Gain Scheduling for Networked Control System

Internet;delay analysis;delay compensation;control systems;DC motors;distributed control;real time system;mobile robots;telerobotics;networks;factory automation;distributed control;adaptive control;communication control applications;communication networks;communication protocols

Tipsuwan, Yodyium ; Griff L. Bilbro, Committee Member,Mo-Yuen Chow, Committee Chair,Hamid Krim, Committee Member,Douglas S. Reeves, Committee Member,Tipsuwan, Yodyium ; Griff L. Bilbro ; Committee Member ; Mo-Yuen Chow ; Committee Chair ; Hamid Krim ; Committee Member ; Douglas S. Reeves ; Committee Member

University:North Carolina State University

关键词: Internet;    delay analysis;    delay compensation;    control systems;    DC motors;    distributed control;    real time system;    mobile robots;    telerobotics;    networks;    factory automation;    distributed control;    adaptive control;    communication control applications;    communication networks;    communication protocols;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/4811/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

Performances of closed-loop control systems operated over a data network are typically degraded by network-induced delays. Furthermore, the closed-loop control systems can become unstable. The purpose of this research has been to develop a control methodology to handle network-induced delay effects using optimal gain scheduling on existing controllers. The proposed gain scheduling technique adapts controller gains externally by modifying a controller output to enable the controller for uses over a data network. Since existing controllers can still be utilized, the proposed methodology can reduce control system reinstallation and replacement costs. First, the effectiveness of the proposed gain scheduling technique on networked DC motor speed control using a PI (Proportional-Integral) controller is investigated. Also, the concept of network traffic condition measurement to select optimal controller gains is presented. Then, a middleware framework to measure network traffic conditions on an IP network based on delays and delay variations and to modify controller gains is described. Suggestion of using neural network in the gain scheduling scheme is also given. Finally, the gain scheduling technique with the middleware framework is then extended to mobile robot path-tracking control.