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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Otto, E. Maksakov, A. Golovin, I. Palis, S. Otto von Guericke Univ D-39106 Magdeburg Germany IU Int Univ Appl Sci D-99084 Erfurt Germany

ISBN: (纸本)9781713872344

During the operation of large gantry cranes oscillations of flexible structures caused by the trolley movement lead to undesired wear of the construction and deteriorate the efficiency of the crane operation. Therefore, a neural network based adaptive control system for tracking control of the payload position and damping of oscillations is presented and validated in numerical simulations. By comparison to a previously derived nonlinear controller, it is shown that the proposed control law achieves good tracking and damping results with minimal a priori knowledge of the crane dynamics and without knowledge about specific plant parameters, therefore reducing the required modeling effort significantly.

关键词： adaptive and learning systems nonlinear adaptive control data-based control lyapunov methods system identification and adaptive control of distributed parameter systems

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Robust adaptive control for Large-scale Inverter-based Resources with Partial and Complete Loss of Inverters 3

Robust Adaptive Control for Large-scale Inverter-based Resou...

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3rd Modeling, Estimation and control Conference (MECC)

作者： Ameli, Sina Olajube, Ayobami Anubi, Olugbenga Moses Florida State Univ Ctr Adv Power Syst Dept Elect & Comp Engn Tallahassee FL 32306 USA

ISBN: (纸本)9781713872344

This article proposes an approach to address the current and the aggregated active power control challenge for large-scale inverter-based resources subjected to partially or completely loss of inverters and grid voltage variations. To address this problem, a distributed active power mechanism is proposed which generates desired inverters current. Then an adaptive mechanism distributes the voltage control input automatically between inverters in response to the partial or complete loss of inverters. An L-2-gain-based controller is designed for each inverter to track the desired current and rejects the grid voltage disturbance. Simulation results show a significant robust tracking for collective active power and current. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Robust control applications system identification and adaptive control of distributed parameter systems power electronics

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Robust adaptive control for Large-scale Inverter-based Resources with Partial and Complete Loss of Inverters

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IFAC-PapersOnLine 2023年第3期56卷 211-216页

作者： Sina Ameli Ayobami Olajube Olugbenga Moses Anubi Department of Electrical and Computer Engineering the Center for Advanced Power Systems Florida State University Department of Electrical and Computer Engineering the Center for Advanced Power Systems Florida State University USA

This article proposes an approach to address the current and the aggregated active power control challenge for large-scale inverter-based resources subjected to partially or completely loss of inverters and grid voltage variations. To address this problem, a distributed active power mechanism is proposed which generates desired inverters current. Then an adaptive mechanism distributes the voltage control input automatically between inverters in response to the partial or complete loss of inverters. An L 2 -gain-based controller is designed for each inverter to track the desired current and rejects the grid voltage disturbance. Simulation results show a significant robust tracking for collective active power and current.

关键词： Robust control applications system identification and adaptive control of distributed parameter systems power electronics

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Neural network based adaptive control of gantry cranes

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IFAC-PapersOnLine 2023年第2期56卷 8091-8096页

作者： E. Otto A. Maksakov I. Golovin S. Palis Otto von Guericke University Magdeburg 39106 Magdeburg Germany IU International University of Applied Sciences 99084 Erfurt Germany

关键词： adaptive and learning systems nonlinear adaptive control data-based control lyapunov methods system identification and adaptive control of distributed parameter systems

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Robust control for a Class of Nonlinearly Coupled Hierarchical systems with Actuator Faults

Robust Control for a Class of Nonlinearly Coupled Hierarchic...

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Modeling, Estimation and control Conference (MECC)

作者： Ameli, Sina Anubi, Olugbenga Moses Florida State Univ Dept Elect & Comp Engn Tallahassee FL 32306 USA Florida State Univ Dept Elect & Comp Engn Ctr Adv Power Syst Tallahassee FL 32306 USA

This paper proposes an approach to addresses the control challenges posed by a faultinduced uncertainty in both the dynamics and control input effectiveness of a class of hierarchical nonlinear systems in which the high-level dynamics is nonlinearly coupled with a multi-agent low-level dynamics. The high-level dynamics has a multiplicative uncertainty in the control input effectiveness and is subjected to an exogenous disturbance input. On the other hand, the low-level system is subjected to actuator faults causing a time-varying multiplicative uncertainty in the dynamical model and associated control effectiveness. Moreover, the nonlinear coupling between the high-level and the low-level dynamics makes the problem even more challenging. To address this problem, an online parameter estimation algorithm is designed, coupled with an adaptive splitting mechanism which automatically distributes the control action among low level multi-agent systems. A nonlinear L-2-gain-based controller, and then a state-feedback controller are designed in the high-level, and the low-level, respectively, to recover the system from faults with high performance in the transient response, and reject the exogenous disturbance. The resulting analysis guarantees a robust tracking of the high-level reference command signal. Copyright (C) 2021 The Authors.

关键词： Backstepping control of distributed parameter systems Hierarchical multilevel and multilayer control Robust control applications Stability of nonlinear systems system identification and adaptive control of distributed parameter systems

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