检索结果-内蒙古大学图书馆

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

nonlinear model predictive control method for high-speed helicopter power system based on integrated onboard model

引用

AEROSPACE SCIENCE AND TECHNOLOGY 2025年 161卷

作者： Song, Jie Chen, Yu Li, Wenbo Wang, Yong Zhang, Haibo Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Jiangsu Prov Key Lab Aerosp Power Syst Nanjing 210016 Peoples R China China Helicopter Res & Dev Inst Jingdezhen 333001 Jiangxi Peoples R China

To mitigate severe fluctuations in engine power turbine speed caused by changes in coaxial rotors, propellers, and aero-surfaces during the mode transition in coaxial high-speed helicopter (CHH), this paper presents a nonlinear model predictive control (NMPC) method for the CHH power system based on an integrated onboard model. Firstly, a digital simulation framework is deployed, incorporating a CHH onboard model based on a T-S fuzzy model and an onboard composite model of variable speed turboshaft engine based on a stacked Long ShortTerm Memory-State Variable model (LSTM-SVM). Subsequently, a nonlinear model predictive control method is devised for the CHH power system. By integrating flight prediction data from the integrated onboard model, an optimized objective function is formulated, taking into account both speed control objectives and the dynamic response characteristics of the turboshaft engine's output shaft. Through rolling optimization and feedback correction methods, real-time optimized control parameters for the turboshaft engine are obtained, ensuring rapid responsiveness in the engine control system. Simulation results demonstrate that the developed integrated onboard model accurately represents the variations in performance parameters during high-speed helicopter flight. Additionally, the nonlinear model predictive control law effectively tracks the variable speed reference commands of the power turbine, maintaining a maximum power turbine speed fluctuation of under 0.46%, thereby significantly enhancing both the engine's response and control quality while preserving computational real-time performance.

关键词： Coaxial high-speed helicopter Turboshaft engine Integrated onboard model nonlinear model predictive control

来源：评论

学校读者我要写书评

暂无评论

nonlinear model predictive control of Tiltrotor Quadrotors using Feasible control Allocation

引用

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2025年第2期111卷 1-16页

作者： Shayan, Zeinab Cristobal, Jann Izadi, Mohammadreza Yazdanshenas, Amin Naderi, Mehdi Faieghi, Reza Toronto Metropolitan Univ Dept Aerosp Engn Autonomous Vehicles Lab 350 Victoria St Toronto ON M5B2K3 Canada Tech Univ Crete Sch Prod Engn & Management Khania Greece

This paper presents a new flight control framework for tiltrotor multirotor uncrewed aerial vehicles (MRUAVs). Tiltrotor designs offer full actuation but introduce complexity in control allocation due to actuator redundancy. We propose a new approach where the allocator is tightly coupled with the controller, ensuring that the control signals generated by the controller are feasible within the vehicle actuation space. We leverage nonlinear model predictive control (NMPC) to implement the above framework, providing feasible control signals and optimizing performance. This unified control structure simultaneously manages both position and attitude, which eliminates the need for cascaded position and attitude control loops. Extensive numerical experiments demonstrate that our approach significantly outperforms conventional techniques that are based on Linear Quadratic Regulator (LQR) and Sliding Mode control (SMC), especially in high-acceleration trajectories and disturbance rejection scenarios, making the proposed approach a viable option for enhanced control precision and robustness, particularly in challenging missions.

关键词： Uncrewed aerial vehicles Quadrotor Tiltrotor quadrotor nonlinear model predictive control control allocation

来源：评论

学校读者我要写书评

暂无评论

Digital twin syncing for autonomous surface vessels using reinforcement learning and nonlinear model predictive control

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-18页

作者： Berg, Henrik Stokland Menges, Daniel Tengesdal, Trym Rasheed, Adil Norwegian Univ Sci & Technol Dept Engn Cybernet Trondheim Norway SINTEF Digital Dept Math & Cybernet Trondheim Norway

Current control systems for autonomous surface vessels (ASVs) often disregard model uncertainties and the need to adapt dynamically to varying model parameters. This limitation hinders their ability to ensure reliable performance under complex and frequently changing maritime conditions, highlighting the need for more adaptive and robust approaches. Therefore, this study introduces an innovative approach that integrates deep reinforcement learning (DRL) with nonlinear model predictive control (NMPC) to optimize the control performance and model parameters of ASVs. The primary objective is to ensure that the digital twin of the ASV remains continuously synchronized with its physical counterpart, thereby enhancing the accuracy, reliability, and adaptability of the digital twin in representing the vessel under complex and dynamic maritime conditions. Leveraging the capabilities of digital twins, agents can be trained in safety-critical applications within a risk-free virtual environment, minimizing the hazards associated with real-world experimentation. The DRL framework optimizes NMPC by tuning its parameters for peak performance and identifying unknown model parameters in real-time, ensuring precise and dependable vessel control. Extensive simulations confirm the effectiveness of this approach in improving the safety, efficiency, and reliability of ASVs. The proposed methods address critical challenges in ASV control by enhancing reliability and adaptability under dynamic conditions, providing a foundation for future advancements in autonomous maritime navigation and control system development.

关键词： Deep reinforcement learning nonlinear model predictive control Autonomous surface vessel Parameter optimization model identification

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

nonlinear model predictive control based on fuzzy wavelet neural network and chaos optimization

引用

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第5期25卷 3569-3577页

作者： Davanipour, Mehrnoush Asadipooya, Hanieh Shiraz Univ Sch Elect Engn Shiraz Iran

In this paper a combined controller is proposed for nonlinear dynamical systems. The controller is constructed by a fuzzy wavelet network and nonlinear model predictive control. Chaotic optimization, which is fast and robust, is applied to generate optimized controlled input in nonlinear model predictive control. The ability of the fuzzy wavelet neural network and the proposed controller is shown by simulation. It is illustrated that the proposed method is able to increase the speed of tracking in addition to having very little steady state error. Using chaotic optimization makes the controller robust in the presence of noise.

关键词： nonlinear model predictive control fuzzy wavelet neural network chaos optimization

来源：评论

学校读者我要写书评

暂无评论

nonlinear model predictive control based on the moving horizon state estimation for the solid oxide fuel cell

引用

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2008年第9期33卷 2355-2366页

作者： Zhang, X. W. Chan, S. H. Ho, H. K. Li, Jun Li, Guojun Feng, Zhenpiny Xi An Jiao Tong Univ Sch Energy & Power Engn Shaanxi 710049 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Fuel Cells Strateg Res Programme Singapore 639798 Singapore

As a nonlinear power generation device, the solid oxide fuel cell (SOFC) often operates under small window of operating conditions due to the constraints stemming from the environmental and safety considerations. The nonlinear model predictive control (NMPC) appears to be well suited control algorithm for this application. NMPC is a closed-loop feedback control scheme that predicts the open-loop optimal input based on the measurements and the setting trajectory. This work aims to develop a closed-loop feedback control strategy based on the NMPC controller for a planar SOFC. The current density, fuel and air molar flow rates are chosen as manipulated variables to control the output power, fuel utilization and temperature. The mole fraction and temperature of the exit gases are set as state variables, which can be estimated from the moving horizon estimation (MHE) method. The validation here is referred to robustness and stability of the controller, a typical case study has been conducted with the power output changes under constant fuel utilization and temperature. Simulation results show that the noise of the output is successfully filtered by the MHE. The NMPC controller works satisfactorily following the setting output trajectory. (c) 2008 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.

关键词： solid oxide fuel cell nonlinear model predictive control moving horizon state estimation

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

nonlinear model predictive control of supercavitating vehicle based on memory effect

引用

OCEAN ENGINEERING 2024年 299卷

作者： Han, Yuntao Du, Xulin Wei, Yanhui Sun, Jie Li, Yakun Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

This paper presents a predictive control strategy based on nonlinear model predictive control (NMPC) for a supercavitating vehicle system with high coupling nonlinearity and model uncertainty. By considering the cavity memory effect and introducing a cavity axis correction model, a nonlinear time -varying dynamic model of the vehicle is established to derive the time -varying tail efficiency and nonlinear planing force models. Based on the model, a predictive controller based on the nonlinear characteristics of the vehicle is designed. The controller can solve a finite horizon optical control problem (FHOCP) online for the updated system state in each system iteration to find the optimal solution at a given time. In addition, the controller introduces a Lyapunov terminal cost function and terminal constraint for the system state to reduce the system's conservativeness, which can improve the control performance of the system. The simulation results verify that the controller exhibits good stability and robustness against external interference and parameter perturbation.

关键词： Supercavitating vehicle Memory effect nonlinear model predictive control Trajectory tracking

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：