation is carried out under 15‐ and 5‐min time scale based on the MPC algorithm with the adjustment of continuous reactive power compensation devices. In the feedback correction stage, a fast calculation method considering reactive power partitioning is adopted instead of solving the 5‐min optimization model for 288 periods, avoiding excessive adjustment of reactive compensation devices. While effectively improving voltage fluctuations caused by wind power prediction deviation, it also alleviated the computational and communication burden on the system. Finally, the effectiveness of the proposed regulation method in this paper is verified with the improved IEEE‐39 test case. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a lateral control scheme based on event‐triggered model predictive control for trajectory tracking of autonomous vehicles. Firstly, the augmentation system is constructed based on the known road curvature information, and the model predictive controller is utilized to obtain the optimal control sequence. Then, an event‐triggered mechanism is introduced to improve the real‐time performance of the control system. The strategy targets to reduce the computational complexity and solving frequency of the optimization problem. In addition, a contraction constraint is structured using the backstepping control strategy to ensure the stability of the control system. Finally, experiments are conducted through the CarSim/Simulink joint simulation platform, and compared with the traditional model predictive control, the method proposed in this paper has better tracking accuracy and improves the real‐time performance of the control system.

In order to reduce the negative influence of wind speed randomness and prediction error on frequency modulation, the reliability of the wind storage system was assessed effectively. In the wind storage frequency modulation system, a state of charge (SOC) adaptive adjustment method for wind speed randomness is proposed. Firstly, through the correlation analysis between the standby capacity of frequency modulation and the output power of wind turbine, the uncertainty of its frequency modulation capacity is revealed. Secondly, in view of the uncertainty of wind turbine frequency modulation, the output power of energy storage frequency modulation is optimized with the goal of minimizing the frequency modulation power deviation of the wind storage front under the framework of model predictive control, and the improved whale optimization algorithm (WOA) is used to solve the problem. Finally, the simulation results show that, under the given 5 min continuous disturbance, the root mean square of frequency regulation of the proposed restoration method is reduced by 56.65% compared to the SOC recovery base point set to 0.5. Under continuous large perturbations, the maximum frequency deviation is reduced by 0.0455 Hz. This effectively shows that this method can not only improve the frequency modulation reliability of wind power system but also improve the continuous frequency modulation capability of energy storage system. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering 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.)

ObjectiveThe independent cabling operation of an autonomous underwater vehicle (AUV) in complex submarine terrain should balance cabling quality with self-safety, which means that the AUV should maintain a stable height relative to the seabed. To this end, this paper designs an optimal obstacle avoidance design for a cable-laying AUV based on model prediction control (MPC).Method First, the method establishes a path-following control model based on MPC. It then classifies different obstacles into topographic bulges or depressions, and establishes a simplified mathematical model of the obstacles. Second, the method designs multiple objective optimization functions for different topographic environments according to the feature points, allowing the AUV to choose the shortest path with the minimum variation in cable-laying height. ResultsThe results show that this method achieves the most minimal changes in cable-laying height while also choosing the most optimal path to avoid obstacles. ConclusionThe proposed method not only ensures the safety of AUVs in complex submarine terrain, but also greatly improves the laying quality of submarine cables.

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology 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 order to reduce the negative influence of wind speed randomness and prediction error on frequency modulation, the reliability of the wind storage system was assessed effectively. In the wind storage frequency modulation system, a state of charge (SOC) adaptive adjustment method for wind speed randomness is proposed. Firstly, through the correlation analysis between the standby capacity of frequency modulation and the output power of wind turbine, the uncertainty of its frequency modulation capacity is revealed. Secondly, in view of the uncertainty of wind turbine frequency modulation, the output power of energy storage frequency modulation is optimized with the goal of minimizing the frequency modulation power deviation of the wind storage front under the framework of model predictive control, and the improved whale optimization algorithm (WOA) is used to solve the problem. Finally, the simulation results show that, under the given 5 min continuous disturbance, the root mean square of frequency regulation of the proposed restoration method is reduced by 56.65% compared to the SOC recovery base point set to 0.5. Under continuous large perturbations, the maximum frequency deviation is reduced by 0.0455 Hz. This effectively shows that this method can not only improve the frequency modulation reliability of wind power system but also improve the continuous frequency modulation capability of energy storage system.

In this paper, a UAV-WPT based on a new orthogonal coupling structure is first designed. An offset weight method is proposed to optimize the design of the system resonance parameters to improve the power transmission efficiency in the offset state. The coupling mechanism's coupling ability and anti-offset performance are experimented with using Ansys software simulation. The system adopts LCC-S topology and proposes a secondary side constant current control strategy based on the model prediction control to ensure constant current output in the case of coupling coefficient deviation. Finally, an experimental platform is designed for experiments. The results show that the system can quickly realize 3A constant current output under the change of load and coupling coefficient, and at the same time, improve the working efficiency of the system. [ABSTRACT FROM AUTHOR]

To improve energy consumption economy and fuel cell life economy, this article proposes a multiobjective real‐time energy management strategy based on short‐term power smoothing prediction and distance adaptive state‐of‐charge (SOC) consumption for fuel cell/battery plug‐in hybrid electric bus. First, a distance adaptive SOC real‐time reference trajectory planning method is proposed by collecting the actual operating condition data of Dalian city buses. Second, the motor power is smoothed using the first‐order exponential smoothing approach, and the long short term memory network is adopted to predict the smoothed motor power. Finally, based on the predicted motor power and a real‐time reference SOC, a model predictive control strategy is designed in the finite prediction horizon, and dynamic programming is used to solve multiobjective cost functions to achieve optimal power allocation. Simulation results show that the proposed strategy has remarkable performance in terms of the total operating cost, SOC sustenance, and fuel cell degradation compared with the rule‐based strategy. In addition, the average time of online energy distribution in each step of the proposed strategy is 43.8 ms, which is much smaller than the simulation step length of 1s. [ABSTRACT FROM AUTHOR]

Energy management strategies with prediction message show great potential in optimizing control objectives for fuel cell hybrid vehicles. This paper presents a novelty model-predictive-control based energy management framework for fuel cell commercial vehicle, in which the vehicle mass is firstly introduced as a variable parameter for controller. A vehicle mass identification model is established and embed in the framework based on recursive least square algorithm, and the influence of variable algorithm parameters on estimator is evaluated. Then the effects of mass varying on vehicle performance is talked about in detail. In addition, the performance for different drive cycles on different loaded is also given. The simulation results show that the fuel consumption is positively correlated with the mass identification error, and the designed controller can reduce the additional fuel consumption to around 0.1%, which is much batter to the deterministic parameters scheme. Finally, the shortcomings of the designed controller are given. This paper provides a reliable theoretical basis to address varying-mass vehicle energy management problems.

In this paper, we address the problem of trajectory tracking control of autonomous vehicles by considering the nonlinear characteristics of tires. By considering the influence of the tires' dynamics on steering stability, the proposed predictive controller can track the desired trajectory and desired velocity in the presence of road curvature while minimizing the lateral tracking deviation. First of all, a hierarchical control structure is adopted, in which the upper-level controller is used to calculate the desired acceleration and the desired front-wheel angle to maintain the control target, and the lower-level controller realized the command through the corresponding component devices. Moreover, a force estimator is designed based on the radial basis function (RBF) neural network to estimate the lateral force of the tires, which is incorporated into the boundary conditions of the vehicle envelope constraint to improve the adaptability of the controller to the vehicle performance. Finally, the proposed controller is tested by co-simulation of CarSim (a simulation software specifically for vehicle dynamics)/Simulink (a modular diagram environment for multidomain simulation as well as model-based design) and hardware-in-loop simulation system. The co-simulation and experimental results demonstrate the controller safely driving at the vehicle's handling limits and effectively reduce the slip phenomenon of the vehicle. [ABSTRACT FROM AUTHOR]

In this paper, a UAV-WPT based on a new orthogonal coupling structure is first designed. An offset weight method is proposed to optimize the design of the system resonance parameters to improve the power transmission efficiency in the offset state. The coupling mechanism’s coupling ability and anti-offset performance are experimented with using Ansys software simulation. The system adopts LCC-S topology and proposes a secondary side constant current control strategy based on the model prediction control to ensure constant current output in the case of coupling coefficient deviation. Finally, an experimental platform is designed for experiments. The results show that the system can quickly realize 3A constant current output under the change of load and coupling coefficient, and at the same time, improve the working efficiency of the system.

Cranes are widely deployed for lifting and moving heavy objects in dynamic environments with human coexistence. Suddenly appeared workers, vehicles, and robots can affect the safety of the cranes. To avoid possible collisions, the cranes must have prediction ability to know how dangerous the situation is. In this paper, we address the safety issues of bridge cranes based on its online physical states and control model. Due to the swing of the payload, the safe braking distance cannot be a constant value. Therefore, we here propose a model prediction control (MPC)‐based anti‐swing method for non‐zero initial states, where a new reference trajectory and a new cost function for optimization are proposed, such that the proposed MPC method can control the crane to follow the proposed reference trajectory and achieve a stable stop state with anti‐swing. Furthermore, an offline learning mechanism is introduced to learn a statistical model between the velocity of the crane and the safe braking distance achieved by using the proposed MPC braking control method. In this way, we can predict how far the crane would require to safely stop without swing based on its current velocity, which is the safe distance prediction to evaluate the dangerous level of the dynamic obstacle. Experiments using both a simulated crane and a real crane demonstrate that the proposed safe braking distance prediction method is effective for safe braking control of the bridge cranes. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of trajectory tracking control of autonomous vehicles by considering the nonlinear characteristics of tires. By considering the influence of the tires’ dynamics on steering stability, the proposed predictive controller can track the desired trajectory and desired velocity in the presence of road curvature while minimizing the lateral tracking deviation. First of all, a hierarchical control structure is adopted, in which the upper-level controller is used to calculate the desired acceleration and the desired front-wheel angle to maintain the control target, and the lower-level controller realized the command through the corresponding component devices. Moreover, a force estimator is designed based on the radial basis function (RBF) neural network to estimate the lateral force of the tires, which is incorporated into the boundary conditions of the vehicle envelope constraint to improve the adaptability of the controller to the vehicle performance. Finally, the proposed controller is tested by co-simulation of CarSim (a simulation software specifically for vehicle dynamics)/Simulink (a modular diagram environment for multidomain simulation as well as model-based design) and hardware-in-loop simulation system. The co-simulation and experimental results demonstrate the controller safely driving at the vehicle’s handling limits and effectively reduce the slip phenomenon of the vehicle.

Cooperative terminal guidance with impact angle constraint is a key technology to achieve a saturation attack and improve combat effectiveness. The present study envisaged cooperative terminal guidance with impact angle constraint for multiple missiles. In this pursuit, initially, the three-dimensional cooperative terminal guidance law with multiple constraints was studied. The impact time cooperative strategy of virtual leader missile and follower missiles was designed by introducing virtual leader missiles. Subsequently, based on the distributed model prediction control combined with the particle swarm optimization algorithm, a cooperative terminal guidance algorithm was designed for multiple missiles with impact angle constraint that met the guidance accuracy. Finally, the effectiveness of the algorithm was verified using simulation experiments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & 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.)

The majority of large-scale data intensive applications designed by MapReduce model are deployed and executed on a large-scale distributed Hadoop system. Running such application on a large cluster requires a large amount of energy. Therefore improving energy efficiency and minimizing energy consumption when executing each MapReduce job is a critical concern for data centers. We propose a control model based on model prediction control (MPC) for improving energy efficiency of Hadoop cluster while satisfying performance goal. The controller can dynamically scale CPU frequency in facing the workloads changes. The wavelet neural network is utilized to build the prediction model, which can accurately estimate energy efficiency ratio based on the features extracting from the variation of Hadoop workloads. With the help of a wavelet neural network, the controller can guarantee the energy efficiency ratio converges to an ideal set point within a finite settling time. We perform the extensive experiments on a Hadoop cluster to compare our controller with the previous MPC controller. The results demonstrate that our controller outperforms the baseline on energy efficiency control and adapts to workloads of Hadoop cluster.

In order to solve the problem that the traditional finite set model predictive control applied to grid-connected inverter has delay and inductance varying with the working state, an improved finite set model predictive control method is proposed. By increasing the two sets of switching voltage vectors to reduce the current approximation error, the cost function value is minimized. And the inductance value is predicted in real time based on the idea of model predictive control. The improved finite set model predictive control method can realize the real-time tracking of the inductance value, improve the control precision, and effectively reduce the total harmonic distortion. Simulation and experimental results verify the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, in order to extend battery lifespan and lift power performance in hybrid batteries/supercapacitors electrical vehicles, a new energy management strategy is proposed based on model prediction control and adaptive method. Firstly, models of batteries and supercapacitors are built, which are then adapted and simplified to develop state space expression. Secondly, a series of reference values of battery power and supercapacitor state of charge for model prediction methods are properly calculated by the adaptive method. Thereafter, an energy management strategy based on the model prediction control method is designed to allocate the output power of batteries and supercapacitors within constraints, which guarantees batteries lifespan and the power performance of vehicle. Finally, simulation and experiment results are provided to evaluate battery lifespan and power performance of vehicles under HWFET and UDDS road conditions. The results obtained show that the proposed strategy, compared with the former methods, reduce average power and power variation of batteries, and effectively utilize supercapacitors depending on the power demand, which can extend battery lifespan and lift the power performance of vehicle. [ABSTRACT FROM AUTHOR]

In this work, iterative learning model predictive control approaches are presented to resolve the control problem of trajectory based aircraft operation with time-of-arrival constraint. Firstly, we formulated this problem being an output tracking issue with along-track wind disturbance. Next, P-type point-to-point iterative learning control algorithms are designed to overcome the influence of repetitive along-track wind. In addition, a point-topoint iterative learning model predictive control algorithm with variable prediction step is also designed to eliminate the interference of non-repetitive gusts on the air route. Finally, numerical simulations are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology 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.)

Flutter suppression of wind turbine blade section based on two types of model prediction control (MPC) algorithms is investigated. The pre-twist blade, exhibiting displacements of flap-wise bending and lead-lag bending, employs 2D airfoil analysis method, and is subjected to destructive divergent instability displacements. Dynamic modelling of two-dimensional (2D) blade section is based on data analysis characterized by data fitting. The MPC methods including standard MPC and terminal-weight MPC are used to realize flutter suppression for divergent instability. Vibration control and control effects of terminal-weight MPC on divergent instability are analyzed in detail, with different parameters of prediction horizon and control horizon discussed. The superiority of terminal-weight MPC can be apparently demonstrated by comparison of standard MPC and sliding mode control with disturbance observer (SMCDO). The feasibility of hardware implementation of MPC algorithm is discussed by human-computer interaction platform. The performance improvement method of the terminal-weight MPC based on offset control has been refined from the point of view of practical application, with its remarkable effect compared with the original terminal-weight MPC. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Heilongjiang University of Science & Technology is the property of Journal of Heilongjiang University of Science & Technology Editorial Department 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.)

Multiple Mobile Robots System (MMRS) has shown many attractive features in lots of real-world applications that motivate their rapid and wide diffusion. In MMRS, the Cooperative Localization (CL) is the basis and premise of its high-performance task. However, the statistical characteristics of the system noise should be already known in traditional CL algorithms, which is difficult to satisfy in actual MMRS because of the numerous of disturbances form the complex external environment. So the CL accuracy will be reduced. To solve this problem, an improved Adaptive Active Cooperative Localization (A2CL) algorithm based on information optimization strategy for MMRS is proposed in this manuscript. In this manuscript, an adaptive information fusion algorithm based on the variance component estimation under Extended Kalman filter (VCEKF) method for MMRS is introduced firstly to enhance the robustness and accuracy of information fusion by estimating the covariance matrix of the system noise or observation noise in real time. Besides, to decrease the effect of observation uncertainty on CL accuracy further, an observation optimization strategy based on information theory, the Model Predictive Control (MPC) strategy, is used here to maximize the information amount from observations. And semi-physical simulation experiments were carried out to verity the A2CL algorithm's performance finally. Results proved that the presented A2CL algorithm based on information optimization strategy for MMRS cannot only enhance the CL accuracy effectively but also have good robustness. [ABSTRACT FROM AUTHOR]

Piezo-actuated micro-/nanopositioning systems have been widely employed in diverse high-precision positioning applications. However, the inherent hysteresis nonlinearity seriously deteriorates the tracking performance of piezo-actuated stages. This paper presents the design, analysis, and validation of a novel control scheme termed model prediction control (MPC) with real-time modified reference based on disturbance observer (DOB) to suppress the hysteresis nonlinearity and model uncertainty, in which the nonlinear effects are treated as an unknown disturbance to the system. In order to remove the most of the interference and diminish the effect of noise, a DOB is designed for the non-minimum phase (NMP) system. Then the difference between the actual displacement and the output of the nominal model termed the residual error is estimated and used to modify the reference in real time for a better performance. By the proposed method, the model of the inherent hysteresis is not required and the controller is established based on the identified nominal model. Its effectiveness is validated through experimental investigations on a commercial nanopositioner. Experimental results show that the proposed method can improve the tracking performance of the piezo-actuated stage, as compared with the traditional MPC and DOB-based MPC. [ABSTRACT FROM AUTHOR]

Cooperative terminal guidance with impact angle constraint is a key technology to achieve a saturation attack and improve combat effectiveness. The present study envisaged cooperative terminal guidance with impact angle constraint for multiple missiles. In this pursuit, initially, the three-dimensional cooperative terminal guidance law with multiple constraints was studied. The impact time cooperative strategy of virtual leader missile and follower missiles was designed by introducing virtual leader missiles. Subsequently, based on the distributed model prediction control combined with the particle swarm optimization algorithm, a cooperative terminal guidance algorithm was designed for multiple missiles with impact angle constraint that met the guidance accuracy. Finally, the effectiveness of the algorithm was verified using simulation experiments.

The modular multilevel converter (MMC) has the advantages of high efficiency, low harmonic, modular design, and easy cascade, which has been widely used in the field of high voltage and large capacity energy conversion. In order to improve the dynamic response speed of the MMC-HVDC based on the modular multi-level converter, a novel method which combines a model predictive control (MPC) of MMC-HVDC system with improved sub-module voltage balanced control strategy is proposed. The method, which utilises the prediction model, feedback correction and rolling optimisation to obtain the optimal voltage control, overcomes the difficulties in the traditional way of setting PI parameters of the internal loop current controller and the outer loop controller and tackles the problem of low dynamic response. Finally, a 21-level MMC-HVDC system simulation model is built on PSCAD-EMTDC software platform. The simulation results indicate the effectiveness and feasibility of the control strategy.

An adaptive-prediction-horizon model prediction control-based path tracking controller for a four-wheel independent control electric vehicle is designed. Unlike traditional model prediction control with fixed prediction horizon, this paper devotes to satisfy the varied path tracking demand by adjusting online the prediction horizon of model prediction control according to its effect on vehicle dynamic characteristics. Vehicle dynamic stability quantized with the vehicle sideslip-feature phase plane is preferentially considered in the prediction horizon adjustment. For stability during switching prediction horizon and for robustness during path tracking, the numerical problem inherent in the adaptive-prediction-horizon model prediction control is analysed and solved by introducing exponentially decreasing weight. Subsequently, the desired motion for path tracking with the four-wheel independent control electric vehicle is realized with a hierarchical control structure. Simulation results finally illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The modular multilevel converter (MMC) has the advantages of high efficiency, low harmonic, modular design, and easy cascade, which has been widely used in the field of high voltage and large capacity energy conversion. In order to improve the dynamic response speed of the MMC-HVDC based on the modular multi-level converter, a novel method which combines a model predictive control (MPC) of MMC-HVDC system with improved sub-module voltage balanced control strategy is proposed. The method, which utilises the prediction model, feedback correction and rolling optimisation to obtain the optimal voltage control, overcomes the difficulties in the traditional way of setting PI parameters of the internal loop current controller and the outer loop controller and tackles the problem of low dynamic response. Finally, a 21-level MMC-HVDC system simulation model is built on PSCAD-EMTDC software platform. The simulation results indicate the effectiveness and feasibility of the control strategy. [ABSTRACT FROM AUTHOR]

Abstract Incorporating Battery Battery Energy Storage System (BESS) with wind farm to build up Wind-Storage Combined Generation System is a promising solution to improve the dependability of wind power. BESS sizing and generation scheduling optimization are the key points of this process to improve the economy and efficiency of the integrated combined system. In this paper, based on Quantization Index (QI) clustering, the fluctuation feature of real-time wind power output is studied to obtain the most economic capacity as well as maximum charging/discharging power of BESS in every scheduling circle (15min). Thus, this size of BESS is taken as parameters and constraints for MPC simulation process to minimize the deviation between generation scheduling plan and actual integrated power of Combined Generation System. The optimization results of case study shows that both economic size and ordinary size performs well for generation scheduling optimization, while the proposed BESS sizing method can reduce invest cost to a large extent. [ABSTRACT FROM AUTHOR]

Energy management strategy is vital for a plug-in hybrid electric vehicle and in this paper, a strategy based on stochastic model predictive control is proposed. Firstly, Markov Chain Monte Carlo Simulation is used to predict velocity sequences in the 10-second horizon followed by post-processing like average filtering, quadratic fitting, etc. which is meant to moderate fluctuations of the results. The RMSE is controlled around 2.4357 Km/h. Moreover, dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of SMPC-based strategy. The results show that the fuel economy of the strategy based on SMPC is around 13 percent worse than that on DP. However, with 14.7 L/100 km as fuel consumption, it is still within reasonable ranges. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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.)

Problems of constructing high-speed control systems of electric drives with switch-mode power converters are discussed in the present paper. Historical aspects of the development of these theoretical issues at the Department of Automated Electric Drives of the Moscow Power Engineering Institute and their importance are discussed. The described converters are control objects of a discrete type that should be considered in the creation of control systems. It is shown that dead-beat control in such systems in the unconstrained case makes a finite number of discreteness intervals equal to the system order. Two currently important principles in construction of a dead-beat control system-namely, ones using predictive models in a real and accelerated time scale-are considered. Since the operation quality of such systems is determined by the adequacy of the predictive model of the control object, the approaches to creating the self-adjustment algorithms allowing one to correct the model in operation are considered. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV) is the key to improve the energy utilization efficiency and vehicle fuel economy. In this paper, a model predictive control (MPC) based on EMS coupled with double Q-learning (DQL) is presented to allocate the power between multiple power sources for PHEV. Firstly, the powertrain framework of the PHEV and its mathematical models were analyzed in detail. Then, based on the required power and speed, an effective convergent offline learning controller was established based on DQL algorithm. Subsequently, the multi-feature input Elman neural network was implemented to predict vehicle speed in MPC, and the trained DQL controller was applied to solve the rolling optimization process in MPC to find the optimal battery output in the prediction horizon. Finally, the proposed strategy was verified in Autonomie software, and the simulation results show that the proposed strategy can achieve a superior fuel economy close to that of the offline stochastic dynamic planning strategy, meanwhile with a perfect adaptability for different state of charge (SOC) reference trajectories. [Display omitted] • An online EMS based on DQL-MPC is proposed for PHEV to improve fuel economy. • The multi-feature input Elman NN is applied to predict vehicle speed in MPC. • The proposed EMS has a perfect adaptability for different SOC reference trajectories. [ABSTRACT FROM AUTHOR]