Data-driven terminal iterative learning control with high-order learning law for a class of non-linear discrete-time multiple-input–multiple output systems

PROJECT TITLE :

Data-driven terminal iterative learning control with high-order learning law for a class of non-linear discrete-time multiple-input–multiple output systems

ABSTRACT:

During this study, a completely unique data-driven terminal iterative learning management with high-order learning law is proposed for a category of non-linear non-affine discrete-time multiple-input-multiple output systems, where only the system state or output at the endpoint is measurable and also the control input is time-varying. A brand new data-driven dynamical linearisation is proposed within the iteration domain and the linearisation data model will be updated by a designed parameter updating law iteratively. The high-order learning control law makes it possible to utilise more management data of previous runs to enhance control performance. The planning and analysis of the proposed approach only depends on the I/O knowledge of the control plant while not requiring any express model information. Each theoretical analysis and extensive simulations are provided to verify the effectiveness and applicability of this novel approach.

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