Title :
A discrete-time learning control algorithm
Author_Institution :
Union Switch & Signal, Pittsburgh, PA, USA
fDate :
29 June-1 July 1994
Abstract :
A discretized version of the D-type learning control algorithm is presented for a MIMO linear discrete-time system. Necessary and sufficient conditions for uniform convergence of the proposed learning algorithm is presented. Then, the author proves that the same condition is sufficient for the global robustness of the proposed learning algorithm to state disturbances, measurement noise at the output and reinitialization error are present at each iteration.
Keywords :
MIMO systems; convergence; discrete time systems; learning systems; linear systems; robust control; D-type learning control algorithm; MIMO linear discrete-time system; discrete-time learning control algorithm; global robustness; measurement noise; necessary and sufficient conditions; reinitialization error; state disturbances; uniform convergence; Control systems; Convergence; Iterative algorithms; Noise measurement; Noise robustness; Nonlinear control systems; Robots; Robust control; Sufficient conditions; Switches;
Conference_Titel :
American Control Conference, 1994
Print_ISBN :
0-7803-1783-1
DOI :
10.1109/ACC.1994.751841
