Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition. (English) Zbl 1283.93329

Summary: In this work we present a discrete-time adaptive iterative learning control (AILC) scheme to deal with systems with time-varying parametric uncertainties. Using the analogy between the discrete-time axis and the iterative learning axis, the new adaptive ILC can incorporate a Recursive Least Squares (RLS) algorithm, hence the learning gain can be tuned iteratively along the learning axis and pointwisely along the time axis. When the initial states are random and the reference trajectory is iteration-varying, the new AILC can achieve the pointwise convergence over a finite time interval asymptotically along the iterative learning axis.


93E35 Stochastic learning and adaptive control
68T05 Learning and adaptive systems in artificial intelligence
93C40 Adaptive control/observation systems
93E24 Least squares and related methods for stochastic control systems
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