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nonlinear model predictive control of vehicle trajectory tracking using tilting technology

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Yao, Jialing Bai, Yanan Jia, Yunyi Nanjing Forestry Univ Coll Automobile & Traff Engn 159 Longpan Rd Nanjing 210037 Jiangsu Peoples R China Clemson Univ Dept Automot Engn Greenville SC USA

To enhance the performance of trajectory tracking in high-speed autonomous vehicles, this paper adopts a new technology for controlling the vehicle body to tilt toward the inside of a curve, known as "tilting technology." It achieves this tilt through an active suspension system that inclines the vehicle body toward the inside of the curve, thereby reducing or offsetting the torque generated by gravity with the torque produced by centrifugal force. This significantly improves the vehicle's handling stability and anti-rollover capability. Integrating this technology with active steering control, a nonlinear model predictive trajectory tracking controller has been designed. For this integrated controller, the Fiala lateral tire force model is used to establish a nonlinear vehicle model with steering-rolling dynamics, while a double-lane-change and single-lane-change tests are designed as the reference paths. To avoid the tilting angle of the vehicle body being too large to exceed the effective stroke of the suspension, a clipped ideal tilt angle is adopted as the desired tilting angle. Simulation verification is carried out to confirm the validity of the integrated trajectory tracking control. The proposed controller is compared with two other trajectory tracking controllers, the controller that takes zero rolling angle as the control target and the controller without rolling control. The results show that, compared with the latter two, the proposed trajectory tracking controller can ensure well tracking ability, meanwhile effectively improving the handling stability, anti-rollover capability, and occupant lateral ride comfort during trajectory tracking for high-speed unmanned vehicles.

关键词： unmanned vehicle nonlinear model predictive control tilting control trajectory tracking handling stability

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

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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

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Integrated guidance and control based on nonlinear model predictive control with simplified primal-dual neural network optimization

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第16期361卷

作者： Cui, Lei Zhen, Zhe Zhang, Kang Hu, Xijing Zuo, Zhiqiang Tianjin Univ Sch Elect & Informat Engn Tianjin Key Lab Intelligent Unmanned Swarm Technol Tianjin 300072 Peoples R China Beijing Inst Control & Elect Technol Beijing 100045 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

In this study, an integrated guidance control (IGC) approach based on nonlinear model predictive control (NMPC) is proposed for the problem of missile interception. It needs to be emphasized that, this approach does not require information of the target's acceleration. In order to achieve missile interception, an NMPC problem is obtained by choosing a suitable cost function with the physical constraints of the missile into account. Then the NMPC problem is transformed into a quadratic programming (QP) problem. The QP problem can then be converted into a solution problem for linear variational inequalities (LVIs), which is further solved by a simplified primal-dual neural network (SPDNN). The efficacy and robustness of the proposed control methodology are demonstrated by numerical simulation.

关键词： Integrated guidance and control nonlinear model predictive control Simplified primal-dual neural network Quadratic programming

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A double-layer Jacobi method for partial differential equation-constrained nonlinear model predictive control

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OPTIMAL control APPLICATIONS & METHODS 2024年第5期45卷 1971-1989页

作者： Deng, Haoyang Ohtsuka, Toshiyuki Kyoto Univ Grad Sch Informat Dept Syst Sci Kyoto Japan

This paper presents a real-time optimization method for nonlinear model predictive control (NMPC) of systems governed by partial differential equations (PDEs). The NMPC problem to be solved is formulated by discretizing the PDE system in space and time by using the finite difference method. The proposed method is called the double-layer Jacobi method, which exploits both the spatial and temporal sparsities of the PDE-constrained NMPC problem. In the upper layer, the NMPC problem is solved by ignoring the temporal couplings of either the state or costate (Lagrange multiplier corresponding to the state equation) equations so that the spatial sparsity is preserved. The lower-layer Jacobi method is a linear solver dedicated to PDE-constrained NMPC problems by exploiting the spatial sparsity. Convergence analysis indicates that the convergence of the proposed method is related to the prediction horizon. Results of a numerical experiment of controlling a heat transfer process show that the proposed method can be two orders of magnitude faster than the conventional Newton's method exploiting the banded structure of NMPC problems. This paper presents a real-time optimization method for nonlinear model predictive control (NMPC) of systems governed by partial differential equations (PDEs). The proposed method performs simple Jacobi-type iterations to make full use of the sparsities exist in both the spatial and temporal directions. The numerical example shows that the proposed method can be two orders of magnitude faster than the conventional structure-exploiting Newton's method. image

关键词： Gauss-Seidel iteration Jacobi iteration nonlinear model predictive control PDE systems real-time optimization

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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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nonlinear model predictive control for the industrial BioPower 5 CHP plant 27

Nonlinear model predictive control for the industrial BioPow...

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IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Kortela, Jukka Aalto Univ Dept Chem & Met Engn Sch Chem Engn PL 16100 FI-00076 Aalto Finland

ISBN: (数字)9781665499965

ISBN: (纸本)9781665499965

In the operation of a biopower plant, a crucial role is played by the residual oxygen content in the flue gas. The flue gas oxygen content, depending on the total air supply and on the fuel composition, provides the information needed to estimate the power developed by the combustion. Therefore, in control systems that operate power plants, the flue gas oxygen content is directly measured and represents a key feedback variable. The novel nonlinear model predictive control developed is based on the model of the BioPower 5 CHP plant which also includes the nonlinear model of the flue gas oxygen content. In addition, the fast response is achieved by regulating primary air flow. To verify the model, experiments were performed at a biopower plant, which utilizes BioGrate combustion technology to enable the use of wet biomass fuels with a moisture content as high as 65%. Then the nonlinear model predictive control was tested in the simulated environment. Finally, the results are presented, analyzed, and discussed.

关键词： NMPC nonlinear model predictive control biomass industrial process

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Improved nonlinear model predictive control with inequality constraints using particle filtering for nonlinear and highly coupled dynamical systems

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nonlinear ENGINEERING - modelING AND APPLICATION 2024年第1期13卷

作者： Ahsan, Muhammad Salah, Mostafa M. Silesian Tech Univ Dept Measurements & Control Syst PL-44100 Gliwice Poland Future Univ Egypt Elect Engn Dept Cairo 11835 Egypt

Motion planning and controller design are challenging tasks for highly coupled and nonlinear dynamical systems such as autonomous vehicles and robotic applications. nonlinear model predictive control (NMPC) is an emerging technique in which sampling-based methods are used to synthesize the control and trajectories for complex systems. In this study, we have developed the sampling-based motion planning algorithm with NMPC through Bayesian estimation to solve the online nonlinear constrained optimization problem. In the literature, different filtration techniques have been applied to extract knowledge of states in the presence of noise. Due to the detrimental effects of linearization, the Kalman filter with NMPC only achieves modest effectiveness. Moving horizon estimation (MHE), on the other hand, frequently relies on simplifying assumptions and lacks an effective recursive construction. Additionally, it adds another optimization challenge to the regulation problem that has to be solved online. To address this problem, particle filtering is implemented for Bayesian filtering in nonlinear and highly coupled dynamical systems. It is a sequential Monte Carlo method that involves representing the posterior distribution of the state of the system using a set of weighted particles that are propagated through time using a recursive algorithm. For nonlinear and strongly coupled dynamical systems, the novel sampling-based NMPC technique is effective and simple to use. The efficiency of the suggested method has been assessed using simulated studies.

关键词： nonlinear model predictive control motion planning based on samples Bayesian estimation constrained optimization problem linearization Monte Carlo sample

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Constrained nonlinear model predictive control of pH value in wet flue gas desulfurization process

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OPTIMAL control APPLICATIONS & METHODS 2023年第3期44卷 1523-1539页

作者： Li, Xiaoli Dong, Jiawei Wang, Kang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing Peoples R China Beijing Univ Technol Engn Res Ctr Digital Community Beijing Peoples R China

In wet flue gas desulfurization (WFGD) process, the pH value of the absorption tower slurry is a crucial factor to the efficiency of desulfurization system. Aiming at the nonlinearity and large lag of the pH change in WFGD process, a predictive control strategy based on Hammerstein-Wiener inverse model compensation is proposed. During the calculation of optimal control, an anti-model of Wiener nonlinearity unit is adopted to transform the output setting values and sampling values. Similarly in the control process, the controller output is applied to the actual controlled object after inverse transformation of the static nonlinear Hammerstein model. Through the above two inverse transformations, the controller output is identical with the input of linear link in the closed-loop system. In this article, the inverse model compensation method is utilized to transform nonlinear process control into linear system control, avoiding the large computation of nonlinear model optimization. Finally, the feasibility and effectiveness of the proposed scheme are verified by simulation.

关键词： Hammerstein-Wiener model inverse model compensation nonlinear model predictive control wet flue gas desulfurization

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nonlinear model predictive control for Enhanced Navigation of Autonomous Surface Vessels

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IFAC-PapersOnLine 2024年第18期58卷 296-302页

作者： Daniel Menges Trym Tengesdal Adil Rasheed Department of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway Department of Mathematics and Cybernetics SINTEF Digital Trondheim Norway

This article proposes an approach for collision avoidance, path following, and anti-grounding of autonomous surface vessels under consideration of environmental forces based on nonlinear model predictive control (NMPC). Artificial Potential Fields (APFs) set the foundation for the cost function of the optimal control problem in terms of collision avoidance and anti-grounding. Depending on the risk of a collision given by the resulting force of the APFs, the controller optimizes regarding an adapted heading and travel speed by additionally following a desired path. For this purpose, nonlinear vessel dynamics are used for the NMPC. To extend the situational awareness concerning environmental disturbances impacted by wind, waves, and sea currents, a nonlinear disturbance observer is coupled to the entire NMPC scheme, allowing for the correction of an incorrect vessel motion due to external forces. In addition, the most essential rules according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) are considered. The results of the simulations show that the proposed framework can control an autonomous surface vessel under various challenging scenarios, including environmental disturbances, to avoid collisions and follow desired paths.

关键词： nonlinear model predictive control Artificial Potential Fields nonlinear Disturbance Observer Autonomous Surface Vessels Collision Avoidance Anti-Grounding

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nonlinear model predictive control for Mitigating Epidemic Spread using a Partial Differential Equation Based Compartmental Dynamic model

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IFAC-PapersOnLine 2024年第28期58卷 366-371页

作者： Logan Street Deepak Antony David Chunyan Liu Shelley Ehrlich Manish Kumar Subramanian Ramakrishnan Department of Mechanical and Materials Engineering University of Cincinnati Cincinnati OH USA Cincinnati Children’s Hospital Medical Center Division of Biostatistics and Epidemiology University of Cincinnati College of Medicine OH USA Department of Mechanical and Aerospace Engineering University of Dayton Dayton OH USA

We present a nonlinear model predictive control (NMPC) framework for epidemic spread mitigation using a Partial Differential Equation (PDE) based Susceptible-Latent-Infected-Recovered (SLIR) epidemiological dynamic model. The spatio-temporal epidemic spread predictions of the model were numerically validated in our previous work using empirical COVID-19 data for Hamilton County, Ohio, employing a single-objective Genetic Algorithm (GA) for training model parameters. The validated model serves as the basis for the NMPC prediction and control framework developed to support the design of optimal Non-Pharmaceutical Interventions for spread mitigation. We consider a cost function comprising the infection spread density and the cost of applied control, with the latter representing socioeconomic effects. With a prediction horizon (T p ) of 30 days and a control horizon (T u ) of 15 days. The NMPC investigates a uniformly distributed control scheme across the entire spatial domain for three different time periods of the COVID-19 pandemic with distinct infection trends. In summary, the article presents one of the first efforts towards developing an NMPC framework based on a spatio-temporal epidemic dynamic model. The results provide an analytical basis for improved spread mitigation of future epidemics.

关键词： nonlinear model predictive control Partial Differential Equations Mathematical modeling Epidemic models Numerical Analysis Numerical Optimization

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