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multi-model predictive control of converter inlet temperature in the process of acid production with flue gas

INTERNATIONAL JOURNAL OF ADAPTIVE control AND SIGNAL PROCESSING 2024年第5期38卷 1725-1743页

作者： Liu, Minghua Li, Xiaoli Wang, Kang Liu, Zhiqiang Li, Guihai Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intelligen Beijing Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing Peoples R China Jiangxi Copper Co Ltd Guixi Smelter Guixi Jiangxi Peoples R China Beijing RTlink Technol Co Ltd Beijing Peoples R China

The smelting of non-ferrous metals produces substantial quantities of sulfur dioxide (SO2$$ {}_2 $$)-laden flue gas, which is seriously harmful to environment and humans. To improve the conversion ratio of SO2$$ {}_2 $$ and minimize environmental pollution, controlling converter inlet temperature during acid production has proven to be an efficient approach. However, unsteadiness of smelting procedure leads to frequent changes in the concentration of SO2$$ {}_2 $$, which affects the catalytic conversion of SO2$$ {}_2 $$ and the production of sulfuric acid. To regulate converter inlet temperature, a proposed method of multi-model predictive control is introduced. First, working conditions are divided and characterized according to the range of SO2$$ {}_2 $$ concentration. Then, the mathematical model is established for each working condition and the model predictive controller is designed. Finally, an effective switching mechanism is established to realize smooth switching under different working conditions and closed-loop control of the whole system. Through simulation validation, compared with traditional single-model predictive controllers and multi-model PID controllers, the proposed approach demonstrates improved transient performance and steady-state performance. Simulation outcomes clearly highlight the superiority of the proposed algorithm.

关键词： acid production converter inlet temperature flue gas multi-model predictive control switching control

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An active fault-tolerant control strategy of aircraft engines based on multi-model predictive control

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INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES 2023年第1期40卷 81-91页

作者： Du, Xian Ma, Yan-Hua Dalian Univ Technol Sch Microelect Dalian Peoples R China DUT Artificial Intelligence Inst Dalian Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian Peoples R China Jiangsu Prov Key Lab Aerosp Power Syst Nanjing Peoples R China

In order to mitigate or even eliminate the adverse effects caused by typical components faults of aircraft engines, an active fault-tolerant strategy based on multi-model predictive control is proposed, which consists of a pre-established multi-model library, a judgement module, and corresponding predictive controllers with smooth transition switching logic. multiple dynamic nonlinear or linear models are firstly established by means of system identification methods, based on the component-level nonlinear engine model or historical data in faults cases. The judgement module is utilized to online compare the engine measured outputs with that of all models in the pattern library and select the best matched dynamic model on the basis of outputs error quadratic performance index, thus determining the most appropriate predictive controller for the next control sample period. When a certain fault occurs, the fault model in the library could be identified and fault-model based predictive controller is activated. Finally, two kinds of pre-considered high-pressure compressor and high-pressure turbine component-level faults are taken as an example to design the active fault-tolerant controller. Simulation results show that the judgement module owns the ability to sense the fault and gives smooth switching signal to the suitable predictive controller, verifying the effectiveness of the proposed technique.

关键词： active fault-tolerant control aircraft engines faults judgement module multi-model predictive control smooth switching

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model Reduction and Chattering Mitigation in multi-model predictive control for Quadrotor UAVs

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IEEE Access 2025年 13卷 102640-102648页

作者： Mustafa Abro, Ghulam E. Memon, Sufyan Ali Rubab, Saddaf Alshammari, Kaznah Khan, Faheem King Fahd University of Petroleum and Minerals Interdisciplinary Research Centre for Aviation and Space Exploration (IRC-ASE) Dhahran 31261 Saudi Arabia Sejong University Department of Defense Systems Engineering Gwangjin-gu Seoul 05006 South Korea University of Sharjah College of Computing and Informatics Department of Computer Engineering Sharjah 27272 United Arab Emirates Northern Border University Faculty of Computing and Information Technology Department of Information Technology Rafha 91911 Saudi Arabia Gachon University Department of Computer Engineering Seongnam-si 13120 South Korea

This study introduces a multi-model predictive control (MMPC) framework for quadrotor attitude regulation, combining the high-precision capabilities of nonlinear model predictive control (NMPC) with the computing efficiency of linear model predictive control (LMPC). Although NMPC provides excellent regulation, its substantial computational expense restricts its real-time use in high-refresh-rate applications like attitude control. In contrast, LMPC exhibits computational efficiency but encounters challenges with stability and precision in nonlinear dynamics. In order to resolve this discrepancy, we propose a MMPC approach that integrates Principal Component Analysis (PCA) for model reduction and employs linear quadrotor attitude models. This approach reduces the computational complexity and the number of necessary Linear model predictive controllers (LMPCs). To improve performance, we implement an adaptive gain scheduling algorithm to mitigate the chattering effect frequently seen in multi-model approaches. This seamless switching technique diminishes high-frequency noise in brushless DC motor operation, minimises peak overshoot, and mitigates actuator wear, thereby enhancing system stability and durability. Comparative assessments indicate that the suggested MMPC framework attains NMPC-equivalent performance while incurring LMPC-level computational expenses, exceeding the efficacy of conventional incremental nonlinear dynamic inversion, sliding mode control, and traditional NMPC and LMPC methodologies. This study offers a computationally efficient and stable control solution for real-time UAV applications, including model reduction, chattering mitigation, and predictive control optimisation for autonomous aerial systems. © 2013 IEEE.

关键词： Attitude control Chattering Mitigation Gain Scheduling multi-model predictive control Nonlinear Dynamics Quadrotor UAVs

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In-depth characteristic analysis and wide range optimal operation of fuel cell using multi-model predictive control

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ENERGY 2021年 234卷

作者： Fu, Hao Shen, Jiong Sun, Li Lee, Kwang Y. Southeast Univ Sch Energy & Environm Minist Educ Key Lab Energy Thermal Convers & Control Sipailou 2 Nanjing 210096 Peoples R China Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

Humidity and voltage are the critical parameters that have a significant influence on the thermal stability and electrical efficiency of the proton exchange membrane fuel cells (PEMFCs). However, the inherent coupling between the two parameters makes the controller design challenging from a system's perspective. Furthermore, frequent load fluctuations during fuel cell operation give rise to considerable fluctuations in voltage and humidity. To mitigate this undesirable variation, the system coupling of voltage and humidity and nonlinearity resulting from varying load are analyzed in this paper, where the coupled dynamics of relative humidity and output voltage are manipulated by the humidifier electrical power and compressor voltage. Based on the analysis, a multi-model predictive control (MMPC) scheme is formulated for the coupled plant dynamics, where the prediction model utilized is developed based on the subspace identification method for local models integrated with fuzzy membership weightings. The MMPC formulation has taken advantage of considering the actuator saturation and the allowable output range as input and output constraints. Simulation results show that the proposed MMPC can improve the tracking performance with a flexible trade-off between the relative humidity and output voltage. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Proton exchange membrane fuel cell (PEMFC) multivariable model predictive control multi-model predictive control

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ν-gap metric based multi-model predictive control of an ocean current turbine system with blade pitch failures

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OCEAN ENGINEERING 2023年 278卷

作者： Ondes, E. Baris Sultan, Cornel Hasankhani, Arezoo VanZwieten, James H. Xiros, Nikolaos I. Virginia Tech 460 Old Turner St Blacksburg VA 24061 USA Florida Atlantic Univ 777 Glades Rd Boca Raton FL 33431 USA Univ New Orleans 2000 Lakeshore Dr New Orleans LA 70148 USA

In this article, a method of combining multiple linear models is developed and used by an advanced model predictive control algorithm for ocean current turbine flight control. The developed model bank consists of faulty and healthy linear models created for the ocean current turbine system. Each linear model is used to compute individual control actions using the model predictive control framework, which are combined to generate the aggregate control action that is applied to the system. A weighted average defined by the nu-gap distance between the current linear approximation of system dynamics and each individual linear system is used to compute the aggregate control action. The multi-model predictive control strategy is applied to control the ocean current turbine to mitigate performance degradation that may occur due to failures in the system. The proposed control framework's effectiveness is shown with the renewable power generation improvement during a faulty case.

关键词： multi-model predictive control Marine renewable energy Fault-tolerant control nu-gap metric Ocean current turbine

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Self-adjusted decomposition for multi-model predictive control of Hammerstein systems based on included angle

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ISA TRANSACTIONS 2020年 103卷 19-27页

作者： Du, Jingjing Zhang, Lei Chen, Junfeng Li, Jian Jiang, XuePing Zhu, Changping Hohai Univ Coll Internet Things Engn Changzhou 213022 Peoples R China Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing 210003 Peoples R China Wanjiang Univ Technol Key Lab Unmanned Aerial Vehicle Dev & Data Applic Maanshan 243031 Peoples R China

A self-adjusted multi-model decomposition (SAMMD) method is proposed based on included angle (IA) to realize multi-model predictive control (MMPC) of Hammerstein systems. Given an initial value for threshold and a step-size, a balanced decomposition in terms of Measurement of Nonlinearity (MoN) is obtained with an appropriate linear model set to approximate the considered Hammerstein system. Based on the linear model set, a MMPC is designed for set-point tracking and anti-disturbance control using an offline weighting method. Thus, time-consuming tuning of threshold value is largely avoided;reliance on experience is greatly decreased. And the efficiency and quality of decomposition are largely raised. A CSTR process and a Lab-tank system that can be approximated by Hammerstein models are investigated. Simulations illustrate that the proposed SAMMD method is both effective and efficient. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Included angle Hammerstein systems Self-adjusted decomposition Balanced decomposition multi-model predictive control

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Gap-based multi-model predictive control of a TITO System

Gap-based Multi-model Predictive Control of a TITO System

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Chinese Automation Congress (CAC)

作者： Du, Jingjing Zhang, Lei Li, Jian Hohai Univ Coll Internet Things Engn Changzhou 213022 Peoples R China Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing 210003 Peoples R China

ISBN: (纸本)9781728140940

Gap metric is widely used in multi-model control. It is used to carry out the multi-model decomposition and it is also used to realize multi-model combination. The multi-model predictive control method, which integrates the advantages of the model predictive control technique and the multi-model control method, has attracted much attention in nonlinear control systems. In this work, we present a gap-based multi-model predictive control (GMMPC) method and apply it to a two-input-two-output (TITO) nonlinear system.

关键词： gap-based multi-model predictive control multi-model decomposition multi-model combination

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A New Weighting Method based on Gap Metric for multi-model predictive control 31

A New Weighting Method based on Gap Metric for Multi-model P...

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31st Chinese control And Decision Conference (CCDC)

作者： Du, Jingjing Zhang, Lei Chen, JunFeng Hohai Univ Coll Internet Things Engn Changzhou 213022 Peoples R China Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing 210003 Peoples R China

ISBN: (纸本)9781728101057

A new weighting method is proposed based on gap metric for multi-model predictive control of nonlinear systems. The proposed weighting method is simple but efficient. It is used to combine the local predictive controllers into a multi-model predictive controller which is employed to optimize and control the considered nonlinear system. Compared to traditional weighting methods, such as the Trapezoidal weighting method, the Gaussian weighting method, and so on, the proposed new weighting method has just one tuning parameter, and thus it is simple and easy to tune. Besides, it continues the merits of the traditional method: the weights can be computed off-line. What is more important, it is effective to schedule the local model predictive controllers and get an effective control signal to control the outputs. Therefore, the closed-loop performance is raised and the stability and robustness are improved. A CSTR system is studied to demonstrate the effectiveness of the proposed method.

关键词： Gap metric multi-model predictive control Weighting method Nonlinear system Tuning

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Nonlinear modeling and Inferential multi-model predictive control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation

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ENERGIES 2018年第3期11卷

作者： Liang, Xiufan Li, Yiguo Wu, Xiao Shen, Jiong Southeast Univ Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Jiangsu Peoples R China

Fuel preparation is the control bottleneck in coal-fired power plants due to the unmeasurable nature or inaccurate measurement of key controlled variables. This paper proposes an inferential multi-model predictive control scheme based on moving horizon estimation for the fuel preparation system in coal-fired power plants, i.e., the pulverizing system, aimed at improving control precision of key operating variables that are unmeasurable or inaccurately measured, and improving system tracking performance across a wide operating range. We develop a first principle model of the pulverizing system considering the nonlinear dynamics of primary air, and then employ the genetic algorithm to identify the unknown model parameters. The outputs of the identified first principle model agree well with measured data from a real pulverizing system. Thereafter we derive a moving horizon estimation approach to estimate the desired, but unmeasurable or inaccurately measured, controlled variables. Estimation constraints are explicitly considered to reduce the influence of measurement uncertainty. Finally, nonlinearity of the pulverizing system is analyzed and a multi-model inferential predictive controller is developed using the extended input-output state space model to achieve offset-free performance. Simulation results show that the proposed soft sensor can provide improved estimates than conventional extended Kalman filter, and the proposed inferential control scheme can significantly improve performance of the pulverizing system.

关键词： pulverizing system soft sensor inferential control moving horizon estimation multi-model predictive control

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Optimal direct adaptive soft switching multi-model predictive control using the gap metric for spacecraft attitude control in a wide range of operating points

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AEROSPACE SCIENCE AND TECHNOLOGY 2018年 77卷 235-243页

作者： Saki, Saman Bolandi, Hossein Mashhadi, Seyed Kamal-e-ddin Mousavi Iran Univ Sci & Technol Dept Elect Engn Tehran *** Iran

In this paper, a new multi-Input multi-Output (MIMO) multi-model predictive control (MMPC) with direct adaptive structure is used for spacecraft attitude control in a wide range of operating points and maneuvers. Because of the highly nonlinear dynamics, the linearized model characteristics are extremely depended to the operating points. In such cases, an expected performance, in the wide range of the operating points, never can be achieved using a single controller and single model (even instability may be anticipated). To handle this problem, in this paper, we divide the whole operating range of the spacecraft to construct sub-models (model bank) using a mathematical tool called as gap metric. Next, an adaptive MPC based on sub-models is designed. In this procedure, there are two problems: stability when switching among models and the minimum number of sub-models. Hard switching among models to update the controller's model causes extreme chattering on the control signal and reference tracking. The motivation of this paper is to present a new solution for the mentioned problems. To solve the first problem (remove chattering), an adaptive soft switching law to tune the controller parameters, when selecting new model, based on the Lyapunov theory is introduced as the main novelty of this paper. This guarantees the stability of the closed loop control system. For solution of the second problem, the number of optimal sub-models is obtained using different simulations. Finally, the effectiveness of the suggested method is proven via simulations. (C) 2018 Elsevier Masson SAS. All rights reserved.

关键词： multi-model predictive control Soft switching Attitude control Gap metric Lyapunov theory

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