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Predictive Control Model Based on the mpc algorithm on Three Different Road Sections

Journal of Electronic Research and Application 2024年第2期8卷 116-123页

作者： Xiaoxi Liu Xixi Zheng Shuming Zhang Zhidong Wang School of Civil Engineering and Transportation Northeast Forestry UniversityHarbin 150040Heilongjiang ProvinceChina

Predictive control is an advanced control algorithm,which is widely used in industrial process *** them,model predictive control(mpc)is an important branch of predictive *** basic principle is to use the system model to predict future behavior and determine the current control action by optimizing the objective *** paper discusses the application of mpc in the prediction and control of the speed of vehicles to optimize traffic *** is a valuable reference for alleviating traffic congestion and improving travel efficiency and smoothness and provides scientific basis and technical support for future highway traffic management.

关键词： mpc algorithm Variable speed limit control Highway

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Interaction of open and closed loop control in mpc

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AUTOMATICA 2017年 82卷 243-250页

作者： Worthmann, Karl Mehrez, Mohamed W. Mann, George K. I. Gosine, Raymond G. Pannek, Juergen Tech Univ Ilmenau Inst Math Ilmenau Germany Mem Univ Newfoundland Intelligent Syst Lab St John NF Canada Univ Bremen Dept Prod Engn Bremen Germany BIBA GmbH Bremen Germany

We present a novel mpc algorithm without terminal constraints and/or costs, for which the prediction horizon is reduced in comparison to standard mpc while asymptotic stability and inherent robustness properties are maintained. To this end, we derive simplified stability conditions and investigate the interplay of open and closed loop control in mpc. The insight gained from this analysis allows to close the control loop more often while keeping the computational complexity of basic mpc. Our findings are verified by numerical simulations. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Model predictive control Asymptotic stability Stability criterion mpc algorithm Closed loop performance

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Autonomous vehicle controlled by safety path planner with collision risk estimation coupled with a non-linear mpc 24th

Autonomous vehicle controlled by safety path planner with co...

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24th Symposium of the International-Association-for-Vehicle-System-Dynamics (IAVSD)

作者： Arrigoni, S. Cheli, F. Manazza, S. S. Gottardis, P. Happee, R. Arat, M. Kotiadis, D. Politecn Milan Dept Mech Engn Milan Italy Delft Univ Technol Dept Mech Engn Delft Netherlands

ISBN: (纸本)9781498777025;9781138028852

Research efforts in both academia and industry connected with the development and implementation of autonomous driving systems are increasing exponentially in last years. The project designed and presented is part of the so called Dutch Automated Vehicle Initiative (DAVI) which aims to investigate, improve and demonstrate automated driving on public roads. The work presented focuses on the path planning and following in highway environment, presenting a novel approach. A modular solution was designed, where the evaluation of risk is implemented in a path planner that select the optimal trajectory among a discrete set of feasible ones in term of comfort. The tracking is performed through a non linear model predictive controller (Nmpc), that also consider collision-risk assessment information. In order to test critical situations that can commonly occur, several scenarios were designed and tested through extensive numerical simulations. Simulation results demonstrate that the proposed system is capable of dealing with complex driving situation, maintaining a human-like behavior.

关键词： Safety path planner collision risk estimation autonomous vehicle obstacle avoidance mpc algorithm

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Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties

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JOURNAL OF PROCESS CONTROL 2019年 74卷 2-12页

作者： Manaf, Norhuda Abdul Qadir, Abdul Abbas, Ali Univ Sydney Sch Chem & Biomol Engn Sydney NSW Australia Univ Teknol Malaysia MJIIT Dept Chem Proc Engn SHIZEN Convers & Separat Technol iKohza Kuala Lumpur Malaysia

This paper employs a control-based optimization algorithm encompassing of an intelligence model predictive control (mpc) scheme and mixed integer non-linear programming (MINLP) for coal-fired power plant retrofitted with flexible solvent-based post combustion CO2 capture (PCC) plant (integrated plant). The agility and robustness of the developed control algorithm (mpc) is demonstrated through the control response time and efficiency of energy requirement including the financial and operational benefits of the plant subjected to plant and market uncertainties. While, the MINLP is utilized to forecast plant operational modes by ensuring the operational fidelity of integrated plant. This involves utilization of historical (2011) and forecast (2020) market conditions (electricity tariff and carbon price) subject to maximum plant net operating revenue. The outcomes show that the future power plant will operate in mixed operation modes, for instance in unit turndown and load following modes, which contribute to a minimum capture energy penalty at 3.13 MJ(th)/tonne CO2. Moreover, under the same year (2020), mpc exhibits superior control performance by satisfactorily obtain 94% actual CO(2 )capture from the ideal cumulative CO2 capture. Additionally, the integrated plant is capable to resume approximately 96% actual revenue from the ideal net operating revenue projected by the control-based optimization algorithm. The algorithm demonstrates that the installation of control system package (mpc) into the flexible PCC plant associated with coal-power generator could contribute to efficient energy management subjects to unprecedented uncertainties. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Control system package mpc algorithm Operational fidelity Efficient energy management Real-time uncertainties Energy security

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Voltage and frequency control during microgrid islanding in a multi-area multi-microgrid system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第6期11卷 1502-1512页

作者： John, Thomas Lam, So Ping Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore

This study presents a control method to regulate load voltage and system frequency during microgrid is landing in a multi-area multi-microgrid (MMG) system. In the event of islanding of a microgrid from the distribution grid in the proposed MMG system, load voltage of the islanded microgrid and system frequency are affected. To overcome these problems, a control system for the MMG system is proposed. The proposed control system facilitates desired power exchange between grid-connected and islanded microgrids, and achieves effective voltage and frequency regulation in the MMG system. The main significance of the proposed MMG system is that multiple microgrids in different locations can be interconnected to meet larger bulk power demands. This ensures improved reliability and security of power supply in the MMG system. An improved model predictive control (mpc) algorithm is used to regulate various parameters such as output voltage, frequency and power of the inverters in the MMG system. The proposed MMG system is tested during islanding and load shedding using simulation studies. The simulation studies show that the inverters can operate effectively using mpc to provide the desired voltage, frequency and power during islanding and load shedding.

关键词： frequency control voltage control distributed power generation power generation control predictive control load shedding power generation reliability power system security frequency control voltage control microgrid islanding multiarea MMG system load voltage regulation system frequency regulation multiarea multimicrogrid system distribution grid grid-connected microgrids reliability power supply security model predictive control algorithm mpc algorithm load shedding power regulation

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Model predictive control with improved discrete space vector modulation for three-level Vienna rectifier

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IET POWER ELECTRONICS 2019年第8期12卷 1998-2004页

作者： Zhu, Wenjie Chen, Changsong Duan, Shanxu Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Luoyu Rd 1037 Wuhan Hubei Peoples R China

This article proposes a model predictive control (mpc) with the discrete space vector modulation (DSVM) to reduce input current ripple of three-level Vienna rectifier. The finite control set mpc (FCS-mpc) applied in power converters has variable switching frequency and needs high sampling frequency for better input current quality. With the proposed approach, more feasible vectors can be used to improve the input current's performance with the DSVM using the virtual vectors. The DSVM-mpc features fixed switching frequency and should implement large numbers of virtual vectors with the calculation burden. The improved DSVM-mpc is proposed to optimise the mpc algorithm by selecting the adjacent feasible vectors according to the reference voltage vector trajectory. The effectiveness of the proposed method is verified by the experimental results in a Vienna rectifier platform, which shows that the proposed method possesses better input current performance compared with the FCS-mpc method.

关键词： PWM rectifiers power convertors machine control rectifiers torque control predictive control PWM power convertors virtual vectors improved DSVM-mpc mpc algorithm adjacent feasible vectors reference voltage vector trajectory Vienna rectifier platform input current performance FCS-mpc method model predictive control improved discrete space vector modulation three-level Vienna rectifier input current ripple finite control set mpc power converters variable switching frequency high sampling frequency input current quality DSVM-mpc features

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Simplified model predictive control of a three-phase T-type NPC inverter

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IET POWER ELECTRONICS 2019年第8期12卷 1917-1930页

作者： Kumar, Eedara Aswani Rao, Rayapudi Srinivasa Sekhar, Koritala Chandra Sasi Inst Technol & Engn Dept Elect & Elect Engn Tadepalligudem Andhra Pradesh India Jawaharlal Nehru Technol Univ Kakinada Dept Elect & Elect Engn Kakinada Andhra Pradesh India RVR & JC Coll Engn Dept Elect & Elect Engn Guntur Andhra Pradesh India

This study presents a simplified model predictive control (Smpc) strategy for three-phase T-type neutral-point-clamped (NPC) inverters to reduce the computational effort while achieving the current control, capacitor voltage equalisation and common-mode voltage (CMV) minimisation targets. The approach involves three stages. In the first stage, the number of switching states is reduced from 27 to 19 for reducing the peak CMV. In the second stage, a graphical approach is proposed to identify candidate control set (CCS) based on the present switching state, which helps in reducing the number of computations further. The number of elements in CCS may be 7, 10, 13 or 19. A look-up table with present applied state and its corresponding CCS is formed based on this graphical approach for all the 19 switching states. The third stage involves modifications to the mpc algorithm. Two versions of Smpc schemes (Smpc-I and Smpc-II) are proposed. Smpc-I can restrict the CMV to one-sixth of DC input voltage. Smpc-II can achieve near zero CMV. The performance of the Smpc strategy is analysed with parameter uncertainty. Computational effort of Smpc schemes is compared with other mpc methods. The feasibility of Smpc algorithm is verified experimentally on T-type NPC inverter prototype.

关键词： voltage control invertors predictive control electric current control mpc algorithm CCS three-phase T-type neutral-point-clamped inverters common-mode voltage minimisation targets candidate control set graphical approach peak CMV capacitor voltage equalisation current control simplified model predictive control strategy three-phase T-type NPC inverter Smpc algorithm DC input voltage Smpc-I Smpc-II

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Robust model predictive control based on recurrent multi-dimensional Taylor network for discrete-time non-linear time-delay systems

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IET CONTROL THEORY AND APPLICATIONS 2020年第13期14卷 1806-1818页

作者： Duan, Zheng-Yi Yan, Hong-Sen Zheng, Xiao-Yi Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Southeast Univ Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 210096 Jiangsu Peoples R China

This study is concerned with the robust model predictive control (mpc) based on recurrent multi-dimensional Taylor network (RMTN) for the discrete-time non-linear time-delay systems. Regarding the mpc algorithm for the discrete-time time-delay systems, the existing literature only considers the linear case. In this study, by designing the suitable terminal cost and terminal region, the mpc scheme is firstly investigated for the non-linear case. Meanwhile, to reduce the computational burden of mpc using the existing model types (e.g. recurrent neural network) as the identified model, an RMTN possessing the concise structure and high computational efficiency is constructed to approximate the state-space model of the system. After trained by the backpropagation through time algorithm, the RMTN obtaining the high accuracy of prediction over a long-range horizon is capable of serving as the prediction model in the mpc scheme. Furthermore, aiming at alleviating the adverse effect of the inevitable identification error, the tube-based mpc is proposed via leveraging dual-mode mpc to guarantee that actual trajectory is contained within a robust tube. The stability of the considered system is proved theoretically, and numerical simulation is employed to validate the effectiveness of the proposed method.

关键词： recurrent neural nets discrete time systems predictive control backpropagation robust control linear systems delay systems RMTN approximate the state-space model prediction model mpc scheme tube-based mpc robust model predictive control recurrent multidimensional Taylor network discrete-time nonlinear time-delay mpc algorithm discrete-time time-delay systems nonlinear case existing model types recurrent neural network

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Improving model predictive control arithmetic robustness by Monte Carlo simulations

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IET CONTROL THEORY AND APPLICATIONS 2012年第8期6卷 1064-1070页

作者： Vouzis, P. D. Collange, S. Arnold, M. G. Kothare, M. V. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Ecole Normale Super Lyon LIP F-69364 Lyon 07 France Lehigh Univ Dept Comp Sci & Engn Bethlehem PA 18015 USA

Model predictive control (mpc) is an optimisation-based algorithm which usually requires a numerical method to calculate the solution of the problem. Inherently, numerical methods for optimisation problems are implemented on a finite-precision hardware platform and are subject to the appearance of numerical instabilities of catastrophic cancellation and ill-conditioned matrices. These anomalies are difficult to detect and overcome, and for safety-critical applications, it is essential to have a mechanism that can at least issue a warning when an arithmetic instability occurs. Towards this direction, Monte Carlo arithmetic (MCA) for the floating-point (FP) number system has been used for both detection and mitigation of catastrophic cancellation and ill-conditioned matrices. An alternative to FP is the Logarithmic Number System (LNS) that recently has been proposed for the real-time hardware implementation of embedded mpc. In this study the authors present the adaptation of MCA to LNS for detecting and mitigating catastrophic cancellation, forming the Monte Carlo Logarithmic Number System (MCLNS). An inherent drawback of MCA is the accuracy deterioration which is a direct consequence of the randomisation in the arithmetic operations. Additionally, multiple simulations of the system result in performance deterioration equal to the number of simulations. Using off-line simulations it is possible to determine the necessary hardware requirements to achieve desired accuracy under performance constraints. These trade-offs are studied and analysed for an mpc algorithm, and the hardware implementation cost of MCLNS is quantified by synthesis on a Xilinx Virtex-IV FPGA.

关键词： mathematics computing Optimisation techniques mpc algorithm Xilinx Virtex-IV FPGA Mathematics computing finite-precision hardware platform predictive control Digital arithmetic methods floating point arithmetic catastrophic cancellation optimisation-based algorithm control engineering computing Monte Carlo logarithmic number system model predictive control arithmetic Control engineering computing Optimal control Monte Carlo methods floating-point number system optimisation ill-conditioned matrices

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Application of an adaptive model predictive control algorithm on the Pelton turbine governor control

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IET RENEWABLE POWER GENERATION 2020年第10期14卷 1720-1727页

作者： Beus, Mateo Pandzic, Hrvoje Univ Zagreb Fac Elect Engn & Comp Dept Energy & Power Syst Unska 3 Zagreb Croatia

Traditionally, hydro turbine governor applications mainly rely on classical proportional-integral-derivative controllers. A classical controller can perform optimally only at the operating point chosen during the controller design. Since hydro power plants are highly non-linear systems alternative control approaches based on adaptive parameters are needed. Historically, due to the limited computation capabilities of microprocessors and programmable logic controllers (PLCs) used in hydro turbine governors, adaptive control schemes were not frequently applied. However, the latest generation of microprocessors and PLCs facilitate the application of adaptive control scheme based on predictive control algorithm for plants with faster dynamic behaviour. In that regard, this study introduces an adaptive controller based on model predictive control (mpc) algorithm developed and applied to a non-linear simulation model of a laboratory hydro power plant. The applied mpc algorithm is based on a linear prediction model whose parameters are identified offline for different operating points across the plant's operating range. The adaptive control scheme updates the prediction model parameters depending on the current operating point. Furthermore, the predictive control algorithm applied in this study is set up as a quadratic programming (QP) optimisation problem that is solved online using a QP solver in a form of Hildreth's algorithm.

关键词： hydraulic turbines linear systems fuzzy control adaptive control power generation control robust control control system synthesis predictive control three-term control quadratic programming nonlinear control systems adaptive model predictive control algorithm Pelton turbine governor control hydroturbine governor applications linear plant model fixed parameter scheme controller design hydropower plants nonlinear systems alternative control approaches adaptive parameters programmable logic controllers hydroturbine governors nonlinear simulation model laboratory hydropower plant mpc algorithm linear prediction model whose parameters adaptive control scheme updates prediction model parameters classical proportional-integral-derivative controllers quadratic programming optimisation QP solver Hildreth's algorithm QP optimisation

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