TY - GEN
T1 - Improving uniform ultimate bounded response of neuroadaptive control approaches using command governors
AU - Magree, Daniel
AU - Yucelen, Tansel
AU - Johnson, Eric
PY - 2013/9/16
Y1 - 2013/9/16
N2 - In this paper, we develop a command governor-based architecture in order to improve the response of neuroadaptive control approaches. Specifically, a command governor is a linear dynamical system that modifies a given desired command to improve transient and steady-state performance of uncertain dynamical systems. It is shown that as the command governor gain is increased, the neuroadaptive system converges to the linear reference system. Simulation results are used to validate the effectiveness of the proposed framework.
AB - In this paper, we develop a command governor-based architecture in order to improve the response of neuroadaptive control approaches. Specifically, a command governor is a linear dynamical system that modifies a given desired command to improve transient and steady-state performance of uncertain dynamical systems. It is shown that as the command governor gain is increased, the neuroadaptive system converges to the linear reference system. Simulation results are used to validate the effectiveness of the proposed framework.
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M3 - Conference contribution
AN - SCOPUS:84883684826
SN - 9781624102240
T3 - AIAA Guidance, Navigation, and Control (GNC) Conference
BT - AIAA Guidance, Navigation, and Control (GNC) Conference
T2 - AIAA Guidance, Navigation, and Control (GNC) Conference
Y2 - 19 August 2013 through 22 August 2013
ER -