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System Reconfiguration and Fault-Tolerant for distributed model predictive control Using Parameterized Network Topology

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IFAC-PapersOnLine 2018年第18期51卷 524-529页

作者： Xiao, Guannan Liu, Fei Institute of Automation Jiangnan University Wuxi214122 China

A parameterized network topology based distributed model predictive control (DMPC) framework is proposed in this work, it is mainly applied in system reconfiguration and sensor fault-tolerant control. The Lyapunov stability condition for DMPC with a parameterized network topology is derived. Regarding to system reconfiguration, the parameterized network topology is served as the explicit reconfiguration model. Furthermore, for fault-tolerant control with sensor bias, the parameterized network topology is used to compensate the sensor fault, and a residual generator is designed by states of predictor and consider a time varying threshold for fault detection. The proposed approach is able to handle the system reconfiguration and fault-tolerant control without backup controllers or controllers redesign, and there is no need of the information of fault because of the using of predictor. © 2018

关键词： Topology controllers distributed parameter networks Fault detection Fault tolerance model predictive control Parameterization Co operative control distributed model predictive control Fault tolerant control Lyapunov stability Reconfigurable control Reconfiguration model Residual generator System reconfiguration

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On Feasibility, Stability and Performance in distributed model predictive control

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IEEE TRANSACTIONS ON AUTOMATIC control 2014年第4期59卷 1031-1036页

作者： Giselsson, Pontus Rantzer, Anders Lund Univ Dept Automat Control S-22100 Lund Sweden

In distributed model predictive control (DMPC), where a centralized optimization problemis solved in distributed fashion using dual decomposition, it is important to keep the number of iterations in the solution algorithm small. In this technical note, we present a stopping condition to such distributed solution algorithms that is based on a novel adaptive constraint tightening approach. The stopping condition guarantees feasibility of the optimization problem and stability and a prespecified performance of the closed-loop system.

关键词： distributed model predictive control feasibility performance guarantee stability

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Low Complexity distributed model predictive control by Using Contractive Sets

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IFAC-PapersOnLine 2017年第1期50卷 13164-13169页

作者： Grancharova, Alexandra Olaru, Sorin Department of Industrial Automation University of Chemical Technology and Metallurgy Bul. Kl. Ohridski 8 Sofia1756 Bulgaria Laboratory of Signals and Systems CentraleSupélec-CNRS-Université Paris-Sud Université Paris-Saclay 3 Rue Joliot-Curie Gifsur-Yvette Cedex91192 France

In this paper, an approach to low complexity distributed MPC of linear interconnected systems with coupled dynamics subject to both state and input constraints is proposed. The suggested approach is based on the idea of introducing a contractive set constraint in the centralized MPC problem formulation, which would guarantee the closed-loop system stability when using a small prediction horizon. Then, a dual accelerated gradient method is applied to obtain distributedly a suboptimal solution of the resulting Quadratic Programming problem. The suggested approach would be appropriate for embedded distributed MPC since it will reduce the complexity of the on-line MPC computations, simplify the software implementation, and reduce the requirements for available memory. © 2017

关键词： predictive control systems Closed loop systems distributed parameter control systems Gradient methods Large scale systems Linear systems model predictive control Quadratic programming System stability Constraints distributed control distributed model predictive control predictive control Problem formulation Quadratic programming problems Software implementation State and input constraints

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Cooperative distributed model predictive control via Linear Programming-A Divide and Conquer Approach

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IFAC-PapersOnLine 2015年第23期48卷 308-313页

作者： Kong, He Longo, Stefano Centre for Automotive Engineering and Technology Cranfield University College Road Cranfield BedfordMK43 0AL United Kingdom

Motivated by the recent progress in centralized model predictive control (MPC) design using a linear program (LP) and distributed MPC with quadratic program (QP) formulations, this paper discusses the design of LP-based cooperative distributed MPC for large scale linear systems with coupled dynamics and decoupled input constraints. As such, we examine the applicability of the Divide and Conquer approach to QP-based cooperative distributed MPC, recently proposed by the authors, to the LP-based case. It is shown that within the Divide and Conquer framework, an upbound of the original LP-based cooperative cost function (instead of itself), can be optimized for computing local inputs, resulting in suboptimal policies. However, advantages of doing so include the independency of the computation of local inputs and a large amount of communication burden reduction, as guaranteed by theoretical analysis and illustrated through numerical simulation. We also present closed-loop stability analysis and point out some questions worth further investigation. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： model predictive control Closed loop control systems Cost functions distributed parameter control systems Linear programming Linear systems predictive control systems Quadratic programming Centralized models Closed loop stability analysis Constrained controls distributed control distributed model predictive control Divide and conquer approach Large scale linear systems predictive control

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Reference Tracking using a Non-Cooperative distributed model predictive control Algorithm

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IFAC-PapersOnLine 2016年第7期49卷 1079-1084页

作者： Maxim, Anca Ionescu, Clara M. Caruntu, Constantin F. Lazar, Corneliu Keyser, Robin De Department of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Blvd. Mangeron 27 Iasi700050 Romania Department of Electrical energy Systems and Automation Ghent University Technologiepark 914 GentB9052 Belgium

In this paper, a non-cooperative distributed model predictive control (DMPC) algorithm for tracking constant references is developed and evaluated. As such, an augmented model is employed (i.e. the control loop is embedded with integrators) and the augmented state contains the state increments and the error between the reference and the predicted output. The algorithm is tested in real life experiments on the quadruple tank process with non-minimum phase behaviour. The experimental results show acceptable performance index for the DMPC method when compared with the centralized approach. © 2016

关键词： model predictive control MIMO systems predictive control systems Acceptable performance Centralized approaches distributed model predictive control Non cooperative Non minimum phase Non minimum phase systems Reference tracking Two agents

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distributed model predictive control Based on Nash Optimality for Large Scale Irrigation Systems ∗

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IFAC-PapersOnLine 2015年第8期48卷 551-555页

作者： Rongchao Zhang Andong Liu Li Yu Wenan Zhang Department of Automation Zhejiang University of Technology Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou 310023 P.R. China

Irrigation system is a large scale system, consisting of many interacting channels, and spanning vast geographical areas. In practice, the water level is expected to the desired reference values for safety and efficiency. In this paper, the model of irrigation system is modeled by the principle on conservation of mass. Because of the strong coupling between neighboring pools in irrigation system and large disturbance from the environment, a non-cooperative distributed model predictive control (NDMPC) algorithm based on Nash optimality is proposed for the large scale irrigation system to design the controller for setting points regulation. Finally, a simulation example is given to demonstrate the effectiveness of the proposed algorithm.

关键词： distributed model predictive control Irrigation systems Nash optimality Large scale systems

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Coordinated Non-Cooperative distributed model predictive control for Decoupled Systems Using Graphs

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IFAC-PapersOnLine 2016年第22期49卷 216-221页

作者： Alrifaee, Bassam Heßeler, Frank-Josef Abel, Dirk Institute of Automatic Control Department of Mechanical Engineering RWTH Aachen University Aachen52074 Germany

This paper proposes a novel strategy for Non-Cooperative distributed model predictive control (Non-Coop. DMPC) of Networked control Systems (NCS) consisting of dynamically decoupled agents where the coupling is achieved in the objective function or in the constraints. Moreover, the coupling is considered to be time-variant. We call this strategy Priority-Based Non-Coop. DMPC (PB-Non-Coop. DMPC). It is based on assigning priorities to the agents and the use of the coupling graph. Each PB-Non-Coop. DMPC is associated with a different agent and computes the local control inputs based only on its states and that of its neighbors. PB-Non-Coop. DMPC satisfies the prediction consistency property and reduces the overall computation time in comparison with existing Non-Coop. DMPC strategies in literature, thereby improves the overall behavior of NCS. We compare PB-Non-Coop. DMPC with centralized MPC as well as with another Non-Coop. DMPC strategy from literature. © 2016

关键词： model predictive control Networked control systems predictive control systems Computation time Consistency property Decoupled system distributed model predictive control Novel strategies Objective functions Priority Time variant

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Detection, isolation and handling of actuator faults in distributed model predictive control systems

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JOURNAL OF PROCESS control 2010年第9期20卷 1059-1075页

作者： Chilin, David Liu, Jinfeng Munoz de la Pena, David Christofides, Panagiotis D. Davis, James F. Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA Univ Seville Dept Ingn Sistemas & Automat Seville 41092 Spain Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

In this work, we focus on monitoring and reconfiguration of distributed model predictive control systems applied to general nonlinear processes in the presence of control actuator faults. Specifically, we consider nonlinear process systems controlled with a distributed control scheme in which two Lyapunov-based model predictive controllers manipulate two different sets of control inputs and coordinate their actions to achieve the desired closed-loop stability and performance specifications. To deal with control actuator faults which may reduce the ability of the distributed control system to stabilize the process, a model-based fault detection and isolation and fault-tolerant control system which detects and isolates actuator faults and determines how to reconfigure the distributed control system to handle the actuator faults while maintaining closed-loop stability is designed. A detailed mathematical analysis is carried out to determine precise conditions for the stabilizability of the fault detection and isolation and fault-tolerant control system. A chemical process example, consisting of two continuous stirred tank reactors and a flash tank separator with a recycle stream and involving stabilization of an unstable steady-state, is used to demonstrate the approach. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Fault detection and isolation Fault-tolerant control distributed model predictive control Nonlinear processes

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distributed model predictive control via Proximal Jacobian ADMM for Building control Applications

Distributed Model Predictive Control via Proximal Jacobian A...

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American control Conference

作者： Xiaodong Hou Yingying Xiao Jie Cai Jianghai Hu James E. Braun School of Electrical and Computer Engineering Purdue University West Lafayette IN 47907 USA School of Mechanical Engineering Purdue University West Lafayette IN 47907 USA

ISBN: (纸本)9781509045839

This paper investigates a distributed model predictive control (DMPC) framework for building control applications. The proposed framework is general in that it can be easily customized to solve the dynamic optimization problem for a broad class of multi-zone buildings with relatively complex HVAC systems. The Proximal Jacobian alternating direction method of multipliers (ADMM), a recent variant of the traditional Gauss-Seidel sequential ADMM is employed and adopted to solve the centralized optimization problem, which ultimately leads to an agent-based parallel updating scheme with guaranteed convergence. A case study on the HVAC energy optimization of a multi-zone building is presented to show the effectiveness of the proposed method.

关键词： distributed model predictive control buildings control Jacobian High Voltage Alternating Current Systems

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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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31st Chinese control Conference

作者： Yang Danxuan Wang Mengling Shi Hongbo East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

ISBN: (纸本)9789881563811

In this paper, a distributed model predictive control (DMPC) strategy is proposed based on agent coordination, in which subsystems couple through the inputs. At 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 time. And then the global optimal solution can be obtained through agent coordination. In 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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