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distributed Stochastic Algorithm Based on Enhanced Genetic Algorithm for Path Planning of Multi-UAV Cooperative Area Search

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第8期24卷 8290-8303页

作者： Zheng, Jibin Ding, Minghui Sun, Lu Liu, Hongwei Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Dalian Maritime Univ Inst Informat Sci Technol Dept Commun Engn Dalian 16026 Peoples R China

Multiple unmanned aerial vehicle (Multi-UAV) cooperative area search is an important and effective means of intelligence acquisition and disaster rescue. Search path planning is a critical factor to improve multi-UAV search performance. Aiming at the search inefficiency resulting from insufficient cooperation between UAVs in existing researches, we present a novel distributed real-time search path planning method based on distributed model predictive control (DMPC) framework. Firstly, we formulate the overall search objective function in finite time domain, considering not only repeated searches, but also maintenance of connectivity and collision avoidance between UAVs. Secondly, we decompose the overall search objective function to establish a distributed constrained optimization problem (DCOP) model, so that all UAVs optimize the overall search objective by interacting with neighbors. Thirdly, aiming at the problem of falling into the local optima in existing algorithms, distributed stochastic algorithm based on enhanced genetic algorithm (DSA-EGA) is proposed to solve the established DCOP model. We design a point crossover operator and introduce anytime local search (ALS) framework that stores the global optimal solution explored. Finally, the simulation results of different benchmark problems demonstrate that the proposed DSA-EGA outperforms other state-of-the-art algorithms in terms of the quality of solution. The simulation results of cooperative area search problems illustrate that the established DCOP model improves the search efficiency by 7.7%, and DSA-EGA improves the search efficiency by 4.3% at least. In addition, we also verify that our method has high scalability.

关键词： Multi-UAV cooperative area search distributed model predictive control distributed constrained optimization problem distributed genetic algorithm

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Robust distributed MPC for disturbed nonlinear multi-agent systems based on a mixed differential-integral event-triggered mechanism

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2024年第1期36卷

作者： He, Ning Du, Jiawei Cheng, Fuan Xu, Zhongxian Xian Univ Architecture & Technol Sch Mech & Elect Engn Xian 710055 Peoples R China Xi An Jiao Tong Univ Minist Educ Key Lab Intelligent Networks & Network Secur Xian 710049 Peoples R China Xian Univ Posts & Telecommun Sch Automat Xian 710121 Peoples R China

The cooperation of multi -agent systems (MAS) based on distributed model predictive control (DMPC) is a current research hotspot, and how to more effectively handle additive disturbances and reduce resource consumption are two difficult problems that need to be solved. In view of this, this paper aims to design a more effective DMPC strategy for MAS, so as to effectively deal with the above problems without loss of the control performance. Firstly, a finite -horizon constrained optimization problem is established for MAS, in which a robustness constraint is designed to effectively handle additive disturbances. Then, a new efficient event -triggering condition is designed, which is established by considering the mixed information of differential and integral of the state error. Furthermore, based on the dual -mode control, an event -triggered robust DMPC algorithm is proposed to further reduce the resource consumption of the system. In addition, theoretical results are provided through analysis and deduction to ensure the iterative feasibility of the algorithm, stability of the closed -loop system and Zeno -free behavior. Finally, the proposed algorithm is applied to two examples for simulation and comparison to verify its effectiveness. The simulation results show that compared to DMPC and classical event -triggered DMPC methods, the proposed algorithm can reduce the average resource consumption of MAS by more than 81% and 36%, respectively, which indicates that the proposed strategy can effectively reduce the computation and communication burden of MAS without affecting the control performance.

关键词： Multi-agent systems distributed model predictive control Additive disturbance Event-triggering condition

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Coordinated Operation of Virtual Coupling High-Speed Trains Under a Novel Sequential model predictive control Framework

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UNMANNED SYSTEMS 2024年第2期12卷 359-373页

作者： Bai, Weiqi Zhang, Zixuan Wu, Xingtang Xiong, Yizhan Dong, Hairong Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

This paper deals with the coordinated control problem of the multiple high-speeds operating under the virtual coupling mode with special consideration of the coupled safety inter-train distance constraints and other independent constraints affecting train dynamics. Considering the inertial lag phenomenon within longitudinal dynamics, this study employs a third-order nonlinear control model to characterize train motion. To cope with the intricate running constraints explicitly, a centralized constrained model predictive (MPC) control problem is formulated for the regulation of the trains. Within the framework of sequential solving approach, a novel distributed model predictive control (DMPC) algorithm is designed based on a distributed train running information exchange mechanism. The algorithm decomposes the centralized MPC problem into a sequence of local problems, allowing for sequential computation and meeting the real-time control requirements. Notably, the algorithm mitigates solution conservatism arising from the distributed optimization mechanism by accounting for potential hypothetical control policies among a train's topological neighbors when designing its control policy. The feasibility and effectiveness of the proposed results are rigorously confirmed through theoretical analysis and illustrated by numerical experiment results.

关键词： Coordinated control multiple high-speed trains distributed model predictive control

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Multi-Rate Sampling control Design and Stability Analysis for Frequency and Voltage Regulation in Islanded Microgrids

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2023年第1期14卷 704-716页

作者： Feng, Kuo Liu, Chunhua City Univ Hong Kong Sch Energy & Environm Hong Kong Peoples R China

The wide use of inverters in microgrids motivates the development of digital controllers with sampling systems. This paper proposes a multi-rate sampling frequency and voltage regulation method for inverters-based distributed generators (DGs). The fast-sampling signals are adopted to achieve the primary control, which is composed of the VSG-based frequency control and the voltage droop control with a low-pass filter (LPF). Based on the unified primary control model, the low-sampling distributed model predictive control (DMPC) is proposed as secondary frequency and voltage control. Considering the microgrid network, the inverter-based DGs supply power for both the local load and loads on other buses. The proposed DMPC method can overcome the disproportionate impedances of interconnecting lines among buses. Then the frequency and voltage of islanded microgrid can be restored to rated values with accurate power sharing. The DMPC-based secondary control and the primary control have different sampling rates. The stability and dynamic performance of the proposed multi-rate sampling control system are investigated for the first time. Finally, both simulation and experiment tests are carried out to validate the effectiveness of the proposed control strategy.

关键词： distributed model predictive control islanded microgrid multi-rate sampling control system stability analysis virtual synchronous generator (VSG)

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Cooperative predictive control for arbitrarily mixed vehicle platoons with guaranteed global optimality

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第8期17卷 1702-1714页

作者： Zhan, Jingyuan Hua, Zhen Zhang, Liguo Beijing Univ Technol Fac Informat Technol Key Lab Computat Intelligence & Intelligent Syst Beijing Peoples R China Beijing Univ Technol Fac Informat Technol Key Lab Computat Intelligence & Intelligent Syst Beijing 100124 Peoples R China

This paper studies the cooperative control problem of a mixed vehicle platoon, which is composed of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs) in an arbitrary order. An alternating direction method of multipliers (ADMM) based distributed model predictive control (DMPC) algorithm is proposed for CAVs to lead the mixed vehicle platoon travelling in a string with anticipated inter-vehicle spacing and a desired velocity. First, the mixed vehicle platoon is divided into multiple interrelated sub-platoons with any two adjacent sub-platoons having a common CAV, and then a generic model is constructed for each sub-platoon based on the intelligent driver model for HDV and the kinematic model for CAV, respectively. Second, a local MPC controller is designed for each sub-platoon to optimize the control inputs of CAVs with the objective of minimizing the position and velocity errors of all vehicles in the sub-platoon, and then the ADMM is utilized to obtain the global optimal solution among local MPC controllers of all sub-platoons. Finally, numerical simulations and experiments are carried out to verify the effectiveness of the proposed DMPC algorithm, the results of which reveal that it can reduce the computation cost significantly and ensure the control performance comparable to the centralized MPC algorithm.

关键词： mixed vehicle platoon distributed model predictive control alternating direction method of multipliers

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Event-Triggered Tube-DMPC With Shrinking Ingredients for Vehicle Platoon Under Disturbance and Communication Delay

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第6期26卷 8467-8480页

作者： Luo, Qianyue Lam, James Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China Univ Hong Kong Shenzhen Inst Res & Innovat SIRI Shenzhen 518000 Peoples R China

This paper presents a novel event-triggered robust distributed model predictive control (DMPC) methodology with a shrinking prediction horizon and shrinking terminal region for vehicle platoon control in the presence of external disturbances and communication delays. Using the framework of tube-based MPC, an information transmission triggering mechanism is designed which is determined by control plan changes for the nominal leading vehicle, to reduce the communication frequency. Then the follower vehicles only update their control inputs, prediction horizons and corresponding terminal sets when they receive new information from the leader, so as to reduce the computational burden. The proposed approach effectively compensates the information errors caused by the communication delays by utilizing the timestamp information. When the leader's control plan changes during the delay period, the recursive feasibility of the local optimization problem and the uniform ultimate bound stability of the platoon system are guaranteed with the proposed controller. The numerical simulation demonstrates the superior performance and robustness of the proposed approach, which offers promising solutions for real-world platoon control systems.

关键词： Delays Event detection Stability analysis Resistance Optimization Vectors Topology Symmetric matrices Numerical stability Intelligent transportation systems Communication delay distributed model predictive control event-triggered control vehicle platoon

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Cooperative predictive control for Mixed Vehicle Platoons on Curved Roads 36

Cooperative Predictive Control for Mixed Vehicle Platoons on...

引用

36th Chinese control and Decision Conference (CCDC)

作者： Wang, Xinghan Zhan, Jingyuan Zhang, Liguo Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

This paper studies the cooperative control problem for a mixed vehicle platoon composed of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs) running on curved roads. Firstly, we propose a distributed model predictive control (DMPC) algorithm for each following CAV in the platoon to maintain a constant speed and a constant inter-vehicle travelling path length along the curved road, where delayed trajectories of preceding vehicles are utilized as the reference. Considering the fact that HDVs in the platoon are inevitably subject to uncertainty due to drivers' behaviors, simply following the preceding HDV's trajectory may lead to undesirable behaviors, and hence we introduce a trajectory correction mechanism based on centerline of the path to correct the vehicle trajectory reference in the DMPC algorithm. This enables the closed-loop mixed vehicle platoon control system to achieve the desired inter-vehicle traveling path length and desired speed on curved roads. Finally, numerical simulation results are presented to validate the effectiveness of the proposed DMPC algorithm for the mixed vehicle platoon.

关键词： Vehicle platooning Mixed vehicle platoon distributed model predictive control Curved road

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Dissipativity-Guaranteed distributed model predictive controller for Reconfigurable Large-Scale System

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2020年第4期39卷 1873-1895页

作者： He, Ye Li, Shaoyuan Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

This paper presents a distributed model predictive control strategy for reconfigurable large-scale systems to maintain global stability when there are subsystems joining in or leaving the system during closed-loop operation, which is also called the plug-and-play control. Dissipativity is an input-output property, and the overall dissipativity property of a large-scale system can be described by the 'linear' combination of the dissipativity properties of its subsystems based on their interconnection topology. The capacity of capturing interaction effects among subsystems on overall stability makes dissipative system theory a useful tool to design control system for reconfigurable large-scale system. The redesign is restricted to subsystems whose input-output property will be directly influenced by the topology change in order to allow quick operation. The overall stability condition can still be guaranteed if the dissipativity property of the redesigned subsystems does not spoil the overall dissipativity condition. Simulation results of a power network system with subsystems joining in and leaving the network show the effectiveness of the proposed strategy.

关键词： distributed model predictive control Reconfigurable large-scale systems Dissipativity

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Energy-Based Cooperative distributed MPC for Frequency control in Microgrids

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IEEE control SYSTEMS LETTERS 2023年 7卷 3441-3446页

作者： de Jong, Thomas Lazar, Mircea Eindhoven Univ Technol Dept Elect Engn NL-5612 AZ Eindhoven Netherlands

This letter develops an energy based cooperative distributed model predictive control (DMPC) scheme for frequency (or voltage) stabilization in microgrids. Firstly, we present a novel integral supply constraint based on Bregman energy functions that ensures equilibrium-independent asymptotic stability of nonlinear microgrid models. Secondly, we design DMPC controllers with integral supply constraints that cooperate towards achieving global microgrid stability via supply sharing. We compare the developed cooperative DMPC scheme with a non-cooperative DMPC scheme that uses passivity-type of constraints and becomes infeasible during a load change scenario. Compared with distributed averaging proportional integral controllers, the developed cooperative DMPC scheme yields shorter settling times (4s versus 18s) and much smaller overshoots. The developed energy based cooperative DMPC scheme results in supply sharing and cooperation among the different generators to achieve frequency stabilization, which is a very desirable property for emerging smart grids with distributed operation.

关键词： distributed model predictive control dissipation inequalities cooperative control energy based control frequency stabilization

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Data-based monitoring and reconfiguration of a distributed model predictive control system

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2012年第1期22卷 68-88页

作者： Chilin, David Liu, Jinfeng Davis, James F. Christofides, Panagiotis D. Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

In this work, we develop a data-based monitoring and reconfiguration system for a distributed model predictive control system in the presence of control actuator faults. Specifically, we first design fault detection filters and filter residuals, which are computed via exponentially weighted moving average, to effectively detect faults. Then, we propose a fault isolation approach that uses adaptive fault isolation time windows whose length depends on the rate of change of the fault residuals to quickly and accurately isolate actuator faults. Simultaneously, we estimate the magnitudes of the faults using a least-squares method and based on the estimated fault values, we design appropriate control system reconfiguration (fault-tolerant control) strategies to handle the actuator faults and maintain the closed-loop system state within a desired operating region. A nonlinear chemical process example is used to demonstrate the approach. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： data-based methods fault detection and isolation distributed model predictive control fault-tolerant control chemical process control

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