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RBF-ARX model-based trust region nonlinear model predictive control and its application on magnetic levitation ball system

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nonlinear DYNAMICS 2025年第3期113卷 2521-2543页

作者： Peng, Tianbo Peng, Hui Kang, Tiao Cent South Univ Sch Automat Changsha 410083 Hunan Peoples R China Xiangjiang Lab Changsha 410205 Peoples R China Hunan Inst Engn Engn Training Ctr Xiangtan 411101 Peoples R China

Real-time nonlinear model predictive control (NMPC) of a nonlinear system with extremely short sampling periods poses significant challenges, particularly in balancing optimality in solving non-convex optimization problems with the computational efficiency required for real-time implementation. To address this, a trust region nonlinear model predictive control (TR-NMPC) method is proposed based on a real-time iteration scheme, enabling stable and effective solutions to the non-convex minimization problem inherent in NMPC. Firstly, radial basis function-based autoregressive model with exogenous variables (RBF-ARX) is employed to describe the dynamics of a magnetic levitation ball system, forming the basis in NMPC design. Then, the non-convex optimization problem in NMPC is approximated in the real-time iteration scheme. To constrain the approximation error, we propose and analyze a trust region optimization method, which dynamically adjusts the trust region in each iteration based on the discrepancy between the designed and approximated objective functions. By combining the trust region optimization method with the RBF-ARX model-based parameter scheduling strategy in real-time iteration scheme, the non-convex optimization problem in NMPC is solved with high real-time efficiency. Simulation and real-time control experiments on the magnetic levitation ball system demonstrate that the proposed NMPC method achieves both exceptional computational efficiency and superior transient performance.

关键词： Real-time iteration nonlinear model predictive control Magnetic levitation system RBF-ARX model Fast responding system

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Anti-wheelie systems for high-performance motorcycles: A nonlinear model predictive control approach

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control ENGINEERING PRACTICE 2025年 157卷

作者： Caiaffa, Luca Maran, Fabio Furlan, Matteo Peron, Stivi Beghi, Alessandro Bruschetta, Mattia Univ Padua Dept Informat Engn Padua Italy Piaggio & Co Pisa Italy

The global proliferation of Powered Two-Wheel (PTWs) underscores the need for increasingly effective active safety systems in motorcycles. Among others, the Anti-wheelie (AW) system is one of the most peculiar and safety-critical, aiming at limiting front wheel lift, preventing from possible vehicle instability, loss of control, and, in general, increased accident risk for motorcyclists. In this paper, an AW system based on an always-active, closed-loop control action that relies on a refined vehicle dynamics model is proposed. A nonlinear model predictive control strategy is leveraged to track an optimal pitch angle, ensuring maximum acceleration while maintaining safe interaction with the rider by constraining the reduction in applied torque. The control system is implemented on Raspberry Pi hardware, coupled to the vehicle's Electronic control Unit (ECU). Preliminary tuning was conducted in a high-fidelity co-simulation environment, and experimental tests were conducted with a sport-commercial vehicle showing satisfactory control performance even in extreme maneuvers. The effectiveness of the control action is further validated through suspension travel measurements and feedback from professional test drivers.

关键词： Anti-wheelie systems nonlinear model predictive control Vehicle control Transportation systems Advanced Riding Assistance Systems (ARAS)

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Aero-engine direct thrust control based on nonlinear model predictive control with composite predictive model

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INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES 2025年第1期42卷 129-137页

作者： Chen, Haoyang Li, Liangliang Zheng, Qiangang Zhang, Haibo Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China Minist Ind & Informat Technol Aeroengine Thermal Environm & Struct Key Lab Nanjing Peoples R China AECC Hunan Aviat Powerplant Res Inst Zhuzhou 412002 Hunan Peoples R China

A novel NMPC (nonlinear model predictive control) based on composite predictive model is proposed and applied to direct engine thrust control. To improve the real-time of NMPC, an adaptive composite model based on SVM (State Variable model), KF (Kalman Filter), and CLM (Component Level model) is proposed as predictive model. The correction theory is adopted to establish a full envelope adaptive on-board predictive dynamic model and reduce the data storage of predictive model. At each sampling time, the CLM is calculated only once in the proposed NMPC, instead of many times in the popular NMPC based on EKF (extended Kalman filler). Therefore, the proposed NMPC has better real-time performance than the popular one. The simulations that consist of the proposed NMPC, the popular NMPC based on EKF, and the traditional controller PID are conducted. The simulations demonstrate that the proposed NMPC not only has greatly better real time performance than popular NMPC, but also has faster response speed than traditional controller PID.

关键词： aero-engine nonlinear model predictive control full envelope adaptive dynamic model Kalman filter

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nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model

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ENERGY 2024年 311卷

作者： Liu, Junbo Cai, Chang Song, Dongran Zhong, Xiaohui Shi, Kezhong Chen, Yinpeng Cheng, Shijie Huang, Yupian Jiang, Xue Li, Qing'an Chinese Acad Sci Inst Engn Thermophys Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Wind Energy Utilizat Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Cent South Univ Sch Automat Changsha 410012 Peoples R China Huadian Elect Power Res Inst Co Ltd New Energy Res Ctr Hangzhou Peoples R China Zhejiang Ocean Univ Dept Naval Architecture & Marine Engn Zhoushan 316022 Peoples R China Zhejiang Tusheng Transmiss & Tr Ltdansformat Engn Wenzhou Peoples R China

The application of floating offshore wind turbines means better exploitation of offshore wind resources. However, the six-degree of freedom motion characteristics of floating platforms bring greater challenges to control system design. Based on the dynamic characteristics of semi-submersible floating offshore wind turbines, this paper establishes a simplified nonlinear dynamic model for control system design. On this basis, a complete framework for nonlinear model predictive control of maximum wind energy extraction for semi-submersible floating offshore wind turbines considering wind and wave disturbances is developed. Based on the previewed wind and wave, a dynamic optimization problem with both state and control constraints is constructed, considering maximum wind energy extraction and torque fluctuation. Then, an improved equilibrium optimizer is proposed to address the nonlinear non-convex dynamic optimization problems, which achieves a better tradeoff between exploitation and exploration. Simulation results verify the superiority of the proposed nonlinear model predictive control framework via the improved equilibrium optimizer, and the influences of different control algorithms on the platform motion state, power coefficient, and equivalent wind speed are analyzed.

关键词： Floating offshore wind turbine Simplified nonlinear dynamic model Maximum wind energy extraction nonlinear model predictive control Improved equilibrium optimizer

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nonlinear model predictive control of flexible ammonia production

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control ENGINEERING PRACTICE 2024年 148卷

作者： Kong, Baiwen Zhang, Qi Daoutidis, Prodromos Univ Minnesota Dept Chem Engn & Mat Sci Minneapolis MN 55455 USA

Flexible dynamic operation of ammonia synthesis is a key enabling factor of green ammonia production in the face of intermittency in renewable energy sources. A nonlinear model predictive control scheme is presented in this paper for the Haber-Bosch ammonia synthesis process with a varying feed flowrate. The considered process consists of three interstage-cooled reactor beds and a flash separator, with a flexible reactor load varying between 50% and 100% of its nominal capacity. The proposed control scheme aims to control the reactor temperatures, separation pressure, and the liquid volume in the flash tank during feed transitions. A simulation study is performed given an assumed 5 -hour feed schedule. The results indicate that all controlled variables are maintained in a safe operating range and tracked with small offsets in the nominal case as well as under disturbances in the reaction rate. It takes approximately 10 min for the reactor temperatures and 40 min for the separation pressure to reach steady state after a large step change in the reactor load. In a comparison with a PID control scheme, the investigated NMPC scheme tracks feed temperatures and the separation pressure significantly faster with less oscillations. This study therefore demonstrates the feasibility and effectiveness of the proposed control scheme in enabling flexible operation of the process.

关键词： Process control Green ammonia nonlinear model predictive control

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nonlinear model predictive control of Post-combustion CO2 Capture Process for Flexible Operation 13th

Nonlinear Model Predictive Control of Post-combustion CO2 Ca...

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13th IFAC Symposium on Dynamics and control of Process Systems, including Biosystems (DYCOPS)

作者： Jung, Howoun Lee, Jay H. Korea Adv Inst Sci & Technol Dept Chem & Biomol Engn Daejeon 305701 South Korea

A large amount of energy requirement for solvent regeneration is a major barrier to the widespread adoption of amine-based post-combustion CO2 capture (PCC). Flexible operation is one of the ways to lower the energy penalty by responding to changes in economic factors like the energy price. However, for effective implementation of flexible operation strategies, it is necessary to identify the most economic operating condition under various potential scenarios and to establish an appropriate control strategy to operate the process. As flexible operation will inherently involve a large operating envelope, we investigate the use of nonlinear model predictive control (NMPC) technology. To circumvent the problem of solving a large-scale nonlinear programming problem online, a simpler NARX model is identified and used. With the NARX model, an offset-free NMPC is designed and simulated under various dynamic scenarios. The developed NARX-based NMPC shows satisfactory control performance, stabilizing the CO2 capture rate faster than LMPC by 60-100 min. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license(https://***/licenses/by-nc-nd/4.0/)

关键词： Post-combustion CO(2)capture Dynamic simulation System identification nonlinear model predictive control

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nonlinear model predictive control for Optimal Motion Planning in Autonomous Race Cars 2nd

Nonlinear Model Predictive Control for Optimal Motion Planni...

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2nd modeling, Estimation and control Conference (MECC)

作者： Sivashangaran, Shathushan Patel, Darshit Eskandarian, Azim Virginia Tech Dept Mech Engn Blacksburg VA 24061 USA

Motorsport has historically driven automobile innovation by challenging the world's best car manufacturers to design, and develop vehicles that push limits of contemporary technology, and compete at physical vehicle limits. Autonomous driving is a rapidly evolving field that garners interest in industry, government, and research due to substantial improvements in road safety, and traffic flow. Autonomous racing is a byproduct of advancements in autonomous driving, and guarantees innovation in the field through the design of state-of-the-art perception, motion planning, and control algorithms developed to perform in fast-paced, multi-object environments at high speeds, operating at a vehicle's acceleration, and tire limits. We propose a high-level nonlinear model predictive control (NMPC) strategy incorporating a Pacejka tire model, and nonlinear vehicle dynamics in the global coordinate system with constraints based on track boundaries, and vehicle input limits for optimal motion planning to minimize lap time. The NMPC motion planner is evaluated in three real-world racetracks, Circuit of the Americas, Circuit de Spa-Francorchamps, and Autodromo Nazionale Monza for three race car classes, Formula 1 (F1), Le Mans Prototype (LMP1), and Grand Touring Endurance (GTE). The proposed NMPC strategy is shown to generate time-optimal trajectories for each vehicle class in the evaluated tracks, conforming to optimal racing lines demonstrated by professional racing drivers. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Autonomous Racing Autonomous Vehicles Motion Planning nonlinear model predictive control Vehicle Dynamics

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Isolated microgrid frequency stabilization through nonlinear model predictive control of a gas engine generator set

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INTERNATIONAL JOURNAL OF ENGINE RESEARCH 2025年第6期26卷 874-884页

作者： Riedler, Thomas Franz, Benjamin Kozek, Martin Huber, Johannes Bachler, Simon Jakubek, Stefan TU Wien Inst Mech & Mechatron Div Proc Control & Automat Getreidemarkt 9 A-1060 Vienna Austria INNIO Jenbacher GmbH & Co Jenbach Austria

Gas engine generator sets are applied in microgrids due to their capability to provide reliable, responsive and efficient distributed power generation, enhancing grid resilience and enabling the integration of renewable energy sources. This article proposes a methodology for stabilizing the frequency of an isolated microgrid subjected to a measurable disturbance by controlling the power of a premixed turbocharged natural gas engine. control difficulties of this task include strongly nonlinear dynamics, time delay in the air-fuel path and constrained operating conditions. Conventional control methods like proportional-integral control have difficulties solving these problems, and require extensive tuning efforts and gain scheduling to deliver acceptable performance, prompting adoption of advanced control strategies. The presented approach utilizes a cascaded control architecture with a nonlinear model predictive controller (NMPC) for high level control and engine specific low-level controllers for controlling the intake manifold pressure and air-fuel ratio. The NMPC captures the inherent nonlinear dynamics, accounts for an input delay and handles state-dependent constraints. The cascaded control architecture enables the usage of a comparably simple model for the NMPC design, reducing the computational cost and the parametrization effort. This additionally enables application of the high level controller for different gas engine types and allows design of the controller in a simple simulation environment prior to the experiments on the real engine. Experimental results highlight the NMPC's ability to control the engine at its physical limits over the entire load range without inducing frequency oscillations using just one parameter set. This emphasizes the robustness of the presented approach, making it a promising solution for real-world applications.

关键词： Generator set gas engine nonlinear model predictive control frequency control microgrid isolated operation time delay

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Trajectory tracking and tether tension control of tethered aerial vehicle using nonlinear model predictive control

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control ENGINEERING PRACTICE 2025年 162卷

作者： Prkacin, Vicko Palunko, Ivana Petrovic, Ivan Univ Dubrovnik Fac Elect Engn & Appl Comp Lab Intelligent Autonomous Syst LARIAT Cira Carica 4 Dubrovnik 20000 Croatia Univ Zagreb Fac Elect Engn & Comp Lab Autonomous Syst & Mobile Robot LAMOR Unska 3 Zagreb 10000 Croatia

Tethered unmanned aerial vehicles are an effective solution for applications demanding extended flight durations. However, these systems exhibit complex nonlinear dynamics and coupling effects, which are further amplified in scenarios where the ground component of the system - the Launch and Recovery System (LARS) is mounted on a mobile platform. In this study, the UAV has the task of following a user-defined trajectory while keeping the tether interaction force and the tether length within safe operating limits. This results in a nonlinear control problem that is subject to constraints. To solve this problem, a nonlinear model predictive control (NMPC) for the tethered aerial system is proposed in this paper. State estimation is achieved by capturing the tether interaction force using a minimal proprioceptive sensing system. It is further demonstrated that active tether force control can improve estimation accuracy. Finally, the proposed control and estimation strategies are implemented and validated experimentally on a UAV-LARS system.

关键词： Tethered aerial vehicle nonlinear model predictive control Coordinated control Minimal proprioceptive sensing

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Neural network-based nonlinear model predictive control with anti-dead-zone function for magnetic shape memory alloy actuator

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nonlinear DYNAMICS 2025年第2期113卷 1315-1332页

作者： Su, Liangcai Zhang, Chen Yu, Yewei Zhang, Xiuyu Su, Chun-Yi Zhou, Miaolei Jilin Univ Dept Control Sci & Engn Changchun 130022 Peoples R China Northeast Elect Power Univ Sch Automat Engn Jilin 132012 Jilin Peoples R China Concordia Univ Gina Cody Sch Engn & Comp Sci Montreal PQ H3G 1M8 Canada

Magnetic shape memory alloy-based actuator (MSMA-BA) has the advantages of large strain and high resolution. However, the inherent hysteresis characteristics accompanied by the dead zone in MSMA seriously degrade the positioning accuracy of MSMA-BA. In this study, a gated recurrent neural network (GRNN)-based nonlinear model predictive control (NMPC) method is designed to achieve precise trajectory tracking control of the MSMA-BA. First, a GRNN-based nonlinear auto-regressive moving average with exogenous inputs (NARMAX) model is designed to predict the various nonlinear characteristics of MSMA-BA. Based on the established model, an NMPC method with an anti-dead-zone function is designed. The introduced anti-dead-zone function enables the proposed NMPC algorithm to accelerate the response speed within the dead zone and prevents violent oscillations in the system. The ability of the NMPC to address the hysteresis characteristics accompanied by the dead zone is enhanced. Additionally, the convergence of the proposed NMPC method is analyzed using the Lyapunov stability theory. Extensive experiments are conducted on the MSMA-BA to validate the effectiveness of the proposed method.

关键词： Magnetic shape memory alloy Hysteresis nonlinear model Anti-dead-zone function Gated recurrent neural network nonlinear model predictive control

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