S.S. Saab (Lebanon)
Iterative learning control; nonlinear systems; stochastic control.
This paper presents a stochastic algorithm that computes the learning gain matrix of a "D-type" iterative learning control (ILC) algorithm for a class of discrete-time varying nonlinear systems with linear input/output actions having relative degree one. The state disturbance, reinitialization errors, and measurement errors are considered to be zero-mean white processes. It is shown that the input error covariance matrix converges to zero in presence of uncorrelated disturbances. This algorithm does not require the knowledge of the state function. The convergence rate of the input error covariance matrix is shown to be inversely proportional to the number of iterations. It is also shown in presence of measurement noise, the state error covariance matrix converges to zero as the number of learning iterations increases. Finally, a numerical example is given, which compares the performance of the proposed algorithm to a conventional ILC algorithm.
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