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ALADIN-α-An open-source MATLAB toolbox for distributed non-convex optimization

OPTIMAL control APPLICATIONS & METHODS 2022年第1期43卷 4-22页

作者： Engelmann, Alexander Jiang, Yuning Benner, Henrieke Ou, Ruchuan Houska, Boris Faulwasser, Timm Karlsruhe Inst Technol Inst Automat & Appl Informat Karlsruhe Germany ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China TU Dortmund Univ Inst Energy Syst Energy Efficiency & Energy Econ Emil Figge Str 70 D-4227 Dortmund Germany

This article introduces an open-source software for distributed and decentralized non-convex optimization named ALADIN-alpha. ALADIN-alpha is a MATLAB implementation of tailored variants of the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm. It is user interface is convenient for rapid prototyping of non-convex distributed optimization algorithms. An improved version of the recently proposed bi-level variant of ALADIN is included enabling decentralized non-convex optimization with reduced information exchange. A collection of examples from different applications fields including chemical engineering, robotics, and power systems underpins the potential of ALADIN-alpha.

关键词： ADMM ALADIN decentralized optimization distributed model predictive control distributed optimization nonconvex optimization optimal power flow

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Decomposition of Nonconvex Optimization via Bi-Level distributed ALADIN

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2020年第4期7卷 1848-1858页

作者： Engelmann, Alexander Jiang, Yuning Houska, Boris Faulwasser, Timm Karlsruhe Inst Technol Inst Automat & Appl Informat D-76131 Karlsruhe Germany Shanghai Tech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China TU Dortmund Univ Dept Elect Engn & Informat Technol D-44227 Dortmund Germany

Decentralized optimization algorithms are of interest in different contexts, e.g., optimal power flow or distributed model predictive control, as they avoid central coordination and enable decomposition of large-scale problems. In case of constrained nonconvex problems, only a few algorithms are currently available-often with limited performance or lacking convergence guarantee. This article proposes a framework for decentralized nonconvex optimization via bi-level distribution of the augmented Lagrangian alternating direction inexact Newton (ALADIN) algorithm. Bi-level distribution means that the outer ALADIN structure is combined with an inner distribution/decentralization level solving a condensed variant of ALADIN's convex coordination quadratic program (QP) by decentralized algorithms. We provide sufficient conditions for local convergence while allowing for inexact decentralized/distributed solutions of the coordination QP. Moreover, we show how decentralized variants of conjugate gradient and alternating direction of multipliers method (ADMM) can be employed at the inner level. We draw upon examples from power systems and robotics to illustrate the performance of the proposed framework.

关键词： Alternating direction of multipliers method (ADMM) augmented Lagrangian alternating direction inexact Newton (ALADIN) conjugate gradient (CG) decentralized optimization decomposition distributed model predictive control distributed optimal power flow distributed optimization

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A Joint Design of Platoon Communication and control Based on LTE-V2V

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第12期69卷 15893-15907页

作者： Hong, Chunhua Shan, Hangguan Song, Meiyan Zhuang, Weihua Xiang, Zhiyu Wu, Yingxiao Yu, Xiaoli Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc & Commun Netw Hangzhou 310027 Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Zhejiang Lab Intelligent Network Res Ctr Hangzhou 310000 Peoples R China Zhejiang Univ Coll Energy Engn Hangzhou 310027 Peoples R China

Recently, vehicle platooning has attracted a lot of attention as one of the promising solutions to the explosion of vehicle numbers. By exchanging information among vehicles through communication networks, vehicle platooning can significantly improve traffic safety and efficiency. In this paper, we develop a joint systematic design of platoon communication and control to reduce position errors of consecutive vehicles and to improve platoon safety. Through separate information dissemination of the platoon leader and followers, we improve the success probability of the platoon leader's information dissemination. To extend the communication range of the platoon leader and also the platoon scale, we propose to use relays to forward the platoon leader's messages. Further, an adaptive distributed model predictive control (DMPC) based controller is presented, which can adjust its control parameters according to platoon state and dynamic information sources. Simulation results not only verify the proposed systematic design in terms of position errors, but also show that our scheme performs well in vehicle failure cases where collisions can be avoided and platoon safety improved.

关键词： Safety Relays Communication networks Network topology Topology Stability analysis Kinetic theory distributed model predictive control intra-platoon communications vehicle platooning vehicular network

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A novel bi-level distributed dynamic optimization method of ship fleets energy consumption

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OCEAN ENGINEERING 2020年 197卷 106802-106802页

作者： Wang, Kai Li, Jiayuan Yan, Xinping Huang, Lianzhong Jiang, Xiaoli Yuan, Yupeng Ma, Ranqi Negenborn, Rudy R. Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China Delft Univ Technol Fac Mech Maritime & Mat Engn Mekelweg 2 NL-2628 CD Delft Netherlands MOST Natl Engn Res Ctr Water Transport Safety WTSC Wuhan 430063 Peoples R China Wuhan Univ Technol Sch Energy & Power Engn Reliabil Engn Inst Wuhan 430063 Peoples R China Univ Cambridge Dept Engn Cambridge CB3 0FA England

The optimization of ship energy consumption is attracting a great deal of attention, as societies seek to save energy and reduce emissions. Shipping companies are more concerned with the energy consumption of a ship fleet, as opposed to that of a single ship. Because the energy consumption of a fleet is influenced by multiple factors including environmental factors, port operations and transport demands, an improvement in a single ship's energy consumption does not necessarily mean that the overall energy consumption of a fleet is good. In addition, those factors are usually varying over time, making it hard to optimize the fleet's energy consumption by methods that do not consider these time-varying factors. Therefore, a bi-level distributed dynamic optimization method based on distributed model predictive control is proposed. Moreover, an upper-level optimization model for fleet operational decision-making and a lower-level dynamic optimization model of fleet energy consumption are established. Based on these, a control algorithm for the dynamic optimization of fleet energy consumption is developed. Finally, a case study is carried out to demonstrate the effectiveness of the method. It can further reduce the energy consumption of each ship by at least 1.1% and about 6.8% for the whole fleet.

关键词： Fleet energy consumption EEOI Speed dynamic optimization distributed model predictive control

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distributed model predictive Consensus of Heterogeneous Time-Varying Multi-Agent Systems: With and Without Self-Triggered Mechanism

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2020年第12期67卷 5358-5368页

作者： Li, Huiyan Li, Xiang Fudan Univ Dept Elect 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 Fudan Univ Inst Brain Sci MOE Frontiers Ctr Brain Sci Shanghai 200433 Peoples R China

This paper provides a framework of designing distributed model predictive controller to reach consensus of a heterogeneous time-varying multi-agent system, and the dynamics of heterogeneous agents are modeled by double integrators and Euler-Lagrange (EL) equations. Firstly, a DMPC-based consensus algorithm is proposed, where the constraints in the algorithm depend on the heterogeneous dynamics. We prove that the resultant DMPC optimization problem is feasible with the designed controllers, which is stable when the system reaches consensus. To further reduce communication cost and solve the problem with asynchronous discrete-time information exchange, self-triggered mechanism is introduced into the framework. Trigger intervals are alternatively optimized with the control inputs, and the influence on the system performance is analyzed. Numerical examples are provided to verify the effectiveness and advantages of the proposed algorithms.

关键词： Multi-agent systems Heuristic algorithms predictive models Mathematical model Time-varying systems Prediction algorithms Nickel Time-varying system heterogeneous multi-agent system consensus distributed model predictive control self-triggered mechanism

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Experimental validation of a semi-distributed sequential quadratic programming method for optimal coordination of automated vehicles at intersections

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OPTIMAL control APPLICATIONS & METHODS 2020年第4期41卷 1068-1096页

作者： Hult, Robert Zanon, Mario Frison, Gianluca Gros, Sebastien Falcone, Paolo Chalmers Univ Technol Dept Elect Engn Horsalsvagen 11 SE-41296 Gothenburg Sweden IMT Sch Adv Studies Lucca Italy Univ Freiburg Dept Microsyst Engn Freiburg Germany Norwegian Univ Sci & Technol Dept Engn Cybernet Trondheim Norway

In this article, we study the optimal coordination of automated vehicles at intersections. The problem can be stated as an optimal control problem (OCP), which can be decomposed as a bi-level scheme composed by one nonlinear program (NLP) which schedules the access to the intersection and one OCP per vehicle which computes the appropriate vehicle commands. We discuss a practical implementation of the bi-level controller where the NLP is solved with a tailored semi-distributed sequential quadratic programming (SQP) algorithm that enables distribution of most computation to the vehicles. Results from an extensive experimental campaign are presented, where the bi-level controller and the semi-distributed SQP are implemented on a test setup consisting of three automated vehicles. In particular, we show that the vehicle-level controller can enforce the scheduled intersection access beyond the accuracy admitted by the sensor system, and that the bi-level controller can handle large perturbations and large communication delays, which makes the scheme applicable in practical scenarios. Finally, the use of wireless communication introduces delays in the outer control loop. To allow faster feedback, we introduce a real-time iteration (RTI) like variation of the bi-level controller. Experimental and simulated results indicate that the RTI-like variation offers comparable performance using less computation and communication.

关键词： automated vehicles distributed model predictive control distributed nonlinear programming intersection coordination

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Unified value-based feedback, optimization and risk management in complex electric energy systems

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OPTIMIZATION AND ENGINEERING 2020年第2期21卷 427-483页

作者： Ilic, Marija Jaddivada, Rupamathi MIT Lab Informat & Decis Syst 77 Massachusetts Ave Cambridge MA 02139 USA

The ideas in this paper are motivated by an increased need for systematic data-enabled resource management of large-scale electric energy systems. The basic control objective is to manage uncertain disturbances, power imbalances in particular, by optimizing available power resources. To that end, we start with a centralized optimal control problem formulation of system-level performance objective subject to complex interconnection constraints and constraints representing highly heterogeneous internal dynamics of system components. To manage spatial complexity, an inherent multi-layered structure is utilized by modeling interconnection constraints in terms of unified power variables and their dynamics. Similarly, the internal dynamics of components and sub-systems (modules), including their primary automated feedback control, is modeled so that their input-output characterization is also expressed in terms of power variables. This representation is shown to be key to managing the multi-spatial complexity of the problem. In this unifying energy/power state space, the system constraints are all fundamentally convex, resulting in the convex dynamic optimization problem, for typically utilized quadratic cost functions. Based on this, an interactive multi-layered modeling and control method is introduced. While the approach is fundamentally based on the primal-dual decomposition of the centralized problem, this is formulated for the first time for the couple real-reactive power problem. It is also is proposed for the first time to utilize sensitivity functions of distributed agents for solving the primal distributed problem. Iterative communication complexity typically required for convergence of point-wise information exchange is replaced by the embedded distributed optimization by the modules when creating these functions. A theoretical proof of the convergence claim is given. Notably, the inherent multi-temporal complexity is managed by performing model predictive cont

关键词： modeling Complex electric energy systems control systems Optimization Reliable electric energy Efficient electric energy systems Electricity markets Interaction variables distributed model predictive control Dynamic monitoring and decision systems (DyMonDS) Supervisory control and data acquisition (SCADA)

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distributed MPC for linear discrete-time systems with disturbances and coupled states

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SYSTEMS & control LETTERS 2020年 135卷

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

This paper presents a robust non-iterative distributed model predictive control algorithm for spatially interconnected systems. The system consists of multiple linear discrete-time subsystems, which are weakly coupled in their states. Each subsystem is subject to state and input constraints as well as disturbances. The predictive model of each subsystem updates its state, depending on its own state and the neighboring states. The disturbance and the model error are handled via robustness constraints. The recursive feasibility of the optimization problems and the stability of the closed-loop systems are established. Finally, the effectiveness and advantages of the proposed algorithm are verified via simulations. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Spatially interconnected systems Coupled states distributed model predictive control State and input constraints

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Robust distributed model predictive consensus of discrete-time multi-agent systems:a self-triggered approach

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Frontiers of Information Technology & Electronic Engineering 2021年第8期22卷 1068-1079页

作者： Jiaqi LI Qingling WANG Yanxu SU Changyin SUN School of Cyber Science and Engineering Southeast UniversityNanjing 210096China School of Automation Southeast UniversityNanjing 210096China

This study investigates the consensus problem of a nonlinear discrete-time multi-agent system(MAS)under bounded additive *** propose a self-triggered robust distributed model predictive control consensus algorithm.A new cost function is constructed and MAS is coupled through this *** on the proposed cost function,a self-triggered mechanism is adopted to reduce the communication ***,to overcome additive disturbances,a local minimum-maximum optimization problem under the worst-case scenario is solved iteratively by the model predictive controller of each *** conditions are provided to guarantee the iterative feasibility of the algorithm and the consensus of the closed-loop *** each agent,we provide a concrete form of compatibility constraint and a consensus error terminal *** examples are provided to illustrate the effectiveness and correctness of the proposed algorithm.

关键词： Consensus Self-triggered control distributed model predictive control

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Constrained distributed model predictive control Strategy Based on Agent Coordination

Constrained Distributed Model Predictive Control Strategy Ba...

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第三十一届中国控制会议

作者： YANG Danxuan,WANG Mengling,SHI Hongbo~* Key Laboratory of Advanced control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology,Shanghai 200237

In this paper,a distributed model predictive control(DMPC) strategy is proposed based on agent coordination,in which subsystems couple through the *** first,the initial feasible solution of each agent can be achieved by solving local optimization problems in which the state constraints of neighbor subsystems are considered at each sampling *** then the global optimal solution can be obtained through agent *** the negotiating process,the innovative global optimization objective is determined for the sake of reducing iteration time and improving the convergence speed efficiently. Finally,the accuracy and efficiency of the proposed scheme is put to test through simulation.

关键词： distributed model predictive control agent coordination

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