BibTex Citation Data :
@article{JM1236, author = {Madchan Anis and Widowati Widowati}, title = {DESAIN UMPAN BALIK KEADAAN MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION DAN DIFFERENTIAL EVOLUTIONALGORITHM STUDI KASUS GERAK LATERAL PESAWAT F-16}, journal = {Jurnal Matematika}, volume = {1}, number = {1}, year = {2012}, keywords = {LQR-invinite horizone, weighting matrix, Particle Swarm Optimization (PSO), Differential Evolution Algorithm (DEA)}, abstract = { The purpose of Linear Quadratic Regulator (LQR) optimal control system is to stabilize the system, so that the output of the system towards a steady state by minimizing the performance index. LQR- invinite horizon is a special case of LQR in thecontinuous time area where the terminal time of the performance index value for infinite time and infinite outputsystem is zero. Performance index will be affected by the weighting matrix. In this paper will be discussed about the application of Particle Swarm Optimization algorithm (PSO) and Differential Evolution Algorithm (DEA) to determine the state feedback of a closed loop system and weighting matrices in the LQR to minimize performance index. PSO algorithm is a computational algorithm inspired by social behavior of flocks of birds and fishes in searching of food. While the DEA is an optimization algorithm that is adopted from evolution and genetics of organisms. Simulations of the PSO algorithm will be compared with DEA. Based on case study, DEA is faster then PSO to get convergence to the optimum solution. }, pages = {148--161} url = {https://ejournal3.undip.ac.id/index.php/matematika/article/view/1236} }
Refworks Citation Data :
The purpose of Linear Quadratic Regulator (LQR) optimal control system is to stabilize the system, so that the output of the system towards a steady state by minimizing the performance index. LQR-invinite horizon is a special case of LQR in thecontinuous time area where the terminal time of the performance index value for infinite time and infinite outputsystem is zero. Performance index will be affected by the weighting matrix. In this paper will be discussed about the application of Particle Swarm Optimization algorithm (PSO) and Differential Evolution Algorithm (DEA) to determine the state feedback of a closed loop system and weighting matrices in the LQR to minimize performance index. PSO algorithm is a computational algorithm inspired by social behavior of flocks of birds and fishes in searching of food. While the DEA is an optimization algorithm that is adopted from evolution and genetics of organisms. Simulations of the PSO algorithm will be compared with DEA. Based on case study, DEA is faster then PSO to get convergence to the optimum solution.
Last update:
