TY - GEN
T1 - Retrospective cost adaptive control of the NASA GTM Model
AU - Coffer, Benjamin C.
AU - Hoagg, Jesse B.
AU - Bernstein, Dennis S.
PY - 2010
Y1 - 2010
N2 - A retrospective cost adaptive control algorithm is used to control the NASA Generic Transport Model (GTM) under various operational environments and ight scenarios. In particular, the adaptive control algorithm is used to follow commanded ight trajectories and reject undesired ight disturbances (e.g., wind gusts) under nominal ight scenarios as well as damaged ight scenarios (e.g., limited control surface effectiveness). Retrospective cost adaptive control is effective for multi-input, multi-output systems that are either minimum phase or nonminimum phase. The adaptive control algorithm requires limited model information, specifically, the first nonzero Markov parameter and the nonminimum-phase transmission zeros of the transfer function from the control signal to the performance, which can be estimated from a finite number of Markov parameters. Furthermore, the adaptive control algorithm is effective for stabilization as well as command following and disturbance rejection, where the command and disturbance spectrum are unknown.
AB - A retrospective cost adaptive control algorithm is used to control the NASA Generic Transport Model (GTM) under various operational environments and ight scenarios. In particular, the adaptive control algorithm is used to follow commanded ight trajectories and reject undesired ight disturbances (e.g., wind gusts) under nominal ight scenarios as well as damaged ight scenarios (e.g., limited control surface effectiveness). Retrospective cost adaptive control is effective for multi-input, multi-output systems that are either minimum phase or nonminimum phase. The adaptive control algorithm requires limited model information, specifically, the first nonzero Markov parameter and the nonminimum-phase transmission zeros of the transfer function from the control signal to the performance, which can be estimated from a finite number of Markov parameters. Furthermore, the adaptive control algorithm is effective for stabilization as well as command following and disturbance rejection, where the command and disturbance spectrum are unknown.
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U2 - 10.2514/6.2010-8404
DO - 10.2514/6.2010-8404
M3 - Conference contribution
AN - SCOPUS:84867809483
SN - 9781600869624
T3 - AIAA Guidance, Navigation, and Control Conference
BT - AIAA Guidance, Navigation, and Control Conference
T2 - AIAA Guidance, Navigation, and Control Conference
Y2 - 2 August 2010 through 5 August 2010
ER -