A discrete filled function method is developed in this paper to solve discrete global optimization problems over "strictly pathwise connected domains." Theoretical properties of the proposed discrete filled function are investigated and a solution algorithm is proposed. Numerical experiments reported in this paper on several test problems with up to 200 variables have demonstrated the applicability and efficiency of the proposed method.
Modeling a launch vehicle dynamics accurately is time-consuming since its dynamics is very complex with high nonlinearity, when considering the influence of load variation and other related factors. Model-free adaptive control (MFAC), as a data-driven control method, has been widely used because of its simple controller structure, low-computational burden, and easy implementation. In this paper, a data-driven attitude improved model-free adaptive control (iMFAC) is first applied for a launch vehicle. First, a controller is designed for the launch vehicle by utilizing the MFAC. Then, the initial values of the pseudo gradient (PG) and the reset values of the PG in the designed controller are optimized under the virtual reference feedback tuning (VRFT) framework through the equivalent relationship between the MFAC and the VRFT in controller structure. Finally, the effectiveness and robustness of the applied iMFAC are verified through qualitative and quantitative analysis compared to the MFAC and PID. INDEX TERMS Attitude control, launch vehicle, model-free adaptive control, virtual reference feedback tuning.
This article considers the tracking control of unknown nonlinear nonaffine repetitive discrete-time multi-input multi-output systems. Two data-driven iterative learning control (ILC) schemes are designed based on two equivalent dynamic linearization data models of an unknown ideal learning controller, which exists theoretically in the iteration domain. The two control schemes provide ways of selecting learning controllers based on the complexity of the controlled nonlinear systems. The learning control gain matrixes of the two learning controllers are optimized through the steepest descent method using only the measured input-output data of the nonlinear systems. The proposed ILC approaches are pure data-driven since no model information of the controlled systems is involved. The stability and convergence of the proposed ILC approaches are rigorously analyzed under reasonable conditions. Numerical simulation and an experiment based on a Gantry-type linear motor drive system are conducted to verify the effectiveness of the proposed datadriven ILC approaches.
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