检索结果-内蒙古大学图书馆

Study on a novel configuration switchable hydraulically interconnected suspension system under nonlinear model predictive control

引用

VEHICLE SYSTEM DYNAMICS 2022年第10期60卷 3440-3461页

作者： Chen, Tong Zheng, Minyi Zhang, Nong Luo, Liang Liu, Pengfei Zhong, Weimin Hefei Univ Technol Sch Automot & Transportat Hefei Peoples R China Hefei Univ Technol Automot Res Inst Hefei Peoples R China

This paper is a study on a novel configuration switchable hydraulically interconnected suspension (CSHIS) system based on nonlinear model predictive control (NMPC). The proposed CSHIS system has significant improvements in stability and performance than the existing CSHIS systems. The CSHIS system uses a direction valve for configuration switching, and improves the overall vehicle dynamic performances. The developed NMPC algorithm enhances system stability and the actuator control process by predicting system states, and consequently improves dynamic performances and power consumption of the vehicle. A nonlinear CSHIS model is firstly established and discretised to predict system states which are used in the NMPC algorithm. Next, the NMPC-based controller is designed to optimise the actuator control process via two weighting factors which are related to dynamic performance and power consumption, respectively. Finally, numerical simulation and experiments on test bench are conducted to validate the proposed CSHIS system. The simulation results show that the proposed CSHIS system simultaneously improves anti-pitch and anti-roll properties compared with a conventional suspension system by 39.5% and 17.81%, respectively. The experiment results illustrate that the CSHIS system remains stable during and after the configuration switching and optimises the actuator control process by the weighting factors.

关键词： Hydraulically interconnected suspension configuration switching nonlinear model predictive control vehicle dynamics energy consumption

来源：评论

学校读者我要写书评

暂无评论

Two-stage Particle Swarm Optimization-based nonlinear model predictive control Method for Reheating Furnace Process

引用

ISIJ INTERNATIONAL 2014年第8期54卷 1836-1842页

作者： Tang, Zhenhao Yang, Yang Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Liaoning Key Lab Mfg Syst & Logist Shenyang 110819 Peoples R China Northeastern Univ Logist Inst Shenyang 110819 Peoples R China

The steel slab temperature control of reheating furnace process plays an important role in the production of high quality reheated slab. Because of the characteristics of nonlinearity, long time-delay and uncertainty, high-accuracy slab temperature control is a challenging problem. This paper proposes a two-stage particle swarm optimization (PSO)-based nonlinear model predictive control (NMPC) method to solve the problem. In this method support vector machine (SVM) is utilized to construct the nonlinear predictive model based on the real production data. To obtain better predictive model dynamically, PSO optimizes the parameters of SVM for different problems. Then PSO solves the rolling optimization problem in NMPC to obtain the proper control variables. Finally, the production data collected from a real reheating furnace process are utilized to test the proposed method. Numerical experiments are done by computer simulation based on the real production data. The experiment results illustrate that the PSO-based SVM can obtain accurate predictive model. Moreover, the proposed nonlinear model predictive control method can obtain outstanding control accuracy in steel slab temperature control.

关键词： steel slab temperature control particle swarm optimization nonlinear model predictive control reheating furnace

来源：评论

学校读者我要写书评

暂无评论

No_x conversion efficiency optimization based on NSGA-II and state-feedback nonlinear model predictive control of selective catalytic reduction system in diesel engine

引用

APPLIED ENERGY 2017年 206卷 959-971页

作者： Wei, Li Yan, Fuwu Hu, Jie Xi, Guangwei Liu, Bo Zeng, Jiawei Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components Wuhan 430070 Hubei Peoples R China Hubei Collaborat Innovat Ctr Automot Components T Wuhan 430070 Hubei Peoples R China

The selective catalytic reduction (SCR) technology is a promising diesel after-treatment technology due to its high NOx reduction potential. A nonlinear model predictive control (NMPC) approach was proposed based on the three-state SCR model here for control and optimization of SCR system to maximize NOx reduction while minimizing NH3 slip and urea consumption;moreover, the ammonia coverage ratio was designed to as a control target, whose desired range was obtained by means of the singular perturbation method, that is to say, the upper limit of ammonia coverage ratio was derived by 10 ppm NH3 slip while the lower limit was calculated by maximum NOx conversion efficiency, which was obtained through a novel model based non-dominated sorting genetic algorithm (NSGA-II) according to an optimum tradeoff between NOx . conversion and NH3 slip at each engine operating point. The state-feedback NMPC algorithm was applied to ensure the estimated ammonia coverage ratio falling in the desired range so that the SCR performance can be optimized over entire engine operating. The standard European steady-state cycle (ESC) was performed for data generation from a diesel engine equipped with a commercial SCR and to verify the effectiveness of the designed control strategy. Simulation results over-the entire ESC indicated that the NOx conversion efficiency may be optimal in various engine operating modes and the NMPC controller ensures the most part of the cycle may conform to the NOx emissions requirements;moreover, the downstream NH3 concentration was less than the limit;especially, the whole mean NH3 slip was 9.7 ppm which is basically close to the given limit (10 ppm);furthermore, the ammonia storage capacity falls rapidly along with growth of the temperature but the NMPC still worked well for prevention of any unacceptably high NH3 slip. The experimental verification was carried out and the results showed that the designed strategy was effective and acceptable compared with the s

关键词： Selective catalytic reduction system Ammonia coverage ratio reference Non-dominated sorting genetic algorithm nonlinear model predictive control Diesel emissions control

来源：评论

学校读者我要写书评

暂无评论

Dynamic modelling and nonlinear model predictive control of a Fluid Catalytic Cracking Unit

引用

COMPUTERS & CHEMICAL ENGINEERING 2009年第3期33卷 605-617页

作者： Roman, Raluca Nagy, Zoltan K. Cristea, Mircea V. Agachi, Serban P. Univ Loughborough Dept Chem Engn Loughborough LE11 3TU Leics England Univ Babes Bolyai Dept Chem Engn Cluj Napoca 400028 Romania

The paper presents the application of two nonlinear model predictive control (NMPC) approaches: quasi-infinite-horizon nonlinear MPC (QIHNMPC) and moving horizon estimator nonlinear MPC (MHE-NMPC) to the Fluid Catalytic Cracking Unit (FCCU). A complex dynamic model of the reactor-regenerator-fractionator system is developed and subsequently used in the controller. The novelty of the model consists in that besides the complex dynamics of the reactor-regenerator system, it also includes the dynamic model of the fractionator, as well as a five lumps kinetic model for the riser. Tight control is achieved using the QIHNMPC approach. The MHE-NMPC considers important features of a real-time control algorithm, resulting in a framework for practical NMPC implementation, such as: state and parameter estimation and efficient solution of the optimisation problem. In the NMPC approach, only measurements available in practice are considered, whereas the rest of the states are estimated together with uncertain model parameters, via MHE technique. Using an efficient numerical implementation based on the multiple shooting algorithm real-time feasibility of the approach is achieved. The incentives of the proposed approaches are assessed on the simulated industrial FCCU. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Catalytic cracking Dynamic modelling nonlinear model predictive control Moving horizon estimator Real-time control

来源：评论

学校读者我要写书评

暂无评论

Inexact Newton-Kantorovich Methods for Constrained nonlinear model predictive control

引用

IEEE TRANSACTIONS ON AUTOMATIC control 2019年第9期64卷 3602-3615页

作者： Dontchev, Asen L. Huang, Mike Kolmanovsky, Ilya V. Nicotra, Marco M. Amer Math Soc Providence RI 02904 USA Univ Michigan Ann Arbor MI 48109 USA Toyota Motor North Amer Res & Dev Ann Arbor MI 48105 USA Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA

In this paper, we consider Newton-Kantorovich type methods for solving control-constrained optimal control problems that appear in model predictive control. Conditions for convergence are established for an inexact version of the Newton-Kantorovich method applied to variational inequalities. Based on these results, two groups of algorithms are proposed to solve the optimality system. The first group includes exact and inexact Newton and Newton-Kantorovich implementations of the sequential quadratic programming. In the second group, exact and inexact Newton and Newton-Kantorovich methods are developed for solving a nonsmooth normal map equation equivalent to the optimality system. Numerical simulations featuring examples from the aerospace and automotive domain are presented, which show that inexact Newton-Kantorovich type methods can achieve significant reduction of the computational time.

关键词： Constrained systems control engineering computing inexact Newton-Kantorovich method linear convergence Newton method nonlinear dynamical systems nonlinear model predictive control optimal control quadratic programming strong regularity

来源：评论

学校读者我要写书评

暂无评论

Enhanced gas-lift system operation using LPV nonlinear model predictive control

GEOENERGY SCIENCE AND ENGINEERING

引用

GEOENERGY SCIENCE AND ENGINEERING 2024年 239卷

作者： Naspolini, Amir Morato, Marcelo Menezes Flesch, Rodolfo Cesar Costa Normey-Rico, Julio Elias Univ Fed Santa Catarina UFSC Inst Nacl Ciencia & Tecnol Controle & Proc Energia Dept Automac & Sistemas DAS Florianopolis SC Brazil Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France

In this work, we propose a novel Linear Parameter Varying (LPV) model predictive control (MPC) scheme for oil production maximisation in wells operated with gas -lift systems. The control of a gas -lift system poses several engineering challenges, including the nonlinear nature of its behaviour and different kinds of process constraints. Recent studies indicate that MPC strategies stand out as promising solutions to address many of these issues - with results already published for gas -lift applications. However, when nonlinear models are used, the corresponding nonlinear MPC (NMPC) algorithms entail substantial computational costs, potentially complicating the feasibility of (real-time) embedded implementation. Furthermore, certain complexities in standard gas -lift system representations hinder the use of typical NMPC solvers (e.g. CasADi), thus making approximation -based schemes unavoidable. Accordingly, we propose an LPV formulation to describe the gaslift system that ensures closed -loop stability and recursive feasibility when implemented as a model for MPC. In this regard, we synthesise an LPV MPC scheme and compare it to an NMPC through CasADi. Several realistic nonlinear numerical simulations are presented and our results indicate that the proposed LPV MPC scheme, in addition to being three times faster (in average), achieved an increase in the total amount of produced oil when compared to an NMPC via CasADi. Then, aiming to maximise oil production rate, we introduce a modified version of the LPV MPC approach, resulting in an increase in the total amount of produced oil of approximately 3% in the studied scenario, when compared to the unmodified LPV MPC scheme.

关键词： Oil production Gas-lift Optimisation nonlinear model predictive control Linear parameter varying system

来源：评论

学校读者我要写书评

暂无评论

nonlinear model predictive control for trajectory tracking of nonholonomic mobile robots: A modified approach

引用

INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2018年第1期15卷

作者： Nascimento, Tiago P. Trabuco Dorea, Carlos Eduardo Goncalves, Luiz Marcos G. Univ Fed Paraiba UFPB LaSER Rua Escoteiros S-NCampus Mangabeira Joao Pessoa Paraiba Brazil Univ Fed Rio Grande Norte UFRN DCA Natal RN Brazil

Trajectory tracking for autonomous vehicles is usually solved by designing control laws that make the vehicles track predetermined feasible trajectories based on the trajectory error. This type of approach suffers from the drawback that usually the vehicle dynamics exhibits complex nonlinear terms and significant uncertainties. Toward solving this problem, this work proposes a novel approach in trajectory tracking control for nonholonomic mobile robots. We use a nonlinear model predictive controller to track a given trajectory. The novelty is introduced by using a set of modifications in the robot model, cost function, and optimizer aiming to minimize the steady-state error rapidly. Results of simulations and experiments with real robots are presented and discussed verifying and validating the applicability of the proposed approach in nonholonomic mobile robots.

关键词： Nonholonomic systems mobile robots nonlinear model predictive control trajectory tracking control

来源：评论

学校读者我要写书评

暂无评论

nonlinear model predictive control of Integrated Diesel Engine and Selective Catalytic Reduction System for Simultaneous Fuel Economy Improvement and Emissions Reduction

引用

JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND control-TRANSACTIONS OF THE ASME 2015年第8期137卷 081008-081008页

作者： Chen, Pingen Wang, Junmin Ohio State Univ Dept Mech & Aerosp Engn Columbus OH 43210 USA

The applications of diesel engines in ground vehicles have attracted much attention over the past decade for the reasons of outstanding fuel economy, power capability, and reliability. With the increasing demand of less greenhouse gas emissions, the current diesel engine fuel efficiency remains unsatisfactory partially due to the conflict between the engine fuel efficiency and engine-out NOx emissions. While advanced aftertreatment systems, such as selective catalytic reduction (SCR) systems or lean NOx trap, have been integrated to diesel engines for reducing the tailpipe NOx emissions, the integrated controls for coordinating diesel engine and SCR system to achieve high engine efficiency and low tailpipe emissions are still limited. The purpose of this study is to develop such an integrated diesel engine and SCR system control method using nonlinear model predictive control (NMPC) approach with both start of injection (SOI) timing and urea solution injection rate as the control inputs. control-oriented engine models were developed to quantify the influences of SOI timing on engine efficiency and engine-out NOx emissions. Simulation results under US06 driving cycle demonstrate that, given the same catalyst size in total, the proposed controllers are capable of reducing total engine fuel consumption over the driving cycle by 9.36% and 9.50%, respectively, for lumped SCR system and two-cell SCR system, while maintaining high NOx conversion efficiencies and low tailpipe ammonia slip.

关键词： diesel engine selective catalytic reduction system integrated control nonlinear model predictive control fuel economy

来源：评论

学校读者我要写书评

暂无评论

nonlinear model predictive control of Electrical Submersible Pumps based on Echo State Networks

引用

ADVANCED ENGINEERING INFORMATICS 2022年 52卷

作者： Jordanou, Jean P. Osnes, Iver Hernes, Sondre B. Camponogara, Eduardo Antonelo, Eric Aislan Imsland, Lars Univ Fed Santa Catarina Dept Automat & Syst Engn BR-88040900 Florianopolis SC Brazil Norwegian Univ Sci & Technol Dept Engn Cybernet Trondheim Norway

Employed for artificial lifting in oil well production, Electrical Submersible Pumps (ESP) can be operated with model predictive control (MPC) to drive an optimal production, while ensuring a safe operation and respecting system constraints. Due to the nonlinear dynamics of ESPs, Echo State Networks (ESNs), a recurrent neural network with fast training, are employed for efficient system identification of unknown dynamic systems. Besides the synthesis of highly accurate prediction models, this work contributes by designing two nonlinear MPC (NMPC) strategies for the control of an ESP-lifted oil well: a standard Single-Shooting NMPC that embeds the ESN model completely, and the Practical nonlinear model predictive controller (PNMPC) that approximates the NMPC through fast trajectory-linearization of the ESN model. Another contribution is the implementation of an error correction filter to reject disturbances and counter modeling errors in both NMPC strategies. Finally, in computational experiments, both ESN-based NMPC strategies performed well in controlling simulated ESP lifted oil wells when the model of the plant is unknown. However, PNMPC was more efficient and induced a similar performance to standard NMPC.

关键词： Echo State Networks nonlinear model predictive control control of unknown dynamic systems Oil production wells Electrical submersible pumps

来源：评论

学校读者我要写书评

暂无评论

Two-timescale nonlinear model predictive control for flexible operation of coal-fired power plant with post combustion CO2 2 capture system

引用

APPLIED THERMAL ENGINEERING 2024年 256卷

作者： Yang, Chao Guo, Wenxuan Shao, Yuhao Lin, Yangshu Pu, Xuesen Wang, Yifan Zheng, Chenghang Ding, Yi Fan, Haidong Zhu, Yucai Gao, Xiang Zhejiang Univ State Environm Protect Ctr Coal Fired Air Pollut C State Key Lab Clean Energy Utilizat Hangzhou 310027 Peoples R China Zhejiang Univ Inst Carbon Neutral State Environm Protect Engn Ctr Coal Fired Air Pol State Key Lab Clean Energy Utilizat Hangzhou 310027 Peoples R China Dongfang Elect Autocontrol Engn Co LTD Deyang 618000 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

Post-combustion carbon capture (PCC) with chemical absorption exhibits significant interactions with coal-fired power plants (CFPP). The mismatch in the timescales of dynamic responses between PCC and CFPP, along with their nonlinear characteristics, presents additional challenges for the design of controllers. To address these obstacles, this work presents a two-timescale nonlinear model predictive control (T-NMPC) strategy to align the operational flexibility of the CFPP-PCC coupled system. A slow timescale nonlinear model predictive controller (S-NMPC) manages the extensive set of variables within the integrated CFPP-PCC system, focusing on long-term stability. In contrast, a fast timescale nonlinear model predictive controller (F-NMPC) dynamically adjusts the CFPP power and steam pressure to meet real-time operational demands. The interaction between these two controllers, both using extraction steam as a controlled variable, increases the flexibility of the CFPP by exploiting the energy buffers in the PCC. Comprehensive simulations of the CFPP-PCC system substantiate the enhancements in automatic generation control (AGC) performance, affirming the strategy's effectiveness.

关键词： Coal-fired power plant Post combustion carbon capture nonlinear model predictive control Two-timescale control Flexible operation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：