Energy Dispatching Based on an Improved PSO-ACO Algorithm

Song, Qisong; Yu, Liya; Li, Shaobo; Hanajima, Naohiko; Zhang, Xingxing; Pu, Ruiqiang

doi:10.1155/2023/3160184

Cited by 8 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To optimize the performance of the LIM predictive control system, the adjusting of design parameters and gains of the observer is necessitated. Therefore, this study employs a particle swarm optimization algorithm to determine the optimal values of these parameters, namely 13 , , , g g m n .The range of values for each parameter must adhere to the requirements outlined in the Lyapunov stability analysis in the previous section [24,25,26].…”

Section: Adaptive Observer Parameter Self-tuningmentioning

confidence: 99%

Predictive current control method based on IPSO-ASMO for LIM

2024

IEICE Electron. Express

View full text Add to dashboard Cite

This paper presents a new method to overcome the degraded control performance of linear induction motors (LIM) due to end effects. The method employs a parameter-self-tuning adaptive sliding mode observer (ASMO) and model predictive current control (MPCC), which first develops a LIM mathematical model considering end effects and then designs the MPCC to strengthen the control. To improve the accuracy of the flux observation, the method develops the ASMO and analyzes the stability of the observer. For parameter tuning, the method utilizes an improved particle swarm optimization algorithm (IPSO). Overall, the experimental findings validate the effectiveness of the proposed algorithm.

show abstract

Section: Adaptive Observer Parameter Self-tuningmentioning

confidence: 99%

Predictive current control method based on IPSO-ASMO for LIM

2024

IEICE Electron. Express

View full text Add to dashboard Cite

show abstract

“…These "particles" fly with a certain velocity and find the best global location after some iterations. The steps of the PSO algorithm are as follows (Moazen et al, 2023;Song et al, 2023;Yin et al, 2023).…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Application research on the prediction of tar yield of deep coal seam mining areas based on PSO-BPNN machine learning algorithm

Qiao

Wang

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

There are abundant deep coal resources in northern Shaanxi, but the fragile natural environment in this area hinders the large-scale exploitation of oil-rich coal. In-situ thermal conversion of deep coal to oil and gas will become an environmentally friendly technology for oil-rich coal mining. Accurate prediction of oil-rich coal tar yield in various regions is a prerequisite. Based on a particle swarm optimization algorithm and two machine learning algorithms, BP neural network and random forest, a prediction model of tar yield from oil-rich coal is constructed in this paper. Combined with the particle swarm optimization method, the problem of slow convergence speed and possibly falling into local minimum value of BP neural network is solved and optimized. The results showed that the PSO-BP had a convergence speed about five times faster than that of the BP neural network. Furthermore, the predicted value of the PSO-BP was consistent with the measured value, and the average relative error was 4.56% lower than that of the random forest model. The advantages of fast convergence and high accuracy of the prediction model are obviously apparent. Accurate prediction of tar yield would facilitate the research process of in-situ fluidized mining of deep coal seams.

show abstract

“…Kamel et al [30] applied a GA-PSO algorithm to a team of wheeled mobile robots' position control, which is superior to the GA or PSO algorithm alone. Song et al [31] improved the PSO-ACO algorithm to achieve comprehensive and global optimization of energy dispatching; the results showed the effectiveness and higher security of this algorithm.…”

Section: Introductionmentioning

confidence: 99%

Time-Optimal Trajectory Planning for Woodworking Manipulators Using an Improved PSO Algorithm

Chen,

Zhang,

2023

Applied Sciences

View full text Add to dashboard Cite

Woodworking manipulators are applied in wood processing to promote automatic levels in the wood industry. However, traditional trajectory planning results in low operational stability and inefficiency. Therefore, we propose a method combining 3-5-3 piecewise polynomial (composed of cubic and quintic polynomials) interpolation and an improved particle swarm optimization (PSO) algorithm to study trajectory planning and time optimization of woodworking manipulators. In trajectory planning, we conducted the kinematics analysis to determine the position information of joints at path points in joint space and used 3-5-3 piecewise polynomial interpolation to fit a point-to-point trajectory and ensure the stability. For trajectory time optimization, we propose an improved PSO that adapts multiple strategies and incorporates a golden sine optimization algorithm (Gold-SA). Therefore, the proposed improved PSO can be called GoldS-PSO. Using benchmark functions, we compared GoldS-PSO to four other types of PSO algorithms and Gold-SA to verify its effectiveness. Then, using GoldS-PSO to optimize the running time of each joint, our results showed that GoldS-PSO was superior to basic PSO and Gold-SA. The shortest running time obtained by using GoldS-PSO was 47.35% shorter than before optimization, 8.99% shorter than the basic PSO, and 6.23% shorter than the Gold-SA, which improved the running efficiency. Under optimal time for GoldS-PSO, our simulation results showed that the displacement and velocity of each joint were continuous and smooth, and the acceleration was stable without sudden changes, proving the method’s feasibility and superiority. This study can serve as the basis for the motion control system of woodworking manipulators and provide reference for agricultural and forestry engineering optimization problems.

show abstract

Energy Dispatching Based on an Improved PSO-ACO Algorithm

Cited by 8 publications

References 45 publications

Predictive current control method based on IPSO-ASMO for LIM

Predictive current control method based on IPSO-ASMO for LIM

Application research on the prediction of tar yield of deep coal seam mining areas based on PSO-BPNN machine learning algorithm

Time-Optimal Trajectory Planning for Woodworking Manipulators Using an Improved PSO Algorithm

Contact Info

Product

Resources

About