Journal of Control Science and Engineering
 Journal metrics
Acceptance rate15%
Submission to final decision59 days
Acceptance to publication29 days
CiteScore0.720
Impact Factor-
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An Improved Method of Particle Swarm Optimization for Path Planning of Mobile Robot

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Journal of Control Science and Engineering publishes research investigating the design, simulation and modelling, implementation, and analysis of methods and technologies for control systems and applications.

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Research Article

Coordinated Optimal Control of Secondary Cooling and Final Electromagnetic Stirring for Continuous Casting Billets

Secondary cooling and final electromagnetic stirring (F-EMS) are both key technologies for continuous casting. These parameters are usually optimized and controlled separately which caused internal quality fluctuations in unsteady conditions. In this paper, a coordinated optimal control strategy based on a multiobjective particle swarm optimization (MOPSO) algorithm is proposed for the parameter optimization of secondary cooling and F-EMS, which is solved based on multiobjective particle swarm optimization (MOPSO) algorithm. The solidification and heat transfer model are developed for the computation of billet temperature and the solidification, and the adaptive grid method is used to improve the diversity and robustness of optimal solutions. The secondary cooling water and F-EMS’ stirring current are dynamically controlled based on the optimization results. The results of field trials showed that the maximum carbon segregation and other quality indexes of billets can be improved significantly.

Research Article

Controller for UAV to Oppose Different Kinds of Wind in the Environment

Small UAVs are susceptible to the external disturbance, especially the wind field disturbance in the atmosphere environment. As a result, UAV’s states including attitude, speed, and position are usually unable to track the desired control commands. In this paper, different types of wind fields which easily affect the UAV are summarized; furthermore, the mechanism of their wind fields affecting the UAV is first strictly analyzed. Next, a novel “reject external disturbance” flight mode for UAV is put forward to offset the trajectory deviation caused by side wind, which makes use of the wind speed information obtained by airspeed and ground speed of UAV. In order to implement the “reject external disturbance” flight mode, the Lyapunov stability theory-based variable model reference adaptive control (VMRAC) system is proposed, and it could also deal with the adverse effects of wind shear and turbulence on UAV flight. Finally, simulation results show that the proposed strategy can significantly improve the trajectory following quality of the UAV under wind disturbance.

Research Article

Design of PI Controller for Voltage Controller of Four-Phase Interleaved Boost Converter Using Particle Swarm Optimization

This article introduces voltage feedback controlling using the PI controller tuned gains by metaheuristic optimizations for a four-phase interleaved boost converter. The metaheuristic optimizations, particle swarm optimization (PSO), genetic algorithm (GA), and Tabu search (TS) are applied to find the optimal gains for the proposed control system. In experiment, the designed control system is implemented on the DSP board TMS320F28335 with MATLAB/Simulink. In this paper, there are two conditions of the control system in the test, without load and with load. The response result of the proposed control system tuned gains by PSO is no overshoot and approaches to the steady state better than GA and TS methods. Moreover, it is able to maintain the output voltage feedback at a constant level according to the control signal both without load and with load conditions. As a result, the four-phase interleaved boost converter is regulated by the PI controller tuned gains with PSO which could efficiently maintain the voltage of both levels.

Research Article

Research on Key Control Technology of Intelligent Rolling Contact Fatigue Test Facility

An intelligent rolling contact fatigue test equipment is developed, and the control methods are presented. For obtaining the slip accurately, the control method based on master-slave synchronization is proposed. For controlling the loads in high precision, the control method took into consideration the influence by two factors, displacement and the load. The nonlinear interference and excess torque in load control are effectively suppressed. Based on the SIMOTION D425 which is the Siemens integrated motion control system, the control system architecture of the intelligent rolling contact fatigue test equipment is constructed. The solutions of slip ratio and the experimental load controlled by these methods are satisfactory with the requirement of design precision. In the validation experiment, the load control accuracy is ±3%, the average error of load control is 1.77%, and the average error of slip control is 0.26%. The experiment results show the proposed control methods are feasible and effective.

Research Article

An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem

The grasshopper optimization algorithm (GOA) is a novel metaheuristic algorithm. Because of its easy deployment and high accuracy, it is widely used in a variety of industrial scenarios and obtains good solution. But, at the same time, the GOA algorithm has some shortcomings: (1) original linear convergence parameter causes the processes of exploration and exploitation unbalanced; (2) unstable convergence speed; and (3) easy to fall into the local optimum. In this paper, we propose an enhanced grasshopper optimization algorithm (EGOA) using a nonlinear convergence parameter, niche mechanism, and the β-hill climbing technique to overcome the abovementioned shortcomings. In order to evaluate EGOA, we first select the benchmark set of GOA authors to test the performance improvement of EGOA compared to the basic GOA. The analysis includes exploration ability, exploitation ability, and convergence speed. Second, we select the novel CEC2019 benchmark set to test the optimization ability of EGOA in complex problems. According to the analysis of the results of the algorithms in two benchmark sets, it can be found that EGOA performs better than the other five metaheuristic algorithms. In order to further evaluate EGOA, we also apply EGOA to the engineering problem, such as the bin packing problem. We test EGOA and five other metaheuristic algorithms in SchWae2 instance. After analyzing the test results by the Friedman test, we can find that the performance of EGOA is better than other algorithms in bin packing problems.

Research Article

Traffic Accident Prediction Based on LSTM-GBRT Model

Road traffic accidents are a concrete manifestation of road traffic safety levels. The current traffic accident prediction has a problem of low accuracy. In order to provide traffic management departments with more accurate forecast data, it can be applied in the traffic management system to help make scientific decisions. This paper establishes a traffic accident prediction model based on LSTM-GBRT (long short-term memory, gradient boosted regression trees) and predicts traffic accident safety level indicators by training traffic accident-related data. Compared with various regression models and neural network models, the experimental results show that the LSTM-GBRT model has a good fitting effect and robustness. The LSTM-GBRT model can accurately predict the safety level of traffic accidents, so that the traffic management department can better grasp the situation of traffic safety levels.

Journal of Control Science and Engineering
 Journal metrics
Acceptance rate15%
Submission to final decision59 days
Acceptance to publication29 days
CiteScore0.720
Impact Factor-
 Submit

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