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A dual-stage optimal iterative learning control for nonlinear non-affine discrete-time systems. (Chinese. English summary) Zbl 1164.93355

Summary: On the basis of a new dynamic linearization technology along the iteration axis, a dual-stage optimal iterative learning control is presented for nonlinear and non-affine discrete-time systems. The dual-stage indicates that two optimal learning stages are designed respectively to improve control input sequence and the learning gain iteratively. The main feature is that the controller design and convergence analysis only depend on the I/O data of the dynamical system. In other words, we can easily select the control parameters without knowing any other knowledge of the system. Simulation study illustrates the geometrical convergence of the presented method along the iteration axis, in which an example of freeway traffic iterative learning control is noteworthy for its intrinsic engineering importance.

MSC:

93C40 Adaptive control/observation systems
93C55 Discrete-time control/observation systems
93B18 Linearizations
68T05 Learning and adaptive systems in artificial intelligence
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