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Panagiotis D Christofides - One of the best experts on this subject based on the ideXlab platform.
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machine learning based Distributed Model predictive control of nonlinear processes
Authorea Preprints, 2020Co-Authors: Scarlett Chen, Zhe Wu, David Rincon, Panagiotis D ChristofidesAbstract:This work addresses the design of Distributed Model predictive control (DMPC) systems for nonlinear processes using machine learning Models to predict nonlinear dynamic behavior. Specifically, sequential and iterative Distributed Model predictive control systems are designed and analyzed with respect to closed-loop stability and performance properties. Extensive open- loop data within a desired operating region are used to develop Long Short-Term Memory (LSTM) recurrent neural network Models with a sufficiently small Modeling error from the actual nonlinear process Model. Subsequently, these LSTM Models are utilized in Lyapunov- based DMPC to achieve efficient real-time computation time while ensuring closed-loop state boundedness and convergence to the origin. Using a nonlinear chemical process network exam- ple, the simulation results demonstrate the improved computational efficiency when the process is operated under sequential and iterative DMPCs while the closed-loop performance is very close to the one of a centralized MPC system.
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Distributed Model predictive control a tutorial review and future research directions
Computers & Chemical Engineering, 2013Co-Authors: Panagiotis D Christofides, R Scattolini, David Muñoz De La PeñaAbstract:Abstract In this paper, we provide a tutorial review of recent results in the design of Distributed Model predictive control systems. Our goal is to not only conceptually review the results in this area but also to provide enough algorithmic details so that the advantages and disadvantages of the various approaches can become quite clear. In this sense, our hope is that this paper would complement a series of recent review papers and catalyze future research in this rapidly evolving area. We conclude discussing our viewpoint on future research directions in this area.
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ACC - Distributed Model predictive control of switched nonlinear systems
2012 American Control Conference (ACC), 2012Co-Authors: Mohsen Heidarinejad, Panagiotis D ChristofidesAbstract:In this work, we present a framework for the design of Distributed Model predictive control systems for a broad class of switched nonlinear systems for which the mode transitions take place according to a prescribed switching schedule. Under appropriate stabilizability assumptions on the existence of a set of feedback controllers that can stabilize the closed-loop switched, nonlinear system, we design a cooperative, Distributed Model predictive control architecture using Lyapunov-based Model predictive control in which the Distributed controllers carry out their calculations in parallel and communicate in an iterative fashion to compute their control actions. The proposed Distributed Model predictive control design is applied to a nonlinear chemical process network with scheduled mode transitions.
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Multirate Distributed Model Predictive Control
Networked and Distributed Predictive Control, 2011Co-Authors: Panagiotis D Christofides, Jinfeng Liu, David Muñoz De La PeñaAbstract:In Chap. 6, a multirate Distributed Model predictive control design for large-scale nonlinear uncertain systems with fast and slowly sampled states is developed. The Distributed Model predictive controllers are connected through a shared communication network and cooperate in an iterative fashion at time instants in which both fast and slowly sampled measurements are available, to guarantee closed-loop stability. When only local subsystem fast sampled state information is available, the Distributed controllers operate in a decentralized fashion to improve closed-loop performance. In the design of the Distributed controllers, bounded measurement noise, process disturbances and communication noise are also taken into account. Using a reactor–separator process example, the stability property and performance of the multirate Distributed predictive control architecture is illustrated.
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sequential and iterative architectures for Distributed Model predictive control of nonlinear process systems
Aiche Journal, 2010Co-Authors: Jinfeng Liu, David Muñoz De La Peña, Xianzhong Che, Panagiotis D ChristofidesAbstract:In this work, we focus on Distributed Model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different Model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the Distributed controllers at each sampling time and a Model of the plant is available, we propose two different Distributed Model predictive control architectures. In the first architecture, the Distributed controllers use a one-directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the Distributed controllers utilize a bi-directional communication strategy, are evaluated in parallel and iterate to improve closed-loop performance. In the design of the Distributed Model predictive controllers, Lyapunov-based Model predictive control techniques are used. To ensure the stability of the closed-loop system, each Model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov-based controller. We prove that the proposed Distributed Model predictive control architectures enforce practical stability in the closed-loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. V C 2010 American Institute of Chemical Engineers AIChE J, 56: 2137–2149, 2010
Arthur Richards - One of the best experts on this subject based on the ideXlab platform.
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Scalable Distributed Model predictive control for constrained systems
Automatica, 2018Co-Authors: F. Elham Asadi, Arthur RichardsAbstract:Abstract A Distributed Model predictive control strategy is proposed for subsystems sharing a limited resource. Self-organized Time Division Multiple Access is used to coordinate subsystem controllers in a sequence such that no two re-optimize simultaneously. This new approach requires no central coordination or pre-organized optimizing sequence. The scheme guarantees satisfaction of coupled constraints despite dynamic entry and exit of subsystems.
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robust Distributed Model predictive control
International Journal of Control, 2007Co-Authors: Arthur RichardsAbstract:This paper presents a formulation for Distributed Model predictive control (DMPC) of systems with coupled constraints. The approach divides the single large planning optimization into smaller sub-problems, each planning only for the controls of a particular subsystem. Relevant plan data is communicated between sub-problems to ensure that all decisions satisfy the coupled constraints. The new algorithm guarantees that all optimizations remain feasible, that the coupled constraints will be satisfied, and that each subsystem will converge to its target, despite the action of unknown but bounded disturbances. Simulation results are presented showing that the new algorithm offers significant reductions in computation time for only a small degradation in performance in comparison with centralized MPC.
Anders Rantzer - One of the best experts on this subject based on the ideXlab platform.
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for Distributed Model predictive control
2015Co-Authors: Pontus Giselssonminh, Dang Doan, Bart De Schutter, Anders RantzerAbstract:We propose a Distributed optimization algorithm for mixedL1/L2-norm optimization based on accelerated gradient methods using dual decomposition. The algorithm achieves convergence rate O( 1 k 2), where k is the iteration number, which significantly improves the convergence rates of existing duality-based Distributed optimization algorithms that achieve O( 1 ). The performance of the developed algorithm is evaluated on randomly generated optimization problems arising in Distributed Model Predictive Control (MPC). The evaluation shows that, when the problem data is sparse and large-scale, our algorithm outperforms state-of-the-art optimization software CPLEX and MOSEK.
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on feasibility stability and performance in Distributed Model predictive control
IEEE Transactions on Automatic Control, 2014Co-Authors: Pontus Giselsson, Anders RantzerAbstract:In Distributed Model predictive control (DMPC), where a centralized optimization problem is 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.
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accelerated gradient methods and dual decomposition in Distributed Model predictive control
Automatica, 2013Co-Authors: Pontus Giselsson, Bart De Schutter, Tamas Keviczky, Minh Dang Doan, Anders RantzerAbstract:We propose a Distributed optimization algorithm for mixed L"1/L"2-norm optimization based on accelerated gradient methods using dual decomposition. The algorithm achieves convergence rate O(1k^2), where k is the iteration number, which significantly improves the convergence rates of existing duality-based Distributed optimization algorithms that achieve O(1k). The performance of the developed algorithm is evaluated on randomly generated optimization problems arising in Distributed Model predictive control (DMPC). The evaluation shows that, when the problem data is sparse and large-scale, our algorithm can outperform current state-of-the-art optimization software CPLEX and MOSEK.
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Distributed Model predictive control with suboptimality and stability guarantees
Conference on Decision and Control, 2010Co-Authors: Pontus Giselsson, Anders RantzerAbstract:Theory for Distributed Model Predictive Control (DMPC) is developed based on dual decomposition of the convex optimization problem that is solved in each time sample. The process to be controlled is an interconnection of several subsystems, where each subsystem corresponds to a node in a graph. We present a stopping criterion for the DMPC scheme that can be locally verified by each node and that guarantees closed loop suboptimality above a pre-specified level and asymptotic stability of the interconnected system.
David Muñoz De La Peña - One of the best experts on this subject based on the ideXlab platform.
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Distributed Model predictive control a tutorial review and future research directions
Computers & Chemical Engineering, 2013Co-Authors: Panagiotis D Christofides, R Scattolini, David Muñoz De La PeñaAbstract:Abstract In this paper, we provide a tutorial review of recent results in the design of Distributed Model predictive control systems. Our goal is to not only conceptually review the results in this area but also to provide enough algorithmic details so that the advantages and disadvantages of the various approaches can become quite clear. In this sense, our hope is that this paper would complement a series of recent review papers and catalyze future research in this rapidly evolving area. We conclude discussing our viewpoint on future research directions in this area.
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Multirate Distributed Model Predictive Control
Networked and Distributed Predictive Control, 2011Co-Authors: Panagiotis D Christofides, Jinfeng Liu, David Muñoz De La PeñaAbstract:In Chap. 6, a multirate Distributed Model predictive control design for large-scale nonlinear uncertain systems with fast and slowly sampled states is developed. The Distributed Model predictive controllers are connected through a shared communication network and cooperate in an iterative fashion at time instants in which both fast and slowly sampled measurements are available, to guarantee closed-loop stability. When only local subsystem fast sampled state information is available, the Distributed controllers operate in a decentralized fashion to improve closed-loop performance. In the design of the Distributed controllers, bounded measurement noise, process disturbances and communication noise are also taken into account. Using a reactor–separator process example, the stability property and performance of the multirate Distributed predictive control architecture is illustrated.
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sequential and iterative architectures for Distributed Model predictive control of nonlinear process systems
Aiche Journal, 2010Co-Authors: Jinfeng Liu, David Muñoz De La Peña, Xianzhong Che, Panagiotis D ChristofidesAbstract:In this work, we focus on Distributed Model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different Model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the Distributed controllers at each sampling time and a Model of the plant is available, we propose two different Distributed Model predictive control architectures. In the first architecture, the Distributed controllers use a one-directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the Distributed controllers utilize a bi-directional communication strategy, are evaluated in parallel and iterate to improve closed-loop performance. In the design of the Distributed Model predictive controllers, Lyapunov-based Model predictive control techniques are used. To ensure the stability of the closed-loop system, each Model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov-based controller. We prove that the proposed Distributed Model predictive control architectures enforce practical stability in the closed-loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. V C 2010 American Institute of Chemical Engineers AIChE J, 56: 2137–2149, 2010
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Distributed Model predictive control of nonlinear process systems
Aiche Journal, 2009Co-Authors: David Muñoz De La Peña, Panagiotis D ChristofidesAbstract: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 are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009
Yuan De-cheng - One of the best experts on this subject based on the ideXlab platform.
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Distributed Model Predictive Control:A Survey
Computer Simulation, 2008Co-Authors: Yuan De-chengAbstract:Some correlative problems about Distributed Model predictive control were reviewed and Distributed Model predictive control was classified in application.Some approaches in recent research were taken for decomposing system Model,decomposing control problem into sub-problem and finding a suitable solution to those were analyzed,how controllers communicate with each other to come to a solution were reviewed.The evolvement of the system analysis approach relatively was introduced and the insufficiencies were reviewed.Finally,future research orientations of Distributed Model predictive control were prospected.
