Search

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.

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]

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, 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]

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 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]

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]

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.

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]

Power controlling on reliability-aware GPU clusters with dynamically variable voltage and speed is investigated as combinatorial optimization problem, namely the problem of minimizing task execution time with energy consumption constraint and the problem of minimizing energy consumption with system reliability constraint. The two problems have applied in general multiprocessor computing and real-time multiprocessing systems where energy consumption and system reliability both are important. These problems which emphasize the trade-off among performance, power and reliability have not been well studied before. In this research, a novel power control model is built based on Model Prediction Control theory. Maximum Entropy Method is used to determine partial ordering relation of control variable and to identify the quality of solutions. Our controller can cap the redundant energy consumption by dynamically transforming energy states of the nodes in GPU cluster. We compare our controller with the control scheme, which does not consider the system reliability. The experimental results demonstrate that the proposed controller is more reliable and valuable. [ABSTRACT FROM AUTHOR]