Title :
Optimal linear quadratic tracking using genetic approach
Author :
Mansouri, Naghmeh ; Khaloozadeh, Hamid
Author_Institution :
Dept. of Electr. Eng., Ferdwosi Univ. of Mashhad, Iran
Abstract :
In this paper a genetic approach is presented to the optimal linear quadratic tracking problem. In this problem the control law is designed such that a cost function is minimized. The design specifications, depends on choosing the weighting matrices Q and R. One must carry out a trial- and error process to choose the weighting matrices that can satisfy the design specifications. To overcome this difficulty we employ the genetic algorithm to find the proper weighting matrices. A computer simulation is performed to track a desired reference trajectory in a motor-generator set as a multivariable system. This method is compared with the one proposed by Bryson and Ho (1975).
Keywords :
Riccati equations; electric generators; electric motors; genetic algorithms; linear quadratic control; multivariable systems; performance index; tracking; Riccati equation; design specifications; genetic algorithm; genetic search; linear quadratic control; motor-generator set; multivariable system; optimal control; optimization; performance index; tracking; weighting matrices; Control theory; Cost function; Feedback; Genetics; Nonlinear equations; Observability; Optimal control; Performance analysis; Riccati equations; Trajectory;
Conference_Titel :
Artificial Intelligence Systems, 2002. (ICAIS 2002). 2002 IEEE International Conference on
Print_ISBN :
0-7695-1733-1
DOI :
10.1109/ICAIS.2002.1048122
