TY - GEN
T1 - Design of a robust neuro-controller for complex dynamic systems
AU - Song, Ki Young
AU - Gupta, Madan M.
AU - Jena, Debashisha
AU - Subudhi, Bidyadhar
PY - 2009
Y1 - 2009
N2 - Design of neuro-controller for complex dynamic systems is a big challenge faced by the researchers. In this paper we present a design of a robust neuro-controller for a dynamic system to make the system response fast with no overshoot. Here the control action decided by the controller completely depends on the value of the error at that point of time. The position feedback which controls the bandwidth of the system as well as the dynamic response is a function of the system error. For large error the position feedback is made large increasing the bandwidth of the system, and for small errors the position feedback value is small. Thus, during the dynamic response of the system the bandwidth of the system is controlled by the system error. Similarly, the velocity feedback which controls the damping in the system is kept very small for large errors, and large for small errors. Thus, in the proposed neuro-controller the position feedback Kp(e,t), and velocity feedback Kv(e,t), are made as a function of error which yields a very fast response with no or very little overshoot.
AB - Design of neuro-controller for complex dynamic systems is a big challenge faced by the researchers. In this paper we present a design of a robust neuro-controller for a dynamic system to make the system response fast with no overshoot. Here the control action decided by the controller completely depends on the value of the error at that point of time. The position feedback which controls the bandwidth of the system as well as the dynamic response is a function of the system error. For large error the position feedback is made large increasing the bandwidth of the system, and for small errors the position feedback value is small. Thus, during the dynamic response of the system the bandwidth of the system is controlled by the system error. Similarly, the velocity feedback which controls the damping in the system is kept very small for large errors, and large for small errors. Thus, in the proposed neuro-controller the position feedback Kp(e,t), and velocity feedback Kv(e,t), are made as a function of error which yields a very fast response with no or very little overshoot.
KW - Neural networks
KW - Position feedback
KW - Robust neuro-controller
KW - Velocity feedback
UR - http://www.scopus.com/inward/record.url?scp=70350432743&partnerID=8YFLogxK
U2 - 10.1109/NAFIPS.2009.5156405
DO - 10.1109/NAFIPS.2009.5156405
M3 - Conference Proceeding
AN - SCOPUS:70350432743
SN - 9781424445776
T3 - Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
BT - NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society
T2 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2009
Y2 - 14 June 2009 through 17 June 2009
ER -