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13th Global Conference on Sustainable Manufacturing - Decoupling Growth from Resource Use

作者： Albalasie, Ahmad Seliger, Guenther Abu Hanieh, Ahmed Tech Univ Berlin Pascalstr 8-9 D-10587 Berlin Germany Birzeit Univ Birzeit Palestine

This paper presents an adaptive model predictive control scheme to control the underactuated and redundant robot, the robot has highly nonlinear coupling because of the existence of a passive axis. adaptive model predictive control provides a framework to solve optimal discrete control problem for a nonlinear system under input saturation and state constraints. The optimal reference trajectory is computed by using Quasi-linearization (QL) approach to minimize the energy consumption for underactuated motion between two points. The challenge is to meet the performance requirements e.g. position accuracy, repeatability, and precision, combined with high speed capability. Numerical simulations are conducted to validate the control scheme. Simulation results show very good comparison and prove the adequateness of this control technique for underactuated industrial robots. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of International Scientific Committee of the 13th Global Conference on Sustainable Manufacturing

关键词： adaptive model predictive control underactuated motion industrial robot trajectory tracki

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Using adaptive model predictive Technique to control Underactuated Robot and Minimize Energy Consumption

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Procedia CIRP 2016年 40卷 407-412页

作者： Ahmad Albalasie Guenther Seliger Ahmed Abu Hanieh Technical University of Berlin Pascalstr. 8-9 Berlin 10587 Germany Birzeit University Birzeit Palestine

关键词： adaptive model predictive control underactuated motion industrial robot trajectory tracki

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adaptive Output-Feedback model predictive control of Hammerstein Systems with Unknown Linear Dynamics

Adaptive Output-Feedback Model Predictive Control of Hammers...

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American control Conference (ACC)

作者： Kamaldar, Mohammadreza Bernstein, Dennis S. Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA

ISBN: (纸本)9798350382662;9798350382655

This paper considers model predictive control of Hammerstein systems, where the linear dynamics are a priori unknown and the input nonlinearity is known. predictive cost adaptive control (PCAC) is applied to this system using recursive least squares for online, closed-loop system identification with optimization over a receding horizon performed by quadratic programming (QP). In order to account for the input nonlinearity, the input matrix is defined to be control dependent, and the optimization is performed iteratively. This technique is applied to output stabilization of a chain of integrators with unknown dynamics under control saturation and deadzone input nonlinearity.

关键词： adaptive model predictive control Hammerstein system input nonlinearity unknown system

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A novel adaptive model predictive controller for load frequency control of power systems integrated with DFIG wind turbines

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NEURAL COMPUTING & APPLICATIONS 2020年第11期32卷 7171-7181页

作者： Mohamed, Mohamed A. Diab, Ahmed A. Zaki Rezk, Hegazy Jin, Tao Fuzhou Univ Dept Elect Engn Fuzhou 350116 Peoples R China Menia Univ Fac Engn Elect Engn Dept Al Minya 61519 Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia

With the rapid growth of renewable energy resources, wind energy system is getting more interest everywhere throughout the world. However, its extensive use in power systems prompts many power system dynamics and stability problems. Load variation and anomalous operating conditions prompt inconsistencies in frequency and planned power trades. These inconsistencies should be remedied by load frequency control. This paper introduces a novel frequency control system utilizing a mix of adaptive model predictive controller (AMPC) and recursive polynomial model estimator (RPME) integrated with double fed induction generator wind turbines. Inside each control duration, the RPME is identifying a discrete-time online autoregressive exogenous model. The latter is used through the AMPC to update the interior plant model in order to achieve a successful nonlinear control. The performance of the proposed system has been verified and contrasted with the conventional MPC system through a computer simulation-based MATLAB/SIMULINK. The simulation results demonstrated the superiority of the proposed system as for the conventional MPC system.

关键词： adaptive model predictive control Load frequency control Power system inertia Online model estimation Wind turbines Power system control

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Efficient control of a nonlinear solid oxide fuel cell system using an adaptive model predictive controller

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第11期31卷

作者： Gupta, Preeti Pahwa, Vivek Verma, Yajvender Pal Panjab Univ Univ Inst Engn & Technol Chandigarh India

Over the years, the solid oxide fuel cell (SOFC) is growing commercially due to its high fuel flexibility, lesser maintenance requirement, and environmental friendliness feature. However, its control is challenging due to its nonlinear behavior and simultaneous management of its operational constraints. Therefore, in this work, an adaptive model predictive control is designed systematically to handle the SOFC's behavior while optimizing its operational constraints such as fuel input, utilization factor, and change in pressure of hydrogen and oxygen. This model-oriented control linearizes, augments, and discretizes the SOFC during run time while estimating the states using a time-varying Kalman filter. Finally, it optimizes the control problem for predicting the internal states of the SOFC using successive linearization. The simulation results show that the proposed controller gives better performance of the SOFC in linear region, and significantly improved performance in nonlinear region in comparison to the conventional model predictive controller. The validation of the work has been done through real-time simulation using OPAL-RT's hardware setup. Using such control is beneficial as it enhances the efficiency of the SOFC significantly. Thus, this advanced controller is most suited for these kinds of nonlinear systems due to its superior performance.

关键词： adaptive model predictive control dynamic model fuel utilization factor linearly time-invariant multiple-input multiple-output model predictive control nonlinear system solid oxide fuel cell time-variant Kalman filter

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adaptive model predictive controller for power regulation and load reduction in large wind turbines

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2025年

作者： Wen, Haoyuan Liang, Dongyang Song, Ziqiu Liu, Yajuan North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

Pitch control is related to the efficient energy conversion and safe operation of wind turbine, which is a challenging control issue because of the highly nonlinear dynamics of wind turbine, system constraints and stochastic environmental disturbances, etc. In this article, we propose the adaptive model predictive pitch control strategy based on linear parameter varying (LPV) model to solve the pitch control issue of large-scale wind turbine. Firstly, a high-fidelity LPV model is constructed to approximate the high-nonlinearity dynamics of wind turbine. The gap metric theory is introduced to optimize the selection of local models. Based on the proposed LPV model, an adaptive model predictive controller with linear time-varying Kalman filter is designed for large-scale wind turbine. The benchmark 5 megawatt (MW) wind turbine model provided by FAST is employed to evaluate the controller performance. The results show that, compared with the traditional gain scheduling proportional-integral control strategy, the proposed control strategy can achieve better power regulation and load reduction performance under gust, step and turbulent wind.

关键词： Wind turbine pitch control linear parameter varying aerodynamic derivatives adaptive model predictive control

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Design of a self-tuning adaptive model predictive controller using recursive model parameter estimation for real-time plasma variable control

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COMPUTERS & CHEMICAL ENGINEERING 2019年 123卷 126-142页

作者： Koo, Junmo Park, Damdae Ryu, Sangwon Kim, Gon-Ho Lee, Youn-Woo Seoul Natl Univ Inst Chem Proc Sch Chem & Biol Engn 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Dept Energy Syst Engn 1 Gwanak Ro Seoul 08826 South Korea

The semiconductor etching process, which is the most important part of the semiconductor manufacturing process, requires higher sophistication as 10 nm semiconductors are mass produced. Etching methods utilizing plasma are getting increasingly popular with the miniaturization of the etching process. As the process performance depends on the state of the plasma variables, such as electron density, it is essential to measure and control these variables in real time. Moreover, to control the plasma-based system, the sensitive and time-varying characteristics of plasma should be considered. This paper proposes a self-tuning adaptive model predictive controller that can effectively perform electron density control. As a first step, an integral squared error-based Bayesian optimization is used to tune the model predictive controller, and its performance is verified on a drift-free Ar plasma system. The self-tuning adaptive model predictive controller is constructed by combining a recursive model parameter estimator with a model predictive controller. The recursive model parameter estimator is designed using a recursive least squares algorithm with Kalman filter interpretation. The effectiveness of the proposed controller is verified through control simulations and a set-point tracking experiment on the electron density with artificial drift in real time. The experimental results show that the performance of the proposed controller is 21% better than that of the conventional model predictive controller. We expect that this result will make a significant contribution to control processes utilizing plasma. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Plasma variable control Real-time control adaptive model predictive control Self-tuning adaptive control Recursive model parameter estimation

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adaptive model predictive control of Uncertain Constrained Systems

Adaptive Model Predictive Control of Uncertain Constrained S...

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American control Conference

作者： Xiaofeng Wang Yu Sun Kun Deng Department of Electrical Engineering at the University of South Carolina Columbia SC 29208 University of Illinois at Urbana-Champaign Urbana 61801

ISBN: (纸本)9781479932757

This paper studies adaptive model predictive control (AMPC) of systems with time-varying and state-dependent uncertainties. We propose an estimation and prediction architecture within the min-max MPC framework. An adaptive estimator is presented to estimate the set-valued measure of the uncertainty using piecewise constant adaptive law. We show that this measure can be arbitrarily accurate if the sampling period in adaptation is small enough. Based on this measure, a prediction scheme is provided that predicts the time-varying feasible set of the uncertainty over the prediction horizon. The results indicate that the proposed approach can efficiently reduce the size of the feasible set for the uncertainty in min-max MPC setting, and therefore improve the control performance. Simulations verify the theoretical results.

关键词： AMPC adaptive model predictive control Uncertain Constrained Systems adaptive model Uncertainty predictive control measure of uncertainty sampling interval Maximum Allowable Concentration time varying

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Engine adaptive Fuel Injection control Using model predictive Self-Tuning Regulator with adaptive Variable Functioning

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SAE INTERNATIONAL JOURNAL OF ENGINES 2021年第5期14卷 671-682页

作者： Shamekhi, Amir-Mohammad Shamekhi, Amir H. KN Toosi Univ Technol Dept Mech Engn Tehran Iran KN Toosi Univ Technol Mech Engn Tehran Iran

In this article, a model predictive self-tuning regulator (MP-STR) with adaptive variable functioning (AVF) is designed for fuel injection feedback control. Proper control of wall-wetting effects in transients is a matter of great challenge in port fuel injection spark-ignition (SI) engines. A self-tuning regulator is employed to address both parametric and unstructured uncertainties, but in a predictive practice, in order to achieve optimal behavior, particularly in transient phases. Additionally, it is applied with AVF to maintain the control system work in real time, that is, at low speeds with longer computation time available, the MP-STR is wholly applied, yet at higher speeds with lower computation time in hand, a lighter controller, for instance, an offline adaptive controller including the predictive compensator with offline parameters, coupled with a simple proportional integral (PI) controller, is employed. As will be seen, the control system is fully capable of maintaining the output close to the desired value, much more preferable than that of the conventional controller.

关键词： Engine fuel injection control adaptive model predictive control Self-tuning regulator SI engines Wall-wetting adaptive variable functioning

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adaptive look-ahead model predictive control strategy of vehicular PEMFC thermal management

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ENERGY 2023年 285卷

作者： Liu, Zhaoming Chang, Guofeng Yuan, Hao Tang, Wei Xie, Jiaping Wei, Xuezhe Dai, Haifeng Tongji Univ Sch Automot Studies Shanghai Peoples R China Tongji Univ Clean Energy Automot Engn Ctr Shanghai Peoples R China Haidriver Energy Technol Co Ltd Qingdao Peoples R China

This study presents an improved model predictive control (MPC) strategy for the thermal management of vehicular fuel cells, with the incorporation of model adaptation, the utilization of look-ahead information, and temperature trajectory planning. To describe the thermal dynamics of the fuel cell cooling system, a threevolume model of a 60 kW fuel cell system is proposed and validated with dynamic load experiments. Additionally, a fuel cell temperature sensitivity experiment is conducted to identify a comfort temperature reference. The predictive models developed to cover the entire range of power levels are based on the integration of lookahead power information and weigh adjustment of actuators. Results from the analysis of look-ahead times suggest that a 4-s preview is suitable for mitigating fuel cell dry and flooding risks. Dynamic control results demonstrate a 22%-60% reduction in temperature tracking error compared to MPC-H, MPC-M, MPC-L and MPID, while energy-saving abilities are increased by 50% relative to MPID, contributing to improve the state balance and economy for vehicular fuel cells.

关键词： Fuel cell system Three-volume thermal model Temperature sensitivity experiment adaptive model predictive control Condition variation look-ahead

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