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39th IEEE Power Electronics Specialists Conference (PESC 08)

作者： Mariethoz, S. Beccuti, A. G. Morari, M. ETH Automat Control Lab Zurich Switzerland

ISBN: (纸本)9781424416677

The present paper deals with the current control of a voltage source inverter connected to the grid through an LCL filter. It proposes model suitable for analysis and control that accounts for the switching behaviour of the converter. Based on this improved model, a model predictive control scheme is proposed, in order to provide a high bandwidth response, making it very suitable for applications such as active filtering. The control scheme is extended with an observer in order to reduce the number of required sensors and to eliminate noise. The effectiveness of the proposed scheme is demonstrated by *** present paper deals with the current control of a voltage source inverter connected to the grid through an LCL filter. It proposes model suitable for analysis and control that accounts for the switching behaviour of the converter. Based on this improved model, a model predictive control scheme is proposed, in order to provide a high bandwidth response, making it very suitable for applications such as active filtering. The control scheme is extended with an observer in order to reduce the number of required sensors and to eliminate noise. The effectiveness of the proposed scheme is demonstrated by experiments.

关键词： explicit model predictive control MPC Kalman Filter PWM Inverter

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Low-complexity stabilizing PWA controllers for linear systems with parametric uncertainties 21st

Low-complexity stabilizing PWA controllers for linear system...

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

作者： Lu, Liang Kvasnica, Michal Zhejiang Univ Ningbo Res Inst Ningbo Peoples R China Slovak Univ Technol Bratislava Dept Informat Engn & Proc Control Bratislava Slovakia

explicit MPC often results in a large number of irregular partitions in the feasible region as the dimension of the system increases and the storage requirement for the control and region parameters often limit its applications. In this paper, we consider a class of discrete-time linear systems with polytopic parametric uncertainties and provide a robust control Lyapunov based synthesis method to obtain robust low-complexity PWA controllers on regular partitionings. By implementing a refinement procedure, we can fit the PWA feedback control law in each regular partitioning based on feasibility of linear programming problems, which preserves stability, constraint satisfaction, and certain performance requirement. Numerical examples will demonstrate the effectiveness of the approach. Copyright (C) 2020 The Authors.

关键词： explicit model predictive control control Lyapunov function Piecewise affine control Parametric uncertainties Linear programming

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Supervisory model predictive control of building integrated renewable and low carbon energy systems

Supervisory model predictive control of building integrated ...

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作者： Faramarz Sadr Loughborough University

学位级别：博士

To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the 2 CO emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energy system, a unified modelling method is used. With this method, many different building energy systems can be modelled uniformly. Two approaches are used; multi-period optimization and hybrid model predictive control. In both approaches the optimization problem is deterministic, so that at each time step the energy consumption of the building, and the available renewable energy are perfectly predicted for the prediction horizon. The controller is simulated in three different applications. In the first application the controller is used for a system consisting of a micro-combined heat and power system with an auxiliary boiler and a hot water storage tank. In this application the controller reduces the operational cost and 2 CO emission by 7.31 percent and 5.19 percent respectively, with respect to the heat led operation. In the second application the controller is used to control a farm electrification system consisting of PV panels, a diesel generator and a battery bank. In this application the operational cost with respect to the common load following strategy is reduced by 3.8 percent. In the third application the controller is used to control a hybrid off-grid power system consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank and a fuel cell. In this application the controller maximizes the total stored energies in the battery bank and the hydrog

关键词： building energy system integrated renewable energy low carbon energy technology CO2 emission supervisory control optimal control model predictive control multi-period optimization multi-parametric programming explicit model predictive control

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Exploiting symmetries in active set enumeration for constrained linear-quadratic optimal control

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AUTOMATICA 2023年第1期151卷

作者： Mitze, Ruth Kvasnica, Michal Moennigmann, Martin Ruhr Univ Bochum Automat Control & Syst Theory Bochum Germany Slovak Univ Technol Bratislava Inst Informat Engn Automat & Math Fac Chem & Food Technol Bratislava Slovakia

This paper studies symmetric constrained linear-quadratic optimal control problems and their para-metric solutions. The parametric solution of such a problem is a piecewise-affine feedback law that can be equivalently expressed as a set of active sets. We show symmetries of the optimal control problem entail symmetries of the active sets, which can be used to simplify finding the set of active sets considerably. Specifically, we improve a recently proposed method for the dynamic-programming -based enumeration of all active sets. The achieved reduction of the computational effort is illustrated with an example.& COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Constrained LQR explicit model predictive control Combinatorial quadratic programming

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Co-optimization strategy of unmanned hybrid electric tracked vehicle combining eco-driving and simultaneous energy management

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ENERGY 2022年第0期246卷 123309-123309页

作者： Guo, Lingxiong Zhang, Xudong Zou, Yuan Han, Lijin Du, Guodong Guo, Ningyuan Xiang, Changle Beijing Inst Technol Collaborat Innovat Ctr Elect Vehicles Beijing Sch Mech Engn Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China Beijing Inst Technol Sch Mech Engn Natl Key Lab Vehicular Transmiss Beijing 100081 Peoples R China Beijing Inst Technol Adv Technol Res Inst Jinan Jinan 250000 Peoples R China

Combining eco-driving optimization and simultaneous proper energy management, this paper proposes an efficient co-optimization strategy of unmanned hybrid electric tracked vehicles (HETVs) based on a hierarchical control framework to achieve accurate path tracking and optimal energy management simultaneously. Constrained by a pre-known reference path, a deep Q-learning (DQL) algorithm with the AMSGrad optimizer is designed in the upper layer to optimize the velocity of both side tracks to find the best trade-off between energy economy and accurate path tracking. Based on the optimal velocity profile obtained from the upper layer, an explicit model predictive control method is designed in the lower layer to distribute the power between the engine generator and battery in real time to achieve approximate optimal fuel economy. Simulation results verify that the designed DQL method only requires 0.67 s on average for real-time velocity planning, which is markedly lower than the dynamic programming algorithm. In addition, the proposed method also exhibits higher rapidity and optimality for velocity planning than the traditional DQL algorithm. Compared with the model predictive control, dynamic programming and a process without velocity planning, the proposed co-optimization strategy achieves good fuel economy, accurate path tracking and high computational efficiency.(c) 2022 Published by Elsevier Ltd.

关键词： Co-optimization strategy Velocity planning Energy management HET V optimizer explicit model predictive control Deep Q-learning algorithm with AMSGrad

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Simultaneous design & control of a reactive distillation system - A parametric optimization & control approach

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CHEMICAL ENGINEERING SCIENCE 2021年 230卷 116232-116232页

作者： Tian, Yuhe Pappas, Iosif Burnak, Baris Katz, Justin Pistikopoulos, Efstratios N. Texas A&M Univ Artie McFerrin Dept Chem Engn College Stn TX 77843 USA Texas A&M Univ Texas A&M Energy Inst College Stn TX 77843 USA

We present a detailed dynamic model for a reactive distillation system, based on which we develop design-dependent explicit optimal control strategies. A mixed-integer dynamic optimization formulation is then proposed integrating design, control and operational components, the solution of which allows us to derive explicit closed-loop strategies that maintain stable and operable conditions in the presence of process disturbances. The simultaneous approach is illustrated with the design of a methyl tert-butyl ether reactive distillation system example as a part of the developed model library in the RAPID SYNOPSIS project. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Reactive distillation Simultaneous design and control Multi-parametric programming explicit model predictive control Mixed-integer dynamic optimization

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Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization control Framework

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ENERGIES 2021年第24期14卷 8493页

作者： Khalid, Adnan Jaffery, Mujtaba Hussain Javed, Muhammad Yaqoob Yousaf, Adnan Arshad, Jehangir Ur Rehman, Ateeq Haider, Aun Althobaiti, Maha M. Shafiq, Muhammad Hamam, Habib Univ Management & Technol Lahore Dept Elect Engn Sialkot Campus Sialkot 51310 Pakistan COMSATS Univ Islamabad Elect & Comp Engn Dept Lahore 54000 Pakistan Super Univ Dept Elect Engn Lahore 54000 Pakistan Govt Coll Univ Dept Elect Engn Lahore 54000 Pakistan Taif Univ Coll Comp & Informat Technol Dept Comp Sci At Taif 21944 Saudi Arabia Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea Univ Moncton Fac Engn Moncton NB E1A 3E9 Canada Spectrum Knowledge Prod & Skills Dev Sfax 3027 Tunisia Univ Johannesburg Sch Elect Engn Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

It is imperative to find new places other than Earth for the survival of human beings. Mars could be the alternative to Earth in the future for us to live. In this context, many missions have been performed to examine the planet Mars. For such missions, planetary precision landing is a major challenge for the precise landing on Mars. Mars landing consists of different phases (hypersonic entry, parachute descent, terminal descent comprising gravity turn, and powered descent). However, the focus of this work is the powered descent phase of landing. Firstly, the main objective of this study is to minimize the landing error during the powered descend landing phase. The second objective involves constrained optimization in a predictive control framework for landing at non-cooperative sites. Different control algorithms like PID and LQR have been developed for the stated problem;however, the predictive control algorithm with constraint handling's ability has not been explored much. This research discusses the model predictive control algorithm for the powered descent phase of landing. model predictive control (MPC) considers input/output constraints in the calculation of the control law and thus it is very useful for the stated problem as shown in the results. The main novelty of this work is the implementation of explicit MPC, which gives comparatively less computational time than MPC. A comparison is done among MPC variants in terms of feasibility, constraints handling, and computational time. Moreover, other conventional control algorithms like PID and LQR are compared with the proposed predictive algorithm. These control algorithms are implemented on quadrotor UAV (which emulates the dynamics of a planetary lander) to verify the feasibility through simulations in MATLAB.

关键词： Mars landing explicit model predictive control unmanned aerial vehicle (UAV) powered descent

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HIL Evaluation of a Novel Real-time Energy Management System for an HEV with a Continuously Variable Transmission

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2021年第4期67卷 142-152页

作者： Taghavipour, Amir Alipour, Ali KN Toosi Univ Technol Fac Mech Engn Tehran Iran

One of the most important challenges facing automotive engineers is reducing vehicle fuel consumption and improving the drivability index. Modern hybrid electric powertrains play an important role in reducing fuel consumption. Continuously variable transmission (CVT) is an automatic transmission that can change the gear ratio seamlessly using a belt and pulleys. CVT performs with infinite gear ratios. controlling and determining the optimal gear ratio, especially in a complex hybrid powertrain, is a major challenge. Therefore, a multi-parametric model predictive controller with real-time implementation capability is proposed that can handle the energy management task concurrently with gear shifting strategy in a parallel pre-transmission hybrid electric vehicle. The proposed controller hardware-in-the-loop (HIL) validation procedure on the high-fidelity Autonomie model shows significant improvement in fuel economy while maintaining drivability in three different driving schedules. HIL evaluation guarantees the real-time capability and the proposed controller readiness to be implemented to real-world control hardware.

关键词： hybrid electric vehicle continuously variable transmission (CVT) Autonomie energy management system explicit model predictive control hardware in the loop

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Data-driven modeling and stability control for industrial zinc roaster and its edge controller implementation

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control ENGINEERING PRACTICE 2023年第1期137卷

作者： Ying, Xinyu Wu, Dehao Huang, Keke Yang, Chunhua Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China

As large-scale industrial equipment, the operation stability control of the zinc roaster can improve the roasting efficiency and ensure industrial safety. In the industrial field, the roaster temperature is generally controlled manually, but automatic control is an important way to obtain real-time, accuracy, and safety of operation performance. However, existing control methods highly depend on manual experience and knowledge, which decreases their robustness and accuracy. In addition, the complex control algorithm can hardly meet the real-time requirement on the premise of pursuing accuracy. In order to address these puzzles, a data-driven explicit model predictive control solution is proposed in this paper. Specifically, the canonical correlation analysis method is introduced to extract the key controllable variables from the complex and coupled observer variables of the industrial field roaster. Then, the subspace identification method is proposed to obtain the system model with a balance between model complexity and fitting accuracy. Finally, an explicit model predictive control is introduced to simplify the calculation process of online control and simultaneously ensure the real-time stability control of the roaster. The hardware platform deployment experiment is presented to verify the feasibility and real-time performance of the proposed control scheme.

关键词： Zinc roasting process Operation control Data-driven explicit model predictive control Hardware platform

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Fixed complexity solution of partial explicit MPC

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COMPUTERS & CHEMICAL ENGINEERING 2022年 157卷 107606-107606页

作者： Galcikova, Lenka Oravec, Juraj Slovak Univ Technol Bratislava Fac Chem & Food Technol Inst Informat Engn Automat & Math Radlinskeho 9 SK-81237 Bratislava Slovakia

Solving large-scale optimization problems with numerous constraints and optimization variables is a challenging task. Partial explicit MPC enables solving the large-scale optimization problem efficiently. This paper pushes the idea of partial explicit MPC to the fixed complexity parametric solution. The idea is to replace the polytopic critical regions that have a variable number of half-spaces with the maximal volume inner approximation based on the Chebyshev balls. As the approximation has a fixed and known structure, the memory footprint of the parametric solution is also fixed and known in advance, without the necessity to solve the large-scale optimization problem. This valuable property enables scaling the solution size a priori to meet the requirements of the hardware, where the MPC controller will be installed. The proposed method also dramatically reduced the memory burden of the partial explicit solution. Moreover, the proposed method improves the accuracy of the initialization of the hot-start procedure. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Parametric optimization Fixed complexity solution explicit model predictive control

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