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20th World Congress of the International-Federation-of-Automatic-control (IFAC)

作者： Albalawi, Fahad Durand, Helen Christofides, Panagiotis D. Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA

Promoting process safety of chemical processes while operating them in an economically-optimal manner is a matter of great importance. In Albalawi et al. (2016), a safety-based economic model predictive control methodology (safety-EMPC) was developed to operate nonlinear processes in an economically-optimal manner while maintaining process safety and closed-loop stability. However, the safety-EMPC control strategy was developed with a centralized economic model predictive control (EMPC) structure;thus, computation time limitations within a sampling period may reduce the effectiveness of such a controller design for promoting process safety. Alternatively, we develop in this work sequential and iterative safety based distributed EMPC schemes (safety-DEMPC) that may overcome the computation time limitations of the centralized safety-EMPC while maintaining similar closed-loop performance. Using a catalytic reactor example, the two proposed safety-DEMPC schemes were demonstrated to achieve similar closed-loop performance to the centralized safety-EMPC while reducing the on-line computation time requirements compared to the centralized safety-EMPC. (C) 2017, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Process safety distributed model predictive control computation time

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Partitioning for Large-scale Systems: A Sequential distributed MPC Design

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IFAC-PapersOnLine 2017年第1期50卷 8838-8843页

作者： Barreiro-Gomez J. Ocampo-Martinez C. Quijano N. Automatic Control Department Universitat Politècnica de Catalunya Institut de Robòotica i Informàtica Industrial (CSIC-UPC) Llorens i Artigas 4-6 Barcelona 08028 Spain Departamento de Ingeniería Electrica y Electrónica Universidad de los Andes Carrera 1 No 18A-10 Bogotá Colombia

Large-scale systems involve a high number of variables making challenging the design of controllers because of information availability and computational burden issues. Normally, the measurement of all the states in a large-scale system implies to have a big communication network, which might be quite expensive. On the other hand, the treatment of large amount of data to compute the appropriate control inputs implies high computational costs. An alternative to mitigate the aforementioned issues is to split the problem into several sub-systems. Thus, computational tasks may be split and assigned to different local controllers, letting to reduce the required time to compute the control inputs. Additionally, the partitioning of the system allows control designers to simplify the communication network. This paper presents a partitioning algorithm performed by considering an information-sharing graph that can be generated for any control strategy and for any dynamical large-scale system. Finally, a distributed model predictive control (DMPC) is designed for a large-scale system as an application of the proposed partitioning method. © 2017

关键词： distributed model predictive control large-scale systems Partitioning

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model predictive control with a mixed integer programming for merging path generation on motor way 1

Model predictive control with a mixed integer programming fo...

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1st Annual IEEE Conference on control Technology and Applications

作者： Mukai, Masakazu Natori, Hiroi Fujita, Masayuki Kogakuin Univ Dept Elect & Elect Engn Tokyo 1638677 Japan Tokyo Inst Technol Dept Syst & Control Engn Tokyo 1528550 Japan

ISBN: (纸本)9781509021826

This paper proposes a path generation method using model predictive control for merging of automobiles on a motor way. The path generation problem is formulated as a mixed integer programming (MIP) problem. The method is implemented based on receding horizon philosophy. The MIP is solved at each step then a first element of the optimal input is applied to system. The point of the proposed method is that it can consider a feasibility of the merging. Computer simulation is carried out to show the effectiveness of the proposed control method. Furthermore it is shown that the feasibility analysis can be employed to the gap control using adaptive cruise control and lane change control.

关键词： merging automobiles MIP merger path generation Automobiles MIP gene Integer Programming Cars distributed model predictive control nonlinear model predictive control predictive control model predictive control Adaptive cruise control Mixed integer

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distributed model predictive load frequency control of multi-area interconnected power system

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 62卷 289-298页

作者： Ma, Miaomiao Chen, Hong Liu, Xiangjie Allgoewer, Frank North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Univ Stuttgart Inst Syst Theory & Automat Control D-70550 Stuttgart Germany Jilin Univ Dept Control Sci & Engn Changchun 130025 Peoples R China

This paper presents a load frequency control (LFC) design using the distributed model predictive control (DMPC) technique for the multi-area interconnected power system. The dynamics model of multi-area interconnected power system is introduced, and Generation Rate Constraint (GRC) and load reference setpoint constraint are considered. The overall system is decomposed into several subsystems and each has its own local area MPC controller. These subsystem-based MPCs exchange their measurements and predictions by communication and incorporate the information from other controllers into their local control objective so as to coordinate with each other. Analysis and simulation results for a three-area interconnected power system show possible improvements on closed-loop performance, computational burden and robustness, while respecting physical hard constraints. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Load frequency control distributed model predictive control Generation Rate Constraint Interconnected power system

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Sequential and Iterative distributed model predictive control of Nonlinear Process Systems Subject to Asynchronous Measurements

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IFAC Proceedings Volumes 2010年第5期43卷 625-630页

作者： Xianzhong Chen Jinfeng Liu David Muñoz de la Peña Panagiotis D. Christofides Department of Chemical and Biomolecular Engineering University of California Los Angeles CA 90095-1592 USA Departamento de Ingeniería de Sistemas y Automática Universidad de Sevilla Sevilla 41092 Spain Department of Electrical Engineering University of California Los Angeles CA 90095-1592 USA

In this work, we focus on sequential and iterative distributed model predictive control (DMPC) of large scale nonlinear process systems subject to asynchronous measurements. Assuming that there is an upper bound on the maximum interval between two consecutive asynchronous measurements, we design DMPC schemes that take into account asynchronous feedback explicitly via Lyapunov techniques. Sufficient conditions under which the proposed distributed control designs guarantee that the states of the closed-loop system are ultimately bounded in regions that contain the origin are provided. The theoretical results are illustrated through a catalytic alkylation of benzene process example.

关键词： distributed model predictive control Nonlinear systems Asynchronous measurements

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A dynamic prognosis algorithm in distributed fault tolerant model predictive control

A dynamic prognosis algorithm in distributed fault tolerant ...

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IEEE Conference on control Applications (CCA)

作者： Zakharov, Alexey Yu, Miao Jamsa-Jounela, Sirkka-Liisa Aalto Univ Dept Biotechnol & Chem Technol Sch Chem Technol Aalto 00076 Finland

ISBN: (纸本)9781479974092

This paper presents a dynamic prognosis algorithm in distributed fault tolerant model predictive control (DFTMPC). The dynamic prognosis, which means predicting the trajectories of process variables under distributed model predictive control, is performed when a fault is diagnosed and several candidate reconfigured controls are proposed. Then, the dynamic prognosis is utilized to check whether the candidate reconfigured controls are able to drive the system to the new operating conditions and to evaluate the performance during the transition period. Thus, the most suitable candidate reconfigured controller is selected and its feasibility is ensured without using a Lyapunov function that is difficult to obtain for large-scale systems. On the other hand, the on-line computation burden of the prognosis algorithm is moderate under the assumption that the sets of active constraints in non-faulty subsystems remain the same as they are at the nominal operating conditions. Thus, the dynamic prognosis for DMPC is aimed to improve the applicability of the existing fault tolerant methods to large-scale systems.

关键词： fault tolerant control distributed model predictive control dynamic prognosis alkylation of benzene industrial application

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A distributed predictive control Strategy Based on State Estimator for islanded microgrid

A Distributed Predictive Control Strategy Based on State Est...

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International Power Electronics Conference

作者： Mi Dong Li Li Xiaoyu Tian School of Information Science and Engineering Central South University Changsha China

The traditional droop control is usually used to share real and reactive power without communication for fully decentralization power system. Because of that the traditional droop control has its inherent limitations, this paper presents a modified droop controller based on Kalman filter to reduce load effect, and then utilizes a distributed consensus algorithm to transmit error voltage to other distributed generation (DG) with sparse communication network, and last make use of distributed model predictive control (MPC) to achieve accurately load power sharing between multi-parallel inverters. Simulation results is verified the reliability of the proposed algorithm in realizing load sharing and the voltage-frequency restoration.

关键词： droop control Kalman filter load effect distributed consensus algorithm distributed model predictive control

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Handling Communication Disruptions in distributed model predictive control

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IFAC Proceedings Volumes 2010年第5期43卷 296-301页

作者： Mohsen Heidarinejad Jinfeng Liu David Muñoz de la Peña Panagiotis D. Christofides Department of Electrical Engineering University of California Los Angeles CA 90095-1592 USA Department of Chemical and Biomolecular Engineering University of California Los Angeles CA 90095-1592 USA Departamento de Ingeniería de Sistemas y Automática Universidad de Sevilla Sevilla 41092 Spain

In this work, we deal with distributed model predictive control (DMPC) of nonlinear systems with communication disruptions between the distributed controllers. Specifically, we focus on the design of DMPC systems that take into account communication channel noise and data losses between the distributed controllers explicitly. In contrast to most of the existing DMPC methods which assume flawless communication, we employ a specific channel model to consider a number of realistic data transmission scenarios. In order to ensure the stability of the closed-loop system under communication disruptions, each model predictive controller utilizes a stability constraint which is based on a suitable Lyapunov-based controller. The theoretical results are demonstrated through a nonlinear chemical process example.

关键词： distributed model predictive control Nonlinear systems Networked control

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distributed model predictive control of Nonlinear Process Systems

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AICHE JOURNAL 2009年第5期55卷 1171-1184页

作者： Liu, Jinfeng Munoz de la Pena, David 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 Univ Seville Dept Ingn Sistemas & Automat Seville 41092 Spain

This work focuses on a class of nonlinear control problems that arise when new control systems which may use networked sensors and/or actuators are added to already operating control loops to improve closed-loop performance. In this case, it is desirable to design the pre-existing control system and the new control system in a way such that they coordinate their actions. To address this control problem, a distributed model predictive control method is introduced where both the pre-existing control system and the new control system are designed via Lyapunov-based model predictive control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov-based controller that stabilizes the nominal closed-loop system using only the pre-existing control loops, two separate Lyapunov-based model predictive controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model predictive control design preserves the stability properties of the Lyapunov-based controller, improves the closed-loop performance, and allows handling input constraints. In addition, the proposed distributed control design requires reduced communication between the two distributed controllers since it requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The theoretical results art? illustrated using a chemical process example. (C) 2009 American Institute of Chemical Engineers AIChE J, 55: 1171-1184, 2009

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

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distributed model predictive control for plant-wide hot-rolled strip laminar cooling process

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JOURNAL OF PROCESS control 2009年第9期19卷 1427-1437页

作者： Zheng, Yi Li, Shaoyuan Wang, Xiaobo Shanghai Jiao Tong Univ Inst Automat Shanghai 200240 Peoples R China Baosteel Iron & Steel Ltd Co Inst Res & Dev Shanghai 201900 Peoples R China

Since hot-rolled strip laminar cooling (HSLC) process is a large-scale, nonlinear system, a distributed model predictive control (DMPC) framework is proposed for computational reason and enhancing the precision and flexibility of control system. The overall system is divided into several interconnected subsystems and each subsystem is controlled by local model predictive control (MPC). These local MPCs cooperate with its neighbours through the scheme of neighbourhood optimization for the improvement of global performance. The state space representation of each subsystem's prediction model is designed by finite volume method firstly, and then is linearized around the current operating point at each step to overcome the computational obstacle of nonlinear model. Moreover, since the strip temperature is measurable only at a few positions in water cooling section due to the difficult ambient conditions, an Extended Kalman Filter (EKF) is used to estimate the transient temperature of strip. Both simulation and experiment results prove the efficiency of the proposed method. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Laminar cooling Hot-rolled strip

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