Method of Inequalities-based Multiobjective Genetic Algorithm for Optimizing a Cart-double-pendulum System  

作　　者：Tung-Kuan Liu Chiu-Hung Chen Zu-Shu Li Jyh-Horng Chou 

机构地区：[1]Institute of Engineering Science and Technology, National Kaohsiung First University of Science and Technology, Taiwan, China [2]Institute of Artificial Intelligence System, Chongqing Institute of Technology, Chongqing 400050, China

出　　处：《International Journal of Automation and computing》2009年第1期29-37,共9页国际自动化与计算杂志（英文版）

基　　金：supported by the National Science Council, Taiwan(No. 96-2221-E-327-027, No. 96-2221-E-327-005-MY2, and No. 96-2628-E-327-004-MY3).

摘　　要：This article presents a multiobjective approach to the design of the controller for the swing-up and handstand control of a general cart-double-pendulum system （CDPS）. The designed controller, which is based on the human-simulated intelligent control （HSIC） method, builds up different control modes to monitor and control the CDPS during four kinetic phases consisting of an initial oscillation phase, a swing-up phase, a posture adjustment phase, and a balance control phase. For the approach, the original method of inequalities-based （MoI） multiobjective genetic algorithm （MMGA） is extended and applied to the case study which uses a set of performance indices that includes the cart displacement over the rail boundary, the number of swings, the settling time, the overshoot of the total energy, and the control effort. The simulation results show good responses of the CDPS with the controllers obtained by the proposed approach.This article presents a multiobjective approach to the design of the controller for the swing-up and handstand control of a general cart-double-pendulum system （CDPS）. The designed controller, which is based on the human-simulated intelligent control （HSIC） method, builds up different control modes to monitor and control the CDPS during four kinetic phases consisting of an initial oscillation phase, a swing-up phase, a posture adjustment phase, and a balance control phase. For the approach, the original method of inequalities-based （MoI） multiobjective genetic algorithm （MMGA） is extended and applied to the case study which uses a set of performance indices that includes the cart displacement over the rail boundary, the number of swings, the settling time, the overshoot of the total energy, and the control effort. The simulation results show good responses of the CDPS with the controllers obtained by the proposed approach.

关 键 词：Genetic algorithms human-simulated intelligent control （HSIC） method of inequalities （MoI） multiobjective control. 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]