In this study, a novel data-driven terminal iterative learning control with high-order learning law is proposed for a class 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 the control input is time-varying. A new data-driven dynamical linearisation is proposed in the iteration domain and the linearisation data model can be updated by a designed parameter updating law iteratively. The high-order learning control law makes it possible to utilise more control knowledge of previous runs to improve control performance. The design and analysis of the proposed approach only depends on the I/O data of the control plant without requiring any explicit model information. Both theoretical analysis and extensive simulations are provided to confirm the effectiveness and applicability of this novel approach.
This study proposes data-driven model-free adaptive control (MFAC), model-free control (MFC) and virtual reference feedback tuning (VRFT) techniques applied to the control of a representative non-linear multi-input-multi-output (MIMO) system represented by the twin rotor aerodynamic system (TRAS). These data-driven techniques are implemented for both a single MIMO controller and two separately designed single-input-single-output controllers running in parallel for azimuth and pitch control. The three techniques are implemented as MFAC and MFC algorithms and as linear controllers tuned by VRFT. The performance of the three data-driven MIMO control system structures is compared systematically on the basis of the experimental results in terms of the values of the sum of mean squared control errors measured on TRAS equipment.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.