Asymptotic properties of distributed and communication stochastic approximation algorithms
SIAM Journal on Control and Optimization
Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
Asynchronous Stochastic Approximations
SIAM Journal on Control and Optimization
Technical communique: Further results on adaptive iterative learning control of robot manipulators
Automatica (Journal of IFAC)
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 22.14 |
The iterative learning control (ILC) is constructed for the discrete-time large scale systems. Each subsystem is affine nonlinear and its observation equation is with noise. Subsystems are nonlinearly connected via the large state vector of the whole system. The possibility of data missing, and communication delay is taken into account. It is proved that ILC given in the paper with probability one converges to the optimal one minimizing the tracking error. The simulation results are consistent with theoretical analysis.