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STdi4DMPC: distributed model predictive control for Connected and Automated Truck Platoon With Mixed Traffic Flow Based on Spatiotemporal Trajectory Prediction

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第10期73卷 14563-14579页

作者： Li, Liyou Lyu, Hao Wang, Ting Cheng, Rongjun Ningbo Univ Fac Maritime & Transportat Ningbo 315211 Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Tongji Univ Coll Transportat Engn Shanghai 201804 Peoples R China

Trucking transportation has brought great convenience to social life, but it also faces many challenges. The rapid development of autonomous driving technology has the potential to alleviate some bottleneck issues and lead to new changes in truck transportation. Ensuring the high-quality operation of connected and automated trucks (CAT) and connected and automated trucks platoon (CATP) in mixed traffic flow has sparked a wave of research. With the goal of achieving efficient and stable longitudinal control of CATP in mixed traffic flow, this paper develops a novel data-driven longitudinal control framework for CATP based on trajectory prediction (STdi4DMPC). Firstly, a spatiotemporal dynamic attention model for trajectory prediction using a Transformer style architecture that integrates driving intentions (STdi) is constructed, capturing trajectory features of different dimensions and dependencies at different scales. Additionally, processing trajectories using empirical mode decomposition and wavelet denoising. Furthermore, a CATP longitudinal controller that conforms to the truck dynamics constraints based on distributed model predicted control (DMPC) is developed. Finally, the reconstituted noise-reduced trajectory of human-driven vehicle (HDV) is employed as a reference trajectory for DMPC, thereby achieving the integration of trajectory prediction model and longitudinal control algorithms. The simulation results based on the CitySim dataset are conducted and results demonstrate the superiority of the proposed longitudinal control strategy.

关键词： Trajectory predictive models Transformers Safety Long short term memory Spatiotemporal phenomena Prediction algorithms Connected automated truck platoon deep learning trajectory prediction longitudinal control distributed model predictive control

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Self-Triggered distributed model predictive control via Path Parameter Synchronization

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年第6期35卷 2027-2042页

作者： Chen, Qianqian Li, Shaoyuan Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Shanghai Peoples R China

This paper investigates the formation tracking problem for multiple mobile robots via self-triggered distributed model predictive control (DMPC) strategy and path-parameter communication manner. To ensure the robots follow the desired formation structure along the predefined paths, we establish appropriate tracking error models that form a multi-agent system. At triggered instants, each agent exchanges a sequence of path parameters representing the robot's position, resolves the optimal control problem (OCP) and subsequently determines the open-loop phase. Different from existing coordination methodology, the proposed scheme exhibits two essential merits in environments where resources are particularly limited: (1) The tracking task of robots is achieved by designing an appropriate OCP under the DMPC scheme, and the formation task of robots is achieved through the synchronization of one-dimensional path parameters instead of the multi-dimensional state information, which demands less communication load;(2) The incorporation of the self-triggered scheduler acquires the desired control performance with less calculation time, thereby relieving the computational and communication costs. Sufficient conditions are proposed to guarantee the recursive feasibility of the OCP and the closed-loop stability. Simulation results illustrate the validity of the proposed control algorithm.

关键词： distributed model predictive control formation tracking control multi-agent systems self-triggered control

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Motion planning of multiple unmanned vehicles using sequential convex programming-based distributed model predictive control

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年第1期35卷 141-167页

作者： Liu, Xiaoming Wu, Fuchun Deng, Yunshan Wang, Ming Xia, Yuanqing North Automat Control Technol Inst Taiyuan Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Zhongyuan Univ Technol Zhengzhou 450007 Peoples R China

This article tackles the complex challenge of multi-vehicle motion planning by presenting the novel Sequential Convex Programming-based distributed model predictive control (SCP-DMPC) algorithm. Existing methods often struggle with the nonconvex nature of optimization in multi-vehicle motion planning, frequently compromising on computational efficiency. SCP-DMPC innovatively combines the predictive control of DMPC with the optimization prowess of SCP to address these issues efficiently, while adhering to both physical and operational constraints. To ensure precise attainment of target poses, a three-stage control strategy is introduced, with theoretical analysis on its recursive feasibility and asymptotic stability, enhancing the reliability of the algorithm. Moreover, a heuristic-based deadlock resolution scheme is devised to prevent vehicle stalling, a common issue in cooperative motion. Validated through simulations in challenging scenarios, including symmetric position swapping and obstacle-laden formation transition, SCP-DMPC demonstrates superior adaptability, precision, and efficiency. These results underscore its potential for robust, real-time unmanned vehicle applications, suggesting new directions for future research and development in the field.

关键词： collision avoidance distributed model predictive control multi-vehicle motion planning sequential convex programming

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A robust distributed model predictive control strategy for Leader-Follower formation control of multiple perturbed wheeled mobile robotics

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EUROPEAN JOURNAL OF control 2025年 81卷

作者： Nguyen, Thanh Long Mai, Xuan Sinh Dao, Phuong Nam Hung Yen Univ Technol & Educ Hung Yen Vietnam Hanoi Univ Sci & Technol Sch Elect & Elect Engn Hanoi Vietnam

This article studies the formation and trajectory tracking control of multiple mobile robots with kinematic sub-systems. We proposed an effective design of robust distributed model predictive control (MPC) strategy for Leader-Follower formation control scheme in a group of multiple perturbed Wheeled Mobile Robotics (WMRs) with the consideration of tracking performance in not only the position but also the orientation, as well as the distance between the center and head in each WMR. Furthermore, the relation between formation control objective and non-holonomic property in each agent is also discussed. For the purpose of achieving the desired formation, according to trajectory of leader WMR, the barycentric of the formation requirement is known as corresponding virtual followers, and a distributed tube-MPC scheme is applied to each follower WMR for tracking a reference trajectory with not only the position but also its orientation. In addition, the stability and the performance tracking of multiple perturbed WMRs are investigated by employing Lyapunov stability theory with the indirect comparison method to be implemented by pointing out precisely the proposed terminal controller and the equivalent terminal region. Comprehensive simulation results in several scenarios demonstrate the validity of the proposed control scheme.

关键词： distributed model predictive control Leader-Follower formation control Wheeled mobile robotics (WMRs) Optimal control Lyapunov stability theory

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A Simple Demonstrator for Multi-agent Circular Formation control With Collision Avoidance Using distributed model predictive control

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2025年第2期23卷 360-369页

作者： Kim, Seongheon Pimentel, Guilherme Araujo Kim, Yoonsoo Vande Wouwer, Alain Gyeongsang Natl Univ Grad Sch Mech & Aerosp Engn Jinju 52828 South Korea Univ Mons Syst Estimat Control & Optimizat SECO B-7000 Mons Belgium

This paper presents the development of a didactic robotic demonstrator to study the circular formation problem (CFP) of multi-agent systems based on distributed model predictive control (DMPC). CFP can arise in various fields, such as robotics, drones, and space applications. This study proposes a method for multiple agents to sequentially enter a circular orbit, one by one, and move along the orbit at a predefined speed while maintaining a desirable distance from each other. Due to the advantages of DMPC, the proposed method allows each agent to independently generate control inputs while considering various types of constraints related to collision avoidance with the other agents. The methodology is tested and validated with three differential drive robots, i.e., Turtlebots.

关键词： Circular formation problem collision avoidance distributed model predictive control multi-agent control

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Iterative distributed model predictive control for nonlinear systems with coupled non-convex constraints and costs

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2024年第11期34卷 7220-7244页

作者： Wu, Jinxian Dai, Li Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

This paper proposes a distributed model predictive control (DMPC) algorithm for dynamic decoupled discrete-time nonlinear systems subject to nonlinear (maybe non-convex) coupled constraints and costs. Solving the resulting nonlinear optimal control problem (OCP) using a DMPC algorithm that is fully distributed, termination-flexible, and recursively feasible for nonlinear systems with coupled constraints and costs remains an open problem. To address this, we propose a fully distributed and globally convergence-guaranteed framework called inexact distributed sequential quadratic programming (IDSQP) for solving the OCP at each time step. Specifically, the proposed IDSQP framework has the following advantages: (i) it uses a distributed dual fast gradient approach for solving inner quadratic programming problems, enabling fully distributed execution;(ii) it can handle the adverse effects of inexact (insufficient) calculation of each internal quadratic programming problem caused by early termination of iterations, thereby saving computational time;and (iii) it employs distributed globalization techniques to eliminate the need for an initial guess of the solution. Under reasonable assumptions, the proposed DMPC algorithm ensures the recursive feasibility and stability of the entire closed-loop system. We conduct simulation experiments on multi-agent formation control with non-convex collision avoidance constraints and compare the results against several benchmarks to verify the performance of the proposed DMPC method.

关键词： distributed model predictive control non-convex constraints sequential quadratic programming

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Real-Time Train Scheduling With Uncertain Passenger Flows: A Scenario-Based distributed model predictive control Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第5期25卷 4219-4232页

作者： Liu, Xiaoyu Dabiri, Azita Wang, Yihui De Schutter, Bart Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands Beijing Jiaotong Univ State Key Lab Adv Rail Autonomous Operat Beijing 100044 Peoples R China

Real-time train scheduling is essential for passenger satisfaction in urban rail transit networks. This paper focuses on real-time train scheduling for urban rail transit networks considering uncertain time-dependent passenger origin-destination demands. First, a macroscopic passenger flow model we proposed before is extended to include rolling stock availability. Then, a distributed-knowledgeable-reduced-horizon (DKRH) algorithm is developed to deal with the computational burden and the communication restrictions of the train scheduling problem in urban rail transit networks. For the DKRH algorithm, a cost-to-go function is designed to reduce the prediction horizon of the original model predictive control approach while taking into account the control performance. By applying a scenario reduction approach, a scenario-based distributed-knowledgeable-reduced-horizon (S-DKRH) algorithm is proposed to handle the uncertain passenger flows with an acceptable increase in computation time. Numerical experiments are conducted to evaluate the effectiveness of the developed DKRH and S-DKRH algorithms based on real-life data from the Beijing urban rail transit network. The simulation results indicate that DKRH can be used to achieve real-time train scheduling for the urban rail transit network, while S-DKRH can handle the uncertainty in the passenger flows with an acceptable sacrifice in computation time.

关键词： Urban rail transit networks time-dependent passenger origin-destination demands uncertain passenger flows distributed model predictive control scenario approach

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distributed model predictive control for nonholonomic multivehicle formation tracking

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2021年第18期31卷 8961-8973页

作者： Liu, Kun Zhang, Qirui Han, Dongyu Ma, Aoyun Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

A distributed model predictive control strategy is proposed for the formation tracking problem of nonholonomic multivehicle systems, where each vehicle is modeled as a unicycle and is affected by disturbances. We design the optimization problems that followers need to solve, and the cooperation among the followers are achieved by the coupling costs between the followers. Meanwhile, the tightened constraints are used to ensure the actual tracking errors satisfy the constraints. The recursive feasibility of the optimization problems and the closed-loop stability of the tracking error systems are established. Finally, the effectiveness of the proposed strategy is verified via an example which contains three followers.

关键词： distributed model predictive control formation tracking nonholonomic multivehicle systems unicycle model

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Asynchronous distributed model predictive control for Multi-Agent Systems

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IEEE ACCESS 2025年 13卷 9592-9605页

作者： Mi, Xiaoxiao Liao, Yuanjiang Zeng, Hongzheng Ningbo Univ Technol Robot Inst Ningbo 315211 Peoples R China Civil Aviat Flight Univ China Key Lab Flight Tech & Flight Safety Civil Aviat Adm China Mianyang 621052 Peoples R China

This paper primarily focuses on the asynchronous cooperation control problem of multi-agent systems with external disturbances. Here, all agents take control actions, such as sampling measurements, computing control inputs, and exchanging information based on their individual clocks rather than a global synchronized clock. Owing to the asynchronous nature, the information of each agent received by all its neighbors at the same time instant may be different and deviate from what this agent predicts, potentially resulting in poor decision-making and even instability. To ensure stability, dynamic compatibility constraints related to all the uncertain deviations are established and incorporated into the optimization problem. Additionally, the constraint restriction method is employed to satisfy physical constraints and optimize performance in the presence of external perturbations. Then, an asynchronous distributed model predictive control with a dual-mode strategy is developed and sufficient conditions on the design parameters are constructed to ensure recursive feasibility and system stability. Finally, simulation experiments on a multiple-vehicle system with asynchronous control actions are carried out to validate the theoretical results.

关键词： Multi-agent systems Optimization Formation control Vectors predictive control Clocks Trajectory Synchronization Sufficient conditions Stability criteria Asynchronous sampling cooperative control distributed model predictive control formation control multi-agent system

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A distributed model predictive control Strategy for Constrained Multi-Agent Systems: The Uncertain Target Capturing Scenario

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第2期21卷 1549-1563页

作者： Fedele, Giuseppe Franze, Giuseppe Univ Calabria DIMES Arcavacata Di Rende Italy

In this paper, a distributed control architecture is presented for addressing the target capturing problem of a multi-agent system whose dynamics is described by double integrator models subject to bounded disturbance effects. Starting from novel kinematic models used as reference trajectories, the aim consists in driving the multi-agent system within the containment region without entering the distancing one and, whenever necessary, there remaining confined. The underlying control problem has been tackled by means of model predictive control arguments. In particular, two different distributed strategies have been developed, and adequately switched to each other, in order to guarantee constraint satisfaction within the capturing region despite any disturbance realization. Note to Practitioners-This paper proposes a methodological solution for dealing with the capturing problem for multi-agent systems operating in uncertain environments where the target is defined by the ring resulting from the distancing and containment ellipsoids. Differently from the existing literature, the proposed approach combines into a unique framework properties coming from potential fields theory and distributed model predictive control philosophy. It is interesting to put in light that the underlying control strategy allows the users to face surveillance and rescue operations, to cite a few, in computational affordable way due to the required memory resources because most of computations can be straightforwardly moved in the off-line phase.

关键词： Multi-agent systems Trajectory control systems Switches predictive models Kinematics Aerospace electronics target capturing distributed model predictive control

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