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A distributed reinforcement learning and model predictive control architecture for routing decisions in urban road networks

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IFAC-PapersOnLine 2023年第2期56卷 2359-2364页

作者： Francesco Giannini Giancarlo Fortino Giuseppe Franzè Francesco Pupo DIMES University of Calabria Italy DIMEG University of Calabria Italy

In this paper, platoons of autonomous vehicles in urban road networks are considered. From a methodological point of view, the problem consists in characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data and a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle.

关键词： Autonomous vehicles Routing decisions distributed Reinforcement Learning distributed model predictive control

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distributed Economic model predictive control of a Catalytic Reactor: Evaluation of Sequential and Iterative Architectures

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IFAC-PapersOnLine 2015年第8期48卷 26-31页

作者： Timothy L. Anderson Matthew Ellis Panagiotis D. Christofides Department of Chemical and Biomolecular Engineering University of California Los Angeles CA 90095-1592 USA Department of Electrical Engineering University of California Los Angeles CA 90095-1592 USA

The development and application of distributed economic model predictive control (DEMPC) methodologies to a catalytic reactor is considered. Two DEMPC methodologies are designed for sequential and iterative implementation, respectively. The DEMPC architectures are evaluated on the basis of the closed-loop performance and on-line computation time requirements compared to a centralized EMPC approach. For the catalytic reactor considered, DEMPC proves to be a viable option as it is able to give similar closed-loop performance while reducing the on-line computation time requirements relative to a centralized EMPC strategy.

关键词： Economic model predictive control distributed model predictive control process control nonlinear systems

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A distributed model predictive control scheme for networks with communication failure

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IFAC Proceedings Volumes 2014年第3期47卷 12004-12009页

作者： Rainer M. Schaich Matthias A. Müller Frank Allgöwer Department of Engineering Science University of Oxford OX1 3PJ UK Institute of Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany

A method to ensure recursive feasibility and asymptotic stability for a distributed model predictive control scheme in case of temporary communication failure is presented. The considered networks are made up of dynamically uncoupled nonlinear systems, coupled through constraints and objectives. The proposed method substitutes affected coupling constraints in such a way that the scheme can not become infeasible, while a decrease in the cost is guaranteed.

关键词： distributed model predictive control Communication failure Collision avoidance

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distributed EVENT-TRIGGERED model predictive control OF COUPLED NONLINEAR SYSTEMS

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SIAM JOURNAL ON control AND OPTIMIZATION 2020年第2期58卷 714-734页

作者： Liu, Changxin Li, Huiping Shi, Yang Xu, Demin Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Univ Victoria Dept Mech Engn Victoria BC V8W 3P6 Canada

This paper studies the distributed event-triggered model predictive control (DMPC) problem of coupled nonlinear systems with constraints. A novel event-triggered DMPC algorithm is proposed by designing a distributed event-triggering strategy and inventing a constraint that restricts the discrepancy between each subsystem's assumed and predicted states. With the designed triggering rule and constraint, the mutual disturbances caused by dynamical coupling are proved to be bounded, and the Zeno behavior is avoided. In addition, the suffcient conditions ensuring algorithm feasibility and closed-loop stability are provided. Finally, simulation studies are conducted to verify the effectiveness of the theoretical results.

关键词： event-triggered control distributed model predictive control coupled nonlinear systems constrained systems

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Weight optimisation for iterative distributed model predictive control applied to power networks

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第1期26卷 532-543页

作者： Mc Namara, Paul Negenborn, Rudy R. De Schutter, Bart Lightbody, Gordon Natl Univ Ireland Univ Coll Cork Control & Intelligent Syst Grp Elect & Elect Engn Dept Cork Ireland Delft Univ Technol Dept Marine & Transport Technol Delft Netherlands Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands

This paper presents a weight tuning technique for iterative distributed model predictive control (MPC). Particle Swarm Optimisation (PSO) is used to optimise both the weights associated with disturbance rejection and those associated with achieving consensus between control agents. Unlike centralised MPC, where tuning focuses solely on disturbance rejection performance, iterative distributed MPC practitioners must concern themselves with the trade off between disturbance rejection and the overall communication overhead when tuning weights. This is particularly the case in large scale systems, such as power networks, where typically there will be a large communication overhead associated with control. In this paper a method for simultaneously optimising both the closed loop performance and minimising the communications overhead of iterative distributed MPC systems is proposed. Simulation experiments illustrate the potential of the proposed approach in two different power system scenarios. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Multi-agent distributed model predictive control Particle swarm optimisation Smart grids Weight tuning Power networks

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A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

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control ENGINEERING PRACTICE 2013年第11期21卷 1594-1605页

作者： Minh Dang Doan Giselsson, Pontus Keviczky, Tamas De Schutter, Bart Rantzer, Anders Cantho Univ Technol Fac Elect Elect & Telecommun Engn Ho Chi Minh City Vietnam Lund Univ Dept Automat Control S-22100 Lund Sweden Delft Univ Technol Delft Ctr Syst & Control NL-2600 AA Delft Netherlands

A distributed model predictive control (DMPC) approach based on distributed optimization is applied to the power reference tracking problem of a hydro power valley (HPV) system. The applied optimization algorithm is based on accelerated gradient methods and achieves a convergence rate of O(1/k(2)), where k is the iteration number. Major challenges in the control of the HPV include a nonlinear and large-scale model, nonsmoothness in the power-production functions, and a globally coupled cost function that prevents distributed schemes to be applied directly. We propose a linearization and approximation approach that accommodates the proposed the DMPC framework and provides very similar performance compared to a centralized solution in simulations. The provided numerical studies also suggest that for the sparsely interconnected system at hand, the distributed algorithm we propose is faster than a centralized state-of-the-art solver such as CPLEX. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Hydro power control distributed optimization Accelerated gradient algorithm model predictive control distributed model predictive control

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distributed fault-tolerant model predictive control for intermittent fault: A cooperative way

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ISA TRANSACTIONS 2019年 89卷 113-121页

作者： Xiao, Guannan Liu, Fei Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

For intermittent actuator faults of large-scale system, a cooperative distributed fault-tolerant model predictive control (DFTMPC) is presented. The actuator plug and play strategy is adopted in the interconnected systems with physical coupling making fault estimation and controller redesign unnecessary. The actuator plug and play process is modeled as a distributed switching model, and there a theoretical stability analysis is provided with switching form of model predictive control (MPC) cost functions. The novel cooperative distributed fault-tolerant performance index is raised in a global view for distributed model predictive control. A simulation example is taken to show the electiveness of the proposed method. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Cooperative fault-tolerant control Plug and play control Actuator fault

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A cooperative game approach to synthesizing a sequential distributed model predictive controller

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INTERNATIONAL JOURNAL OF DYNAMICS AND control 2023年第5期11卷 2514-2522页

作者： Fitriana, Vina Salmah Suparwanto, Ari Univ Gadjah Mada Math Dept Bulaksumur 21 Sleman 55281 Yogyakarta Indonesia Univ Teknol Sumbawa Elect Engn Dept Sumbawa 84371 Nusa Tenggara B Indonesia

This paper presents a distributed model predictive control algorithm for a discrete linear time-invariant system that involves a cooperative game approach. It is assumed that this system consists of several subsystems which are connected through inputs. The subsystems are updated one by one in a sequential manner. Once a subsystem is completed its optimization, its optimal input is shared with the next interconnected subsystem. The process of synthesizing the optimal control for each subsystem involves the computation of disagreement points for each subsystem by employing a feasible cooperation approach. Meanwhile, in the negotiation process, the subsystem can validate whether or not it should cooperate at each time step. The feasibility of the proposed approach at each iteration and time step is proven. Furthermore, the stability of the proposed method is investigated by using the convexity of the cost function. Finally, the proposed method is applied to a canal irrigation system.

关键词： distributed model predictive control Sequential control Cooperative game Disagreement point Canal irrigation systems

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distributed output-feedback model predictive control for multi-agent consensus

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SYSTEMS & control LETTERS 2019年 127卷 52-59页

作者： Copp, David A. Vamvoudakis, Kyriakos G. Hespanha, Joao P. Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

We propose a distributed output-feedback model predictive control approach for achieving consensus among multiple agents. Each agent computes a distributed control action based on an output-feedback measurement of a local neighborhood tracking error and communicates information only to its neighbors, according to a communication network modeled as a directed graph. Each agent computes its distributed control action by solving a local min-max optimization problem that simultaneously computes a local state estimate and control input under worst-case assumptions on unmeasured input disturbances and measurement noise. Under easily verified controllability and observability assumptions, this distributed output-feedback model predictive control approach provides an upper bound on the group consensus error, thereby ensuring practical consensus in the presence of unmeasured disturbances and noise. A numerical example with four agents connected in a directed graph is given to illustrate the results. (C) 2019 Elsevier B.V. All rights reserved.

关键词： distributed model predictive control Output-feedback Moving horizon estimation Multi-agent consensus

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Sequential distributed model predictive control for State-Dependent Nonlinear Systems

Sequential Distributed Model Predictive Control for State-De...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Abokhatwa, Salah G. Katebi, Reza Univ Strathclyde Ind Control Ctr Dept Elect & Elect Engn Glasgow G1 1QE Lanark Scotland

ISBN: (纸本)9781479906529

In this paper, sequential nonlinear distributed model predictive control (DMPC) algorithms for large-scale systems that can handle constraints are proposed. The proposed algorithms are based on nonlinear MPC strategy, which uses a state-dependent nonlinear model to avoid the complexity of the nonlinear programming (NLP) problem. In this distributed framework, local MPCs solve convex optimization problem and exchange information via one directional communication channel at each sampling time to achieve the global control objectives of the system. Numerical simulation results show that the performance of the proposed DMPC algorithms is close to the centralized NMPC but computationally more efficient compared to the centralized one.

关键词： distributed model predictive control Centralized model predictive control Nonlinear State-dependent control Supervisory model predictive control

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