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nonlinear model predictive control-Based Guidance Algorithm for Quadrotor Trajectory Tracking with Obstacle Avoidance

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Journal of Systems Science & Complexity 2021年第4期34卷 1379-1400页

作者： ZHAO Chunhui WANG Dong HU Jinwen PAN Quan School of Automation Northwestern Poly technical UniversityXi'an 710072China

This paper studies a novel trajectory tracking guidance law for a quadrotor unmanned aerial vehicle(UAV)with obstacle avoidance based on nonlinear model predictive control(NMPC)*** augmenting a reference position trajectory to a reference dynamical system,the authors formulate the tracking problem as a standard NMPC design problem to generate constrained reference velocity commands for ***,concerning the closed-loop stability,it is difficult to find a local static state feedback to construct the terminal constraint in the design of NMPC-based guidance *** order to circumvent this issue,the authors introduce a contraction constraint as a stability constraint,which borrows the ideas from the Lyapunov’s direct method and the backstepping *** achieve the obstacle avoidance extension,the authors impose a well-designed potential field function-based penalty term on the performance *** the practical application,the heavy computational burden caused by solving the NMPC optimization problem online is alleviated by using the dynamical adjustment of the prediction horizon for the real-time ***,extensive simulations and the real experiment are given to demonstrate the effectiveness of the proposed NMPC scheme.

关键词： Backstepping input constraints nonlinear model predictive control obstacle avoidance quadrotor UAV trajectory tracking

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

GEOENERGY SCIENCE AND ENGINEERING

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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

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

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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

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Design of a tracking controller for autonomous articulated heavy vehicles using a nonlinear model predictive control technique

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART K-JOURNAL OF MULTI-BODY DYNAMICS 2024年第2期238卷 334-362页

作者： Sharma, Tarun He, Yuping Univ Ontario Inst Technol Dept Automot & Mechatron Engn Oshawa ON Canada Univ Ontario Inst Technol Dept Automot & Mechatron Engn Oshawa ON L1G 0C5 Canada

This article proposes a design of a tracking controller for autonomous articulated heavy vehicles (AAHVs) using a nonlinear model predictive control (NLMPC) technique. Despite economic and environmental benefits in freight transportation, articulated heavy vehicles (AHVs) exhibit poor directional performance due to their large sizes, multi-unit vehicle configurations, and high centers of gravity (CGs). AHVs represent a 7.5 times higher risk of traffic accidents than single-unit vehicles (e.g. rigid trucks, cars, etc.) in highway operations. Human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous driving control of AHVs. To increase the safety of AHVs, we design a novel NLMPC-based tracking controller for an AHV, that is, a tractor/semi-trailer combination, and this tracking controller is distinguished from others with the feature of controlling both the lateral and longitudinal motions for both the leading and trailing units. To design the tracking controller, a new prediction AHV model is developed, which represents both the lateral and longitudinal dynamics of the vehicle and captures its rearward amplification feature over high-speed evasive maneuvers. With the proposed tracking controller, the AAHV tracks the predefined reference path and follows a planned forward-speed scheme. Co-simulation demonstrates the effectiveness and robustness of the proposed NLMPC tracking controller.

关键词： Autonomous driving control tracking-controller design nonlinear model predictive control articulated heavy vehicles multi-body vehicle models co-simulations

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Enhanced Active Safety Through Integrated Autonomous Drifting and Direct Yaw Moment control via nonlinear model predictive control

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 4172-4190页

作者： Stano, Pietro Tavernini, Davide Montanaro, Umberto Tufo, Manuela Fiengo, Giovanni Novella, Luigi Sorniotti, Aldo Univ Surrey Ctr Automot Engn Guildford GU2 7XH England Univ Sannio Dept Engn I-82100 Benevento Italy Kineton Srl Kineton R&D I-80146 Naples Italy Politecn Torino Dept Mech & Aerosp Engn I-10129 Turin Italy

The introduction of active safety systems and advanced driver assistance systems has enhanced the control authority over the vehicle dynamics through specialized actuators, enabling, for instance, independent wheel torque control. During emergency situations, these systems step in to aid the driver by limiting vehicle response to a stable and controllable range of low longitudinal tire slips and slip angles. This approach makes vehicle behavior predictable and manageable for the average human driver;however, it is conservative in case of driving automation. In fact, past research has shown that exceeding the operational boundaries of conventional active safety systems enables trajectories that are otherwise unattainable. This paper presents a nonlinear model predictive controller (NMPC) for path tracking (PT), which integrates steering, front-to-total longitudinal tire force distribution, and direct yaw moment actuation, and can operate beyond the limit of handling, e.g., to induce drift, if this is beneficial to PT. Simulation results of emergency conditions in an intersection scenario highlight that the proposed solution provides significant safety improvements, when compared to the concurrent operation of PT algorithms and the current generation of vehicle stability controllers.

关键词： Force Wheels Safety Tires Torque Vehicle dynamics Roads Automated vehicles autonomous drifting active safety control allocation nonlinear model predictive control

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Computationally-efficient nonlinear model predictive control of wave energy converters with imperfect wave excitation previews

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OCEAN ENGINEERING 2025年 319卷

作者： Zhan, Siyuan Chen, Yutao Ringwood, John, V Trinity Coll Dublin Dept Mech Mfg & Biomed Engn Dublin D02 PN40 Ireland Fuzhou Univ Sch Elect Engn & Automat Fuzhou 350025 Peoples R China Maynooth Univ Ctr Ocean Energy Res Maynooth W23F2H6 Kildare Ireland

Energy maximising (EM) control of wave energy converters (WECs) is a noncausal problem, where wave prediction information can be used to increase the energy conversion rate significantly. However, current approaches do not consider the prediction error evolution in the control formulation process, leading to potential unpredictable performance degradation. Moreover, most existing real-time WEC control approaches assume linear dynamics, motivated by their simplicity and mild computational cost and, thus, are not effective for real-time control for WECs with nonlinear dynamics. Targeting imperfect wave prediction and nonlinear WEC dynamics, this paper proposes a computationally-efficient nonlinear MPC (NMPC) scheme for WECs with (typically) imperfect wave excitation preview. This is achieved by introducing an input move blocking scheme when formulating and solving the online optimisation problem, i.e., defining finer discretisation grids for the control input and wave prediction at the early stages of the prediction horizon, where the wave prediction is more accurate, and coarser grids at the latter stages of the horizon, to reflect less inaccurate wave prediction information. Numerical simulation results are presented, based on a conceptual nonlinear point-absorber WEC, to verify the efficacy of the proposed NMPC method, in terms of produced energy, computational complexity, and robustness against wave prediction inaccuracy.

关键词： nonlinear model predictive control Wave prediction Wave energy converter Reduced computational complexity

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nonlinear model predictive control based on K-step control invariant sets

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EUROPEAN JOURNAL OF control 2024年 80卷

作者： Zhao, Zhixin Girard, Antoine Olaru, Sorin Univ Paris Saclay CNRS CentraleSupelec Lab Signaux & Syst 3 Rue Joliot Curie F-91190 Gif Sur Yvette France

One of the fundamental issues in nonlinear model predictive control (NMPC) is to be able to guarantee the recursive feasibility of the underlying receding horizon optimization. In other terms, the primary condition for a safe NMPC design is to ensure that the closed-loop solution remains indefinitely within the feasible set of the optimization problem. This issue can be addressed by introducing a terminal constraint described in terms of a control invariant set. However, the control invariant sets of nonlinear systems are often impractical to use or even to construct due to their complexity. The K-step control invariant sets are representing generalizations of the classical one-step control invariant sets and prove to retain the useful properties for MPC design, but often with simpler representations, and thus greater applicability. In this paper, a novel NMPC scheme based on K-step control invariant sets is proposed. We employ symbolic control techniques to compute a K-step control invariant set and build the NMPC framework by integrating this set as a terminal constraint, thereby ensuring recursive feasibility.

关键词： nonlinear model predictive control Symbolic control Safety

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nonlinear model predictive control for trajectory-planning and tracking based on tilting technology to achieve vehicle obstacle avoidance

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VEHICLE SYSTEM DYNAMICS 2024年第12期62卷 3276-3296页

作者： Sun, Jiajia Yao, Jialing Jia, Yunyi Yao, Feifan Shi, Wenku Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing Peoples R China Clemson Univ Dept Automot Engn Greenville SC USA IBM Global Serv Dalian Co Ltd Shanghai Branch Shanghai Peoples R China Jilin Univ State Key Lab Automot Simulat & Control Changchun Peoples R China

High-speed autonomous vehicles can effectively improve the performance of obstacle-avoidance trajectory-planning and tracking control through integration with existing electronic control systems. However, relatively little research has been conducted in this field. This paper proposes a novel methodology of obstacle-avoidance trajectory-planning and tracking control based on active front-wheel steering integrated with tilting technology that can tilt the vehicle body toward the inside of the curve via active suspension when turning. The controller is designed using hierarchical control, in which the upper layer uses the point-mass vehicle model to design the trajectory-planning algorithm based on model predictive control. The lower layer uses the nonlinear vehicle model to design obstacle-avoidance tracking nonlinear model predictive control based on active steering integrated with tilt control. Then, the constrained nonlinear model predictive control problem is transformed into a constrained nonlinear programming problem, which is solved by sequential quadratic programming. Finally, simulations were performed using the CarSim/Simulink co-simulation platform. Two other hierarchical obstacle-avoidance tracking nonlinear model predictive controllers were designed as comparison objects. The simulation results show that the planning trajectory of the proposed integrated controller is closest to the obstacle. This controller effectively improves the vehicle obstacle-avoidance trajectory-tracking performance, handling stability, and maneuverability.

关键词： Autonomous vehicles nonlinear model predictive control trajectory-planning tilting technology CarSim/Simulink co-simulation

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Multi-AAV Cooperative Path Planning Using nonlinear model predictive control With Localization Constraints

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第8期25卷 9453-9466页

作者： Manoharan, Amith Sharma, Rajnikant Baliyarasimhuni, Sujit Univ Tartu Inst Technol EE-50090 Tartu Estonia Univ Cincinnati Aerosp Engn & Engn Mech Dept Cincinnati OH 45221 USA Indian Inst Sci Educ & Res Bhopal Bhopal 462066 India

In this paper, we solve a joint cooperative localization and path planning problem for a group of Autonomous Aerial Vehicles (AAVs) in GPS-denied areas using nonlinear model predictive control (NMPC). A moving horizon estimator (MHE) is used to estimate the vehicle states with the help of relative bearing information to known landmarks and other vehicles. The goal of the NMPC is to devise optimal paths for each vehicle between a given source and destination while maintaining desired localization accuracy. Estimating localization covariance in the NMPC is computationally intensive;hence, we develop an approximate analytical closed-form expression based on the relationship between covariance and path lengths to landmarks. Using this expression while computing NMPC commands reduces the computational complexity significantly. We present numerical simulations to validate the proposed approach for different numbers of vehicles and landmark configurations. We also compare the results with EKF and RRT* based methods to show the superiority of the proposed closed-form approach.

关键词： Location awareness Path planning Estimation Uncertainty Position measurement predictive control Laplace equations Path planning cooperative localization nonlinear model predictive control UAVs

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Neural critic learning with accelerated value iteration for nonlinear model predictive control

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NEURAL NETWORKS 2024年 176卷 106364页

作者： Xin, Peng Wang, Ding Liu, Ao Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

In practical industrial processes, the receding optimization solution of nonlinear model predictive control (NMPC) is always a very knotty problem. Based on adaptive dynamic programming, the accelerated value iteration predictive control (AVI-PC) algorithm is developed in this paper. Integrating iteration learning with the receding horizon mechanism of NMPC, a novel receding optimization solution pattern is exploited to resolve the optimal control law in each prediction horizon. Besides, the basic architecture and the specific form of the AVI-PC algorithm are demonstrated, including the relationship among the iterative learning process, the prediction process, and the control process. On this basis, the convergence and admissibility conditions are established, and the relevant properties are comprehensively analyzed when the accelerated factor satisfies the established conditions. Furthermore, the accelerated value iterative function is approximated through the single critic network constructed by utilizing the multiple linear regression method. Finally, the plentiful simulation experiments are conducted from various perspectives to verify the effectiveness and progressiveness of the AVI-PC algorithm.

关键词： Adaptive critic designs Accelerated mechanism nonlinear model predictive control Neural networks Value iteration

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