Title :
Complete Compensation for Time Delay in Networked Control System Based on GPC and BP Neural Network
Author :
Wang, Tian-kun ; Zhou, Li-hui ; Han, Pu ; Zhang, Qian
Author_Institution :
North China Electr. Power Univ., Beijing
Abstract :
A new framework is proposed to cope with the uncertain time delay of networked control system. Event-clock-driven controller nodes, together with clock-driven sensor nodes and actuator nodes are required in this framework. Queuing Strategy is introduced both in controller nodes and actuator nodes while the time delay between controller node and actuator node is compensated by multi-step control increment given by the algorithm of General Predictive Control. An output error prediction model is built using BP neural network to deal with the time delay between sensor node and controller node. The principle of this model is to revise the predictive output of general predictive control model using predictive error signal; if the value of time delay exceeds the upper limit, controller nodes will immediately produce the control strategies adopting the revised predictive output, and thus the compensation for time delay between sensor nodes and controller nodes would be accomplished. Simulation experiments are practiced over Ethernet network which embraces both kinds of time delay. It is proved that the scheme of complete compensation remains a good control performance.
Keywords :
backpropagation; compensation; control engineering computing; delays; neural nets; predictive control; queueing theory; actuator node; backpropagation neural network; event-clock-driven controller; general predictive control; networked control system; predictive error signal; queuing strategy; uncertain time delay; Actuators; Clocks; Delay effects; Error correction; Ethernet networks; Networked control systems; Neural networks; Prediction algorithms; Predictive control; Predictive models; BP neural network; General Predictive Control (GPC); Networked Control System (NCS); Queuing Strategy (QS); Time delay;
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370222