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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Osman, Mostafa Mehrez, Mohamed W. Yang, Shiyi Jeon, Soo Melek, William Univ Waterloo Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada

Motion control of mobile manipulators (a robotic arm mounted on a mobile base) can be challenging for complex tasks such as material and package handling. In this paper, a task-space stabilization controller based on nonlinear model predictive control (NMPC) is designed and implemented to a 10 Degrees of Freedom (DOF) mobile manipulator which consists of a 7-DOF robotic arm and a 3-DOF mobile base. The system model is based on kinematic models where the end-effector orientation is parameterized directly by a rotation matrix. The state and control constraints as well as singularity constraints are explicitly included in the NMPC formulation. The controller is tested using real-time simulations, which demonstrate high positioning accuracy with tractable computational cost. Copyright (C) 2020 The Authors.

关键词： Mobile manipulator task-space control non-linear model predictive control

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control of Dual Stator Induction Generator Based Wind Energy Conversion System 4

Control of Dual Stator Induction Generator Based Wind Energy...

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4th IEEE-Nigeria-Section International Conference on Disruptive Technologies for Sustainable Development (IEEE NIGERCON)

作者： Abdulwahab, Ibrahim Abubakar, Adamu Saidu Olaniyan, Abdulrahman Sadiq, Bashir Olaniyi Faskari, Shehu A. Ahmadu Bello Univ Elect Engn Zaria Nigeria Kampala Int Univ Comp Engn Kampala Uganda Baze Univ Elect Engn Abuja Nigeria

ISBN: (纸本)9781665479783

The work presents a robust control scheme for a Dual Stator Induction Generator (DSIG) based Wind Energy Conversion System (WECS). This developed scheme mitigates deviations in torque and flux of the generator that occurs due to imbalance in the currents of the generator. The models developed comprises of a wind turbine system, a six phase SCIG, two - three phase converters for controlling the generator and a three-phase converter that connects the generator with the grid. A back-to-back voltage source converter was utilized for interfacing the DSIG with the grid. The non-linear model predictive control (NMPC) scheme was used for the control. All simulations were carried out in Matlab 2019b. The result obtained for the electromagnetic torque values obtained showed proper tracking of the reference torque, with an average percentage deviation of 17.11%. These minimal deviations were due to the large variations of the wind speed during these periods. While for the direct rotor flux, it was observed that, the flux settles at time 0.7 seconds which is well within the settling time limit. This showed that the developed scheme has properly decoupled the direct rotor flux from the quadratic rotor flux. For the grid side, the DC link voltage obtained has a settling time of 0.8 seconds, with a minimal overshoot.

关键词： Wind energy conversion system non-linear model predictive control Field Oriented control Dual Stator Induction Generator

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controller design for polymer electrolyte membrane fuel cell systems for automotive applications

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2021年第45期46卷 23263-23278页

作者： Gomez, Juan Carlos Serra, Maria Husar, Attila UPC CSIC Inst Robot & Informat Ind C Llorens & Artigas 4-6 Barcelona 08028 Spain

Continuous developments in Proton Exchange Membrane Fuel Cells (PEMFC) make them a promising technology to achieve zero emissions in multiple applications including mobility. Incremental advancements in fuel cells materials and manufacture processes make them now suitable for commercialization. However, the complex operation of fuel cell systems in automotive applications has some open issues yet. This work develops and compares three different controllers for PEMFC systems in automotive applications. All the controllers have a cascade control structure, where a generator of setpoints sends references to the subsystems controllers with the objective to maximize operational efficiency. To develop the setpoints generators, two techniques are evaluated: off-line optimization and model predictive control (MPC). With the first technique, the optimal setpoints are given by a map, obtained off-line, of the optimal steady state conditions and corresponding setpoints. With the second technique, the setpoints time profiles that maximize the efficiency in an incoming time horizon are continuously computed. The proposed MPC architecture divides the fast and slow dynamics in order to reduce the computational cost. Two different MPC solutions have been implemented to deal with this fast/slow dynamics separation. After the integration of the setpoints generators with the subsystems controllers, the different control systems are tested and compared using a dynamic detailed model of the automotive system in the INN-BALANCE project running under the New European Driving Cycle. (c) 2021 The Authors. Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY-NC-ND license (http:// ***/licenses/by-nc-nd/4.0/).

关键词： Automotive application control structure Efficiency optimization Fuel cell system non-linear model predictive control

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A fast implementation of coalitional model predictive controllers based on machine learning: Application to solar power plants

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 118卷

作者： Masero, Eva Ruiz-Moreno, Sara Frejo, Jose Ramon D. Maestre, Jose M. Camacho, Eduardo F. Univ Seville Dept Ingn Sistemas & Automatica C-Camino Descubrimientos S-N Seville 41092 Spain

This article proposes a real-time implementation of distributed model predictive controllers to maximize the thermal energy generated by parabolic trough collector fields. For this control strategy, we consider that each loop of the solar collector field is individually managed by a controller, which can form coalition with other controllers to attain its local goals while contributing to the overall objective. The formation of coalitions is based on a market-based mechanism in which the heat transfer fluid is traded. To relieve the computational burden online, we propose a learning-based approach that approximates optimization problems so that the controller can be applied in real time. Finally, simulations in a 100 -loop solar collector field are used to assess the coalitional strategy based on neural networks in comparison with the coalitional model predictive control. The results show that the coalitional strategy based on neural networks provides a reduction in computing time of up to 99.74% and a minimal reduction in performance compared to the coalitional model predictive controller used as the baseline.

关键词： Neural networks Artificial intelligence non-linear model predictive control Coalitional control Multi-agent systems Solar thermal applications

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End-Effector Stabilization of a 10-DOF Mobile Manipulator using nonlinear model predictive control

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IFAC-PapersOnLine 2020年第2期53卷 9772-9777页

作者： Mostafa Osman Mohamed W. Mehrez Shiyi Yang Soo Jeon William Melek Mechanical and Mechatronics Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada

关键词： Mobile manipulator task-space control non-linear model predictive control

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Data-Driven modeling and control of Cyclic Variability of an Engine Operating in Low Temperature Combustion Modes

Data-Driven Modeling and Control of Cyclic Variability of an...

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modeling, Estimation and control Conference (MECC)

作者： Batool, Sadaf Naber, Jeffrey D. Shahbakhti, Mahdi Michigan Technol Univ Houghton MI 49931 USA Univ Alberta Edmonton AB Canada

Combustion cyclic variability in an internal combustion engine leads to cyclic variations in the engine torque output and emissions. Combustion cyclic variability is often characterized by coefficient of variation of indicated mean effective pressure (COVIMEP) that is used as an indicator of combustion stability. These cyclic variations are inevitable and cannot be completely eliminated but can be controlled to allow stable engine operation. This work focuses on control oriented modeling of COVIMEP to limit engine cyclic variations in low temperature combustion (LTC) modes. COVIMEP is generally stochastic in nature;thus, a data-driven approach is used to develop a predictive model of COVIMEP for Homogeneous Charge Compression Ignition (HCCI) and Reactivity controlled Compression Ignition (RRCI) modes. This work presents the development of a cycle-by-cycle model predictive controller for a 2.0 liter multi-mode LTC engine. Physics-based control-oriented models for combustion phasing (CA50) and IMEP are augmented with the new data-driven COVIMEP model to limit the cyclic variations below 3% for HCCI and RCCI modes. These models are then used to design closed-loop non-linear model predictive controllers to control CA50 and IMEP while constraining COVIMEP to ensure stable engine operation for varying load conditions. Copyright (C) 2021 The Authors.

关键词： Combustion Stability Machine Learning non-linear model predictive control Homogeneous Charge Compression Ignition (HCCI) Reactivity controlled Compression Ignition (RCCI)

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Real-time techno-economical operation of preserving microgrids via optimal NLMPC considering uncertainties

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Engineering Science and Technology, an International Journal 2024年 57卷

作者： Yaghoubi, Elaheh Yaghoubi, Elnaz Yusupov, Ziyodulla Rahebi, Javad Department of Electrical and Electronics Engineering Karabuk University Karabuk Turkey Department of Software Engineering Istanbul Topkapi University Istanbul Turkey

In the modern era, managing optimal real-time control of microgrids during the operation phase has been a significant challenge, requiring careful consideration of both technical and economic factors. This paper introduces a framework for the real-time control of islanded microgrids using a preserving network. This structure incorporates various distributed generation sources, including rotating and non-rotating resources, along with energy storage systems. The optimization function within model predictive control (MPC) manages essential network parameters, such as frequency and voltage, while addressing real-time economic and technical objectives. To enhance precision and account for uncertainties in generation and consumption parameters, the integration of continuous power flow and the preserving network model is employed. This approach aims to create a model that closely mirrors real-world conditions, ensuring a more accurate representation of microgrid dynamics. The proposed structure demonstrates significant improvements in both technical and economic performance compared to Standard MPC and Adaptive MPC, highlighting its potential for more efficient islanded microgrid management. The proposed framework achieves notable reductions in total voltage deviation of 85.87% and 87.62% compared to Standard MPC and Adaptive MPC, respectively. Additionally, it delivers impressive enhancements in frequency deviation of 99.46% and 96.62% compared to Standard MPC and Adaptive MPC, respectively. Economically, the proposed framework significantly outperforms both, reducing costs by 39.29% compared to Standard MPC and by 28.12% compared to Adaptive MPC. © 2024 THE AUTHORS

关键词： Continuous power flow Network preserving model non-linear model predictive control Real-time Techno-economical control

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A novel Economic nonlinear model predictive controller for power maximisation on wind turbines

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2022年 170卷

作者： Pustina, L. Biral, F. Serafini, J. Roma Tre Univ Dept Engn Rome Italy Univ Trento Dept Mech & Struct Engn Trento Italy

Reducing the Levelized Cost of Energy (LCoE) is one of the main objectives of the wind turbine industry. There are several ways to achieve this goal: reducing construction and installation costs, reducing Operat-ing&Maintenance costs, or increasing the power output. In this work, an Economic nonlinear model predictive control strategy is developed to maximise the power production of wind turbines. A novel three-states, non-linear Reduced Order model is developed to predict aerodynamic power, rotor thrust and generator temperature with suitable accuracy. The control action is obtained from a constrained optimisation problem that uses the developed model, where the objective is the maximisation of the integral of the aerodynamic power. A set of constraints (including a bound on the generator temperature and the rotor thrust) are imposed. First, the turbine model is validated against high-fidelity simulations, then the controller performance and robustness are assessed in the entire wind range of operation, obtaining a significant increase of average power. Apart from the assessment of the controller performance in OpenFAST, the controller robustness is verified, introducing errors in the estimation of incoming wind, up to the case of a complete lack of information. The controller (freely downloadable from a dedicated repository) is effective in all the operating regions without the need for logical switches. Moreover, thanks to the optimised numerical solver adopted, it can be applied to actual wind turbines (which require real-time algorithmic performance).

关键词： Wind turbine control Power maximisation non-linear model predictive control non-linear Reduced Order model

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Improving path-tracking performance of an articulated tractor-trailer system using a non-linear kinematic model

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 196卷 106826-106826页

作者： Murillo, M. Sanchez, G. Deniz, N. Genzelis, L. Giovanini, L. Ciudad Univ UNL Res Inst Signals Syst & Computat Intelligence FICH UNL CONICET 4 Piso FICH RA-3000 Santa Fe S Argentina Univ Nacl Litoral UNL Fac Engn & Water Resources FICH RA-3000 Santa Fe S Argentina

This paper presents a novel non-linear mathematical model of an articulated tractor-trailer system that can be used, in combination with receding horizon techniques, to improve the performance of path tracking tasks of articulated systems. Due to its dual steering mechanisms, this type of vehicle can be very useful in precision agriculture, particularly for seeding, spraying and harvesting in small fields. The articulated tractor-trailer system model was embedded within a non-linear model predictive controller and the trailer position was monitored. When the kinematic of the trailer was considered, the deviation of trailer's position was reduced substantially alongside not only straight paths but also in headland turns. Using the proposed mathematical model, we were able to control the trailer's position itself rather than the tractor's position. The Robot Operating System (ROS) framework and Gazebo simulator were used to perform realistic simulations examples.

关键词： Tractor-trailer system Articulated vehicle Kinematic model non-linear model predictive control

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An optimal charging algorithm to minimise solid electrolyte interface layer in lithium-ion battery

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JOURNAL OF POWER SOURCES 2021年 482卷 228895-228895页

作者： Malik, M. S. S. Li, Guang Chen, Zheng Queen Mary Univ London Sch Engn & Mat Sci London E1 4NS England

This article presents a novel control algorithm for online optimal charging of lithium-ion battery by explicitly incorporating degradation mechanism into control, to reduce the degradation process. The health of battery directly relates to degradation and capacity fade in cycles of charging. We mainly focus on the growth the solid electrolyte interface (SEI) layer, which is the primary source of degradation of batteries. This article addresses the challenge of minimising SEI layer growth during charging by incorporating the first-order layer growth rate model into a non-linear model predictive control approach. A single particle model (SPM) is used for optimal charging using orthogonal projection-based model reformulation. Gauss pseudo-spectral method is used for the optimisation of charging trajectories. Results of the optimal algorithm are compared with the traditional constant current constant voltage (CCCV) approach without considering SEI layer growth. is ensured that overpotential caused by lithium plating remains in a healthy regime which is another feature the proposed strategy. Simulation results are presented to demonstrate the advantages of the proposed charging method.

关键词： Optimal charging non-linear model predictive control Lithium-ion battery Pseudo-spectral methods

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