Title :
Discrete-time learning control algorithm for a class of nonlinear systems
Author_Institution :
Union Switch & Signal, Pittsburgh, PA, USA
Abstract :
Applies a discrete-time learning algorithm to a class of discrete-time varying nonlinear system. The author investigates the robustness of the algorithm to state disturbance, measurement noise and reinitialization errors. Then, the author proves that the input and the state variables will always be bounded if certain conditions are met. Moreover, the author shows that the input error and state error will converge uniformly to zero in absence of all disturbances. A numerical example is added to illustrate the results
Keywords :
convergence; discrete time systems; learning systems; nonlinear control systems; robust control; discrete-time learning control algorithm; input error; measurement noise; nonlinear systems; reinitialization errors; robustness; state disturbance; state error; uniform convergence; Control systems; Convergence; Mechanical systems; Noise measurement; Noise robustness; Nonlinear control systems; Nonlinear systems; Robots; Switches; Time varying systems;
Conference_Titel :
American Control Conference, Proceedings of the 1995
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2445-5
DOI :
10.1109/ACC.1995.532347
