Large-scale systems: modeling, control, and fuzzy logic
Large-scale systems: modeling, control, and fuzzy logic
Direct learning of control efforts for trajectories with different magnitude scales
Automatica (Journal of IFAC)
Analysis of iterative learning control for a class of nonlinear discrete-time systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Iterative learning control: analysis, design, integration and applications
Iterative learning control: analysis, design, integration and applications
Automatic Control Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On iterative learning from different tracking tasks in the presence of time-varying uncertainties
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatica (Journal of IFAC)
Model-based iterative learning control with a quadratic criterion for time-varying linear systems
Automatica (Journal of IFAC)
Brief Iterative learning control for linear discrete time nonminimum phase systems
Automatica (Journal of IFAC)
Robust optimal design and convergence properties analysis of iterative learning control approaches
Automatica (Journal of IFAC)
International Journal of Systems Science
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In the procedure of steady-state hierarchical optimization for large-scale industrial processes, it is often necessary that the control system responds to a sequence of step function-type control decisions with distinct magnitudes. In this paper a set of iterative learning controllers are de-centrally embedded into the procedure of the steady-state optimization. This generates upgraded sequential control signals and thus improves the transient performance of the discrete-time large-scale systems. The convergence of the updating law is derived while the intervention from the distinction of the scales is analysed. Further, an optimal iterative learning control scheme is also deduced by means of a functional derivation. The effectiveness of the proposed scheme and the optimal rule is verified by simulation.