Decentralized iterative learning control for large-scale interconnected nonlinear systems. (Chinese. English summary) Zbl 1313.93004
Summary: The problem of decentralized iterative learning control algorithms for a class of large-scale interconnected nonlinear systems is considered. Here, the considered large-scale interconnected nonlinear systems are assumed to be quite general. Based on P-type learning scheme and D-type learning scheme, decentralized iterative learning control laws are designed for such large-scale interconnected nonlinear systems. The proposed controller of each subsystem only relies on local output variables without any information exchange with other subsystems. By using the contraction mapping principle, it is shown that both schemes mentioned can guarantee that the output tracking error for each subsystem converges along the iteration axis.
MSC:
93A14 | Decentralized systems |
93C10 | Nonlinear systems in control theory |
68T05 | Learning and adaptive systems in artificial intelligence |
93A15 | Large-scale systems |