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On distributed model predictive control for vehicle platooning with a recursive feasibility guarantee

IFAC-PapersOnLine 2017年第1期50卷 7193-7198页

作者： Shi S. Lazar M. Eindhoven University of Technology Eindhoven MB 5600 Netherlands

This paper proposes a distributed model predictive control algorithm for vehicle platooning and more generally networked systems in a chain structure. The distributed models of the vehicle platoon are coupled through the input of the preceding vehicles. Using the principles of robust model predictive control, the coupled input is regarded as a disturbance and the robust model predictive control algorithm proposed by [Kerrigan, 2001] is employed for every vehicle. Based on this approach, a new distributed model predictive control algorithm is proposed by communicating the inputs among the vehicles, which enlarges the feasible regions of the local controllers while recursive feasibility still holds. © 2017

关键词： Constrained control distributed model predictive control Recursive feasibility Vehicle platooning

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Optimization Target Resetting distributed model predictive control for Accelerated Cooling Process

Optimization Target Resetting Distributed Model Predictive C...

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10th World Congress on Intelligent control and Automation (WCICA)

作者： Zheng, Yi Li, Shaoyuan Wang, Xiaobo Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ China Dept Automat Shanghai Peoples R China Baosteel Iron & Steel Co Ltd Ctr Res & Dev Shanghai 600 Peoples R China

ISBN: (纸本)9781467313988

In accelerated cooling (ACC) processes-large scale systems, since the starting temperature varies along plate, the cooling curves of plate-points are different to each other for obtaining the uniform characteristic of the whole plate. To satisfy the complicated control targets-the various cooling curves, and real-time control requirement, a novel method called Optimization Target Resetting based distributed model predictive control (OTR-DMPC) is proposed. The whole system is divided into many subsystems, and each subsystem is controlled by a local model predictive control (MPC) to decrease the computation cost. The neighborhood optimization is used to coordinate subsystem-based MPC to guarantee the global performance. Meanwhile, the optimization target of each subsystem-based MPC is recalculated in each control period according to the current starting temperature. The experimental results show the efficiency of the proposed method.

关键词： model predictive control distributed model predictive control Accelerated cooling Laminar cooling

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A Jacobi Decomposition Algorithm for distributed Convex Optimization in distributed model predictive control

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IFAC-PapersOnLine 2017年第1期50卷 4905-4911页

作者： Dang Doan M. Diehl M. Keviczky T. De Schutter B. Systems Control and Optimization Laboratory Department of Microsystems Engineering and Department of Mathematics University of Freiburg Germany Delft Center for Systems and Control Delft University of Technology Netherlands

In this paper we introduce an iterative distributed Jacobi algorithm for solving convex optimization problems, which is motivated by distributed model predictive control (MPC) for linear time-invariant systems. Starting from a given feasible initial guess, the algorithm iteratively improves the value of the cost function with guaranteed feasible solutions at every iteration step, and is thus suitable for MPC applications in which hard constraints are important. The proposed iterative approach involves solving local optimization problems consisting of only few subsystems, depending on the flexible choice of decomposition and the sparsity structure of the couplings. This makes our approach more applicable to situations where the number of subsystems is large, the coupling is sparse, and local communication is available. We also provide a method for checking a posteriori centralized optimality of the converging solution, using comparison between Lagrange multipliers of the local problems. Furthermore, a theoretical result on convergence to optimality for a particular distributed setting is also provided. © 2017

关键词： cooperative optimization algorithm distributed model predictive control distributed optimization Jacobi algorithm

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Formation control of Skid-Steered Vehicles Based on distributed model predictive control

Formation Control of Skid-Steered Vehicles Based on Distribu...

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International Conference on Autonomous Unmanned Systems (ICAUS)

作者： Wang, Yiping Li, Xueyuan Liu, Qi Li, Songhao Luan, Tian Li, Zirui Beijing Inst Technol Sch Mech Engn Beijing Peoples R China Delft Univ Technol Fac Civil Engn & Geosci Dept Transport & Planning Stevinweg 1 NL-2628 CN Delft Netherlands

ISBN: (纸本)9789819904815;9789819904792;9789819904785

The skid-steered vehicle has the advantages of simple structure and strong maneuverability. Its formation driving can effectively improve safety, reduce energy consumption and exert its benefits, and has wide application prospects in military and civilian fields. Differential skid steering has strong horizontal and vertical coupling characteristics, so the tracking performance of the vehicle is poor. Therefore, it is of great significance to study horizontal and vertical joint control. Firstly, the mathematical model of the vehicle platoon is established to realize the formation control of skid-steered vehicles. Then, a combined horizontal and vertical control strategy for skid-steered vehicle formation is proposed, and a distributed model predictive controller is designed. Finally, simulation experiments verified that the designed method has good feasibility and stability.

关键词： Skid-steered vehicle Formation driving Lateral and longitudinal control distributed model predictive control

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Numerical benchmarking of dual decomposition-based optimization algorithms for distributed model predictive control

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RESULTS IN control AND OPTIMIZATION 2024年 17卷

作者： Yfantis, Vassilios Wagner, Achim Ruskowski, Martin TU Kaiserslautern Chair Machine Tools & Control Syst Dept Mech & Proc Engn Gottlieb Daimler Str 42 D-67663 Kaiserslautern Germany German Res Ctr Artificial Intelligence Innovat Factory Syst Trippstadter Str 122 D-67663 Kaiserslautern Germany

This paper presents a benchmark study of dual decomposition-based distributed optimization algorithms applied to constraint-coupled model predictive control problems. These problems can be interpreted as multiple subsystems which are coupled through constraints on the availability of shared limited resources. In a dual decomposition-based framework the production and consumption of these resources can be coordinated by iteratively computing their prices and sharing them with the involved subsystems. Following a brief introduction to model predictive control different architectures and communication topologies for a distributed setting are presented. After decomposing the system-wide control problem into multiple subproblems by introducing dual variables, several distributed optimization algorithms, including the recently proposed quasi-Newton dual ascent algorithm, are discussed. Furthermore, an epigraph formulation of the bundle cuts as well as a line search strategy are proposed for the quasi-Newton dual ascent algorithm, which increase its numerical robustness and speed up its convergence compared to the previously used trust region. Finally, the quasi-Newton dual ascent algorithm is compared to the subgradient method, the bundle trust method and the alternating direction method of multipliers for a large number of benchmark problems. The used benchmark problems are publicly available on GitHub.

关键词： distributed model predictive control Dual decomposition Subgradient method ADMM Bundle method Quasi-Newton

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Robust distributed model predictive control Based Consensus of General Linear Multi-agent Systems

Robust Distributed Model Predictive Control Based Consensus ...

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IEEE International Symposium on Circuits and Systems (IEEE ISCAS)

作者： Zhan, Jingyuan Chen, Yangzhou Aleksandrov, Alexander Li, Xiang Beijing Univ Technol Coll Metropolitan Transportat Beijing Key Lab Transportat Engn Beijing 100124 Peoples R China St Petersburg State Univ St Petersburg Russia Fudan Univ Sch Informat Sci & Engn Adapt Networks & Control Lab Shanghai 200433 Peoples R China Fudan Univ Sch Informat Sci & Engn Res Ctr Smart Networks & Syst Shanghai 200433 Peoples R China

ISBN: (纸本)9781728103976

This paper investigates the robust distributed model predictive control (DMPC) based consensus problem of general linear discrete-time multi-agent systems subject to external disturbances. We first propose a robust DMPC based consensus algorithm, mainly relying on the DMPC based consensus of a nominal system whose state deviation from the actual state is bounded by an invariant set. We then prove the feasibility and robust consensus of the proposed algorithm. Finally, we present a numerical example to verify the theoretical results.

关键词： Robust consensus Multi-agent system distributed model predictive control disturbance

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Experimental results for distributed model predictive control applied to a water distribution system

Experimental results for distributed model predictive contro...

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IEEE International Symposium on Intelligent control (ISIC)

作者： Hentzelt, Sebastian Klingler, Andreas Graichen, Knut Univ Ulm Inst Measurement Control & Microtechnol D-89069 Ulm Germany

ISBN: (纸本)9781479974061

In this paper, experimental results are presented for a distributed model predictive control ( DMPC) scheme applied to a laboratory-scale water distribution system consisting of connected water tanks. The setup is an example of coupled dynamical systems whose modular structure makes them candidates for agent based distributed control methods. A set of low cost Raspberry Pi microcomputers connected via a common Ethernet-network is used for the implementation of the DMPC. The DMPC algorithm employs an augmented Lagrangian approach to solve the distributed optimal control problem (OCP) subject to nonlinear system dynamics in continuous-time form and input constraints. In this way, the performance and modular character of the DMPC as well as the individual runtime footprints of the communication and computation with respect to the overall runtime of the distributed algorithm are studied.

关键词： continuous time systems distributed algorithms distributed control local area networks microcomputers nonlinear dynamical systems optimal control predictive control tanks (containers) water supply DMPC algorithm Ethernet-network OCP augmented Lagrangian approach connected water tanks continuous-time form coupled dynamical systems distributed algorithm distributed model predictive control scheme distributed optimal control problem input constraints laboratory-scale water distribution system low cost Raspberry Pi microcomputers modular character modular structure nonlinear system dynamics Couplings Heuristic algorithms Optimization Storage tanks Topology Valves Water resources distributed model predictive control distributed algorithms MODULAR CONSTRUCTIONS Micro computer Continuous time systems Water distribution systems decentralised control Local Area Networks Open circuit potential Storage Tanks Nonlinear dynamical systems Heuristic algorithms predictive control VALVE GATE Dimyristoylphosphatidylcholine

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Online Trajectory Planning and Collision Avoidance for a Group of Robots Using distributed model predictive control 28th

Online Trajectory Planning and Collision Avoidance for a Gro...

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28th International Conference on Robotics in Alpe-Adria-Danube Region (RAAD)

作者： Costantini, Giuliano Rostami, Ramin Goerges, Daniel Univ Kaiserslautern Dept Elect & Comp Engn Erwin Schrodinger Str 12 D-67663 Kaiserslautern Germany

ISBN: (纸本)9783030196486;9783030196479

In this paper the trajectory planning problem for unicycle robots is studied. This problem is particularly difficult to solve online due to its inherently nonlinear and nonconvex structure. The first aim of the paper is to overcome these difficulties using model predictive control (MPC) when a single robot is considered. Several requirements must be fulfilled by the optimal trajectory, such as respecting physical limitations as well as track constraints. The second aim of the paper is to extend the results to a group of robots using distributed MPC providing formation control and inter-robot collision avoidance.

关键词： Autonomous vehicles Unicycle robots Trajectory planning Collision avoidance distributed model predictive control

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Automatic Generation control Design of Doubly-Fed Induction Generator Integrated Power System Based on distributed model predictive control 5

Automatic Generation Control Design of Doubly-Fed Induction ...

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5th International Conference on Power and Energy Technology, ICPET 2023

作者： Zhang, Jialin Wei, Ping Qin, Boyu Xi'an Jiaotong University The School of Electrical Engineering Xi'an China Northwest Branch of State Grid Corporation of China Xi'an China

ISBN: (纸本)9798350339673

With the large-scale grid integration of doubly-fed induction generator (DFIG), uncertainties also arise on the power generation side, which puts higher demands on the performance of frequency controllers. Based on model predictive control, this paper investigates the AGC strategy under the participation of DFIG to improve their secondary frequency regulation support capability. By combining control with optimization, an AGC controller is designed based on distributed model predictive control, which adapts to the system's uncertainties through rolling optimization and feedback correction and transforms the optimization problem and constraint conditions into standard quadratic forms for ease of solution. Finally, the frequency support capability of the proposed AGC controller is verified in three-area transmission system. © 2023 IEEE.

关键词： automatic generation control distributed model predictive control doubly-fed induction generator

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A Computationally Efficient Non-Cooperative distributed model predictive control Algorithm for Two Agent Systems 20

A Computationally Efficient Non-Cooperative Distributed Mode...

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20th International Conference on System controls and Computer Science 2015

作者： Maxim, Anca Lazar, Corneliu Caruntu, Constantin F. Gheorghe Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Iasi Romania

ISBN: (纸本)9781479917808

This paper presents a non -cooperative distributed model predictive control algorithm used in cyber-physical systems for two agent systems coupled through the inputs. Each agent computes the optimal input trajectory for its corresponding subsystem as the minimum of a local optimization problem. The input trajectory of the neighbor, which is used in the local optimization problem, is obtained based on the previous optimal control sequence and is received in a communication session. After that, the optimization problem is solved and the optimal input is sent to the process. This approach is computationally efficient because the communication between the two agents is reduced at minimum (only one session each sampling period) and the optimal input is obtained solving one optimization problem thus diminishing the overall computational time. The algorithm was implemented in Matlab and the obtained performances were compared with a cooperative distributed model predictive control strategy. Both methods were tested in simulation on a quadruple tanks process and the results recommend the noncooperative strategy which has similar results with less computational requirements.

关键词： distributed model predictive control non-cooperative and cooperative games two agents control system

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