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nonlinear model predictive control with set terminal constraint for safe robot motion planning via speed and separation monitoring

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control ENGINEERING PRACTICE 2025年 154卷

作者： Nurbayeva, Aigerim Rubagotti, Matteo Nazarbayev Univ Sch Engn & Digital Sci Dept Robot 53 Kabanbay Batyr Ave Astana 01000 Kazakhstan

This paper proposes a methodology for safely planning the motion of a robot manipulator sharing its workspace with a human operator. The motion of the robot is continuously re-planned via nonlinear model predictive control (NMPC), imposing the so-called speed and separation monitoring (SSM) condition to guarantee human safety. Contrary to previous works in the field, the NMPC algorithm is designed with an ellipsoidal terminal constraint, to enlarge the domain of attraction compared to the casein which a point terminal constraint was imposed. This is a very important aspect in real-world applications, allowing the robot to plan its motion from initial configurations that are relatively far from the goal point. Theoretical results are proved on recursive feasibility and closed-loop stability for both cases of NMPC with point and set terminal constraints, under the simplifying assumption of a static human. The effectiveness of the proposed approach is verified via numerical evaluation of the domain of attraction and with experiments on a UR5 manipulator.

关键词： nonlinear model predictive control Robot motion planning Human-robot workspace sharing

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nonlinear model predictive control Based on Disturbance Observer for Cross-Domain Unmanned Platform 17th

Nonlinear Model Predictive Control Based on Disturbance Obse...

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17th International Conference on Intelligent Robotics and Applications

作者： Yan, Zefeng Xu, Denan Lu, Liang Yang, Junyu Han, Bin Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan Peoples R China

ISBN: (纸本)9789819607976;9789819607983

To address the issue of trajectory tracking under dynamic wave disturbances, this study proposes a nonlinear model predictive control method based on disturbance observer (DO-NMPC). The disturbance observer performs online observation of the generalized disturbance forces encountered by the unmanned platform during its motion, estimating the loads caused by wave disturbances in real-time. This information is used for active compensation in the model predictive control algorithm, enabling the control system to actively suppress wave disturbances related to unmodeled dynamics that cannot be measured. Simulation tests were conducted to compare the performance of the proposed DO-NMPC control strategy with the LQR control strategy in tracking straight-line and rotational trajectories on the water surface under wave disturbances. The simulation results validate the robustness and advantages of the proposed control strategy.

关键词： Cross-domain unmanned platform Trajectory tracking nonlinear model predictive control Disturbance observer

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A nonlinear model predictive control for air suspension in hub motor electric vehicle

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2025年第2-3期239卷 564-585页

作者： Yu, Yi Li, Zhongxing Zhou, Yin Wang, Xue Jiangsu Univ Sch Automot & Traff Engn Zhenjiang Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China

The hub-motor electric vehicle (HM-EV) is considered as an ideal configuration for electric vehicles (EVs). However, the electromechanical coupling effect deteriorates HM-EV ride comfort, which limits its widespread application in EVs. In this study, the HM-EV dynamic system with air springs is proposed to intervene in vehicle attitude and ride comfort. The HM-EV dynamic model with air spring, considering the electromechanical coupling effect, is established and the test validation is investigated. Then quasi-infinite horizon nonlinear model predictive control (QIH NMPC) is designed to improve the longitudinal and vertical dynamic performance. The dynamic performance of passive suspension, air suspension based on QIH NMPC, air suspension based on MPC, and PID control receptively, are compared under several random road scenarios. Finally, the results indicated that the proposed control algorithm can improve ride comfort, reduce motor vibration, and improve longitudinal and vertical dynamic performance.

关键词： Air suspension HM-EV nonlinear model predictive control ride comfort

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A novel nonlinear model predictive control approach to minimize losses open-channel irrigation systems

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ISA TRANSACTIONS 2025年 159卷 278-292页

作者： Conde, Gregory Ocampo-Martinez, Carlos Quijano, Nicanor Univ Florida Indian River Res & Educ Ctr Ft Pierce Agr & Biol Engn Dept Gainesville FL 32611 USA Univ Politecn Cataluna Automatic Control Dept Barcelona Spain Univ Los Andes Sch Engn Bogota Colombia

Open-channel irrigation systems (OCIS) are widely recognized as the simplest, most widely adopted, and economically advantageous method of water transportation in agriculture. However, these systems often experience substantial water losses due to seepage and leaks, which are closely correlated with the channel levels. The control mechanisms commonly discussed for OCIS primarily emphasize preserving constant channel levels, leading to constant losses. In order to overcome this limitation, the present study offers a paradigm change in control objectives, thereby facilitating the management of channel levels and flow variability. The new objectives seek to address user water demands, mitigate channel water levels, prevent overflow instances, and limit ecological and infrastructural impacts. A request-based operational approach is utilized in implementing a nonlinear model predictive control (NMPC) technique. The NMPC employs a simple modeling framework that can effectively describe the behavior of OCIS in various operational settings. It also integrates a receding-horizon cost function to optimize water allocation and minimize water levels. The provided design of the control strategy includes a set of feasible conditions that ensure the operability and stability of the controlled system. The efficacy of the proposed control technique has been verified by experimentation on a well-established testbed documented in previous academic papers. This validation process has demonstrated a notable 50% decrease in water waste while simultaneously ensuring sufficient water supply to users.

关键词： Irrigation systems Water management Open-channel hydraulics nonlinear model predictive control Efficiency enhancement Hydraulic modeling

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Tracking long-distance power transmission line under uncertain wind fields by QUAV using nonlinear model predictive control

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PHYSICA SCRIPTA 2025年第4期100卷 045221-045221页

作者： Li, Kun Kang, Liping Han, Ying Leau, Yu-Beng Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Liaoning Peoples R China Univ Malaysia Sabah Fac Comp & Informat Jalan UMS Kota Kinabalu 88400 Sabah Malaysia

In long-distance power transmission line inspections, utilizing a quadrotor unmanned aerial vehicle (QUAV) to stably and safely track high-voltage power lines from a safe distance has been demonstrated as an effective method. However, due to the intricate 3D spatial configuration of the power lines and the challenges in accurately detecting them, maintaining a consistent and safe tracking distance between the drone and the power lines remains a significant challenge. Additionally, the variability of wind patterns in long-distance inspection areas further complicates the task of ensuring stable tracking. Therefore, considering both the kinematic constraints of the QUAV-gimbal camera system and the influence of dynamic wind fields, this study proposes a nonlinear model predictive controller (NMPC) based approach for QUAV power line tracking. Initially, the QUAV's target tracking control task is formulated as an optimization problem, incorporating stringent motion attitude constraints to establish a cost function for target tracking distance in 3D space. Subsequently, by integrating gimbal camera functionality, a field-of-view (FOV) cost model is developed to determine the QUAV's target point while accounting for wind disturbance factors. Through the implementation of the NMPC controller, continuous airborne tracking is achieved while mitigating adverse effects from time-varying wind fields. Ultimately, optimal control input commands are generated for use with both the QUAV and gimbal controllers. Simulation experiments validate that this method effectively enables the QUAV to track long-distance power lines within time-varying wind field environments, with high precision, efficiency, and robust tracking performance.

关键词： transmission line inspection quadrotor unmanned aerial vehicle target tracking time-varying wind field nonlinear model predictive control

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Deep Neural Network-based Approximation of nonlinear model predictive control: Applications to Truck-trailer control System

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2025年第2期23卷 510-519页

作者： Park, Suyong Nguyen, Duc Giap Jin, Yongsik Park, Jinrak Kim, Dohee Eo, Jeong Soo Han, Kyoungseok Hanyang Univ Automot Engn Seoul 04763 South Korea Kyungpook Natl Univ Mech Engn Daegu 41566 South Korea Elect & Telecommun Res Inst ETRI Robot IT Convergence Res Sect Daegu 42994 South Korea Hyundai Motor Co 18278 Namyang Eup Hwaseong South Korea

In this work, we demonstrate the efficiency of approximating nonlinear model predictive control (NMPC) using deep neural networks (DNN). We design an implicit NMPC for forward and backward motions of the truck trailer (TT) to handle complexity of nonlinear system dynamics. However, the high computational load of implicit MPC poses challenges for real-time implementation. To address this issue, we employ a DNN-based NMPC approximation to estimate parametric functions. As a result, the DNN-based NMPC approximation can mimic the optimal control policy of implicit MPC. Additionally, the average computation times for implicit NMPC and the DNN-based NMPC approximation in hardware-in-the-loop (HIL) tests are 36.541 ms and 0.031 ms, respectively.

关键词： Approximation deep neural network hardware-in-the-loop nonlinear model predictive control truck-trailer system

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Multi-Phase Optimal control Problems for Efficient nonlinear model predictive control With acados

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OPTIMAL control APPLICATIONS & METHODS 2025年第2期46卷 827-845页

作者： Frey, Jonathan Baumgaertner, Katrin Frison, Gianluca Diehl, Moritz Univ Freiburg Dept Microsyst Engn IMTEK Freiburg Im Breisgau Germany Univ Freiburg Dept Math Freiburg Im Breisgau Germany

Computationally efficient nonlinear model predictive control (NMPC) relies on elaborate discrete-time optimal control problem (OCP) formulations trading off accuracy with respect to the continuous-time problem and online computational burden. Such formulations, however, are in general not easy to implement within specialized software frameworks tailored to numerical optimal control. This article introduces a new multi-phase optimal control problem (MOCP) interface for the open-source software acados allowing to conveniently formulate such problems and generate fast solvers that can be used for NMPC. While multi-phase OCP formulations occur naturally in many applications, such as, for example, walking robots, this work focuses on MOCP formulations that can be used to efficiently approximate standard continuous-time OCPs in the context of NMPC. To this end, the article discusses advanced control parametrizations, such as closed-loop costing and piecewise polynomials with varying degree, as well as partial tightening and formulations that leverage models of different fidelity. An introductory example is presented to showcase the usability of the new interface. Finally, three numerical experiments demonstrate that NMPC controllers based on multi-phase formulations can efficiently trade off computation time and control performance.

关键词： nonlinear model predictive control optimal control real-time control

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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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