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Panagiotis D Christofides - One of the best experts on this subject based on the ideXlab platform.
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decentralized machine learning based predictive control of nonlinear processes
Chemical Engineering Research & Design, 2020Co-Authors: Scarlett Chen, Zhe Wu, Panagiotis D ChristofidesAbstract:Abstract This work focuses on the design of decentralized model predictive control (MPC) systems for nonlinear processes, where the nonlinear process is broken down into multiple, yet coupled subsystems and the dynamic behavior of each subsystem is described by a machine learning model. One decentralized MPC is designed and used to control each subsystem while accounting for the interactions between subsystems through feedback of the entire process state. The closed-Loop Stability of the overall nonlinear process network and the performance properties of the decentralized model predictive control system using machine-learning prediction models are analyzed. More specifically, multiple recurrent neural network models suited for each different subsystem need to be trained with a sufficiently small modeling error from their respective actual nonlinear process models to ensure closed-Loop Stability. These recurrent neural network models are subsequently used as the prediction model in decentralized Lyapunov-based MPCs to achieve efficient real-time computation time while ensuring closed-Loop state boundedness and convergence to the origin. The simulation results of a nonlinear chemical process network example demonstrate the effective closed-Loop control performance when the process is operated under the decentralized MPCs using the independently-trained recurrent neural network models, as well as the improved computational efficiency compared to the closed-Loop simulation of a centralized MPC system.
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control lyapunov barrier function based predictive control of nonlinear processes using machine learning modeling
Computers & Chemical Engineering, 2020Co-Authors: Zhe Wu, Panagiotis D ChristofidesAbstract:Abstract Control Lyapunov-Barrier functions (CLBF) have been adopted to design model predictive controllers (MPC) for input-constrained nonlinear systems to ensure closed-Loop Stability and process operational safety simultaneously. In this work, a CLBF-MPC using an ensemble of recurrent neural network (RNN) models is proposed with guaranteed closed-Loop Stability and process operational safety for two types of unsafe regions, i.e., bounded and unbounded sets, for nonlinear processes. The application of the proposed RNN-based CLBF-MPC method is demonstrated through a chemical process example.
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handling bounded and unbounded unsafe sets in control lyapunov barrier function based model predictive control of nonlinear processes
Chemical Engineering Research & Design, 2019Co-Authors: Panagiotis D ChristofidesAbstract:Abstract Control Lyapunov-Barrier function (CLBF) has been used to design controllers for nonlinear systems subject to input constraints to ensure closed-Loop Stability and process operational safety simultaneously. In this work, we developed Control Lyapunov-Barrier functions for two types of unsafe regions (i.e., bounded and unbounded sets) to solve the problem of stabilization of nonlinear systems with guaranteed process operational safety. Specifically, in the presence of a bounded unsafe region embedded within the closed-Loop system Stability region, the CLBF-MPC is developed by incorporating CLBF-based constraints and discontinuous control actions at potential stationary points (except the origin) to guarantee the convergence to the origin (i.e., closed-Loop Stability) and the avoidance of unsafe region (i.e., process operational safety). In the case of unbounded unsafe sets, closed-Loop Stability with safety is readily guaranteed under the CLBF-MPC since the origin is the unique stationary point in state-space. The application of the proposed CLBF-MPC method is demonstrated through a chemical process example with a bounded and an unbounded unsafe region, respectively.
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achieving operational process safety via model predictive control
Journal of Loss Prevention in The Process Industries, 2018Co-Authors: Fahad Albalawi, Helen Durand, Anas Alanqar, Panagiotis D ChristofidesAbstract:Abstract Model predictive control (MPC) has been widely adopted in the chemical and petrochemical industry due to its ability to account for actuator constraints and multi-variable interactions for complex processes. However, closed-Loop Stability is not guaranteed within the framework of MPC without additional constraints or assumptions. An MPC formulation that can guarantee closed-Loop Stability in the presence of uncertainty is Lyapunov-based model predictive control (LMPC) which incorporates Stability constraints based on a stabilizing Lyapunov-based controller. Though LMPC drives the closed-Loop state trajectory to a steady-state, it lacks the ability to adjust the rate at which the closed-Loop state approaches the steady-state in an explicit manner. However, there may be circumstances in which it would be desirable, for safety reasons, to be able to adjust this rate to avoid triggering of safety alarms or process shut-down. In addition, there may be scenarios in which the current region of operation is no longer safe to operate within, and another region of operation (i.e., a region around another steady-state) is appropriate. Motivated by these considerations, this work develops two novel LMPC schemes that can drive the closed-Loop state to a safety region (a level set within the Stability region where process functional safety is ensured) at a prescribed rate or can drive the closed-Loop state to a safe level set within the Stability region of another steady-state. Recursive feasibility and closed-Loop Stability are established for a sufficiently small LMPC sampling period. A comparison between the proposed method, which effectively integrates feedback control and safety considerations, and the classical LMPC method is demonstrated with a chemical process example. The chemical process example demonstrates that the safety-LMPC drives the closed-Loop state into a safe level set of the Stability region two sampling times faster than under the classical LMPC in the presence of process uncertainty.
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real time preventive sensor maintenance using robust moving horizon estimation and economic model predictive control
Aiche Journal, 2015Co-Authors: Liangfeng Lao, Helen Durand, Matthew Ellis, Panagiotis D ChristofidesAbstract:Conducting preventive maintenance of measurement sensors in real-time during process operation under feedback control while ensuring the reliability and improving the economic performance of a process is a central problem of the research area focusing on closed-Loop preventive maintenance of sensors and actuators. To address this problem, a robust moving horizon estimation (RMHE) scheme and an economic model predictive control system are combined to simultaneously achieve preventive sensor maintenance and optimal process economic performance with closed-Loop Stability. Specifically, given a preventive sensor maintenance schedule, a RMHE scheme is developed that accommodates varying numbers of sensors to continuously supply accurate state estimates to a Lyapunov-based economic model predictive control (LEMPC) system. Closed-Loop Stability for this control approach can be proven under fairly general observability and stabilizability assumptions to be made precise in the manuscript. Subsequently, a chemical process example incorporating this RMHE-based LEMPC scheme demonstrates its ability to maintain process Stability and achieve optimal process economic performance as scheduled preventive maintenance is performed on the sensors. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3374–3389, 2015
Xavier Darzacq - One of the best experts on this subject based on the ideXlab platform.
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ctcf and cohesin regulate chromatin Loop Stability with distinct dynamics
eLife, 2017Co-Authors: Anders S Hansen, Claudia Cattoglio, Iryna Pustova, Robert Tjian, Xavier DarzacqAbstract:Folding of mammalian genomes into spatial domains is critical for gene regulation. The insulator protein CTCF and cohesin control domain location by folding domains into Loop structures, which are widely thought to be stable. Combining genomic and biochemical approaches we show that CTCF and cohesin co-occupy the same sites and physically interact as a biochemically stable complex. However, using single-molecule imaging we find that CTCF binds chromatin much more dynamically than cohesin (~1-2 min vs. ~22 min residence time). Moreover, after unbinding, CTCF quickly rebinds another cognate site unlike cohesin for which the search process is long (~1 min vs. ~33 min). Thus, CTCF and cohesin form a rapidly exchanging 'dynamic complex' rather than a typical stable complex. Since CTCF and cohesin are required for Loop domain formation, our results suggest that chromatin Loops are dynamic and frequently break and reform throughout the cell cycle.
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CTCF and Cohesin Regulate Chromatin Loop Stability with Distinct Dynamics
2016Co-Authors: Anders S Hansen, Claudia Cattoglio, Iryna Pustova, Robert Tjian, Xavier DarzacqAbstract:Folding of mammalian genomes into spatial domains is critical for gene regulation. CTCF and cohesin control domain location by folding domains into Loop structures, which are thought to be highly stable. Combining genomic, biochemical and single-molecule imaging approaches, we show that although CTCF and cohesin can physically interact, CTCF binds chromatin much more dynamically than cohesin (~1 min vs. ~22 min residence time). Moreover, after unbinding, CTCF quickly rebinds another cognate site unlike cohesin (~1 min vs. ~33 min). Thus, CTCF and cohesin form a rapidly exchanging "dynamic complex" rather than a typical stable complex. Since CTCF and cohesin are required for Loop domain formation, our results suggest that chromatin Loops constantly break and reform throughout the cell cycle.
Anders S Hansen - One of the best experts on this subject based on the ideXlab platform.
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ctcf and cohesin regulate chromatin Loop Stability with distinct dynamics
eLife, 2017Co-Authors: Anders S Hansen, Claudia Cattoglio, Iryna Pustova, Robert Tjian, Xavier DarzacqAbstract:Folding of mammalian genomes into spatial domains is critical for gene regulation. The insulator protein CTCF and cohesin control domain location by folding domains into Loop structures, which are widely thought to be stable. Combining genomic and biochemical approaches we show that CTCF and cohesin co-occupy the same sites and physically interact as a biochemically stable complex. However, using single-molecule imaging we find that CTCF binds chromatin much more dynamically than cohesin (~1-2 min vs. ~22 min residence time). Moreover, after unbinding, CTCF quickly rebinds another cognate site unlike cohesin for which the search process is long (~1 min vs. ~33 min). Thus, CTCF and cohesin form a rapidly exchanging 'dynamic complex' rather than a typical stable complex. Since CTCF and cohesin are required for Loop domain formation, our results suggest that chromatin Loops are dynamic and frequently break and reform throughout the cell cycle.
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CTCF and Cohesin Regulate Chromatin Loop Stability with Distinct Dynamics
2016Co-Authors: Anders S Hansen, Claudia Cattoglio, Iryna Pustova, Robert Tjian, Xavier DarzacqAbstract:Folding of mammalian genomes into spatial domains is critical for gene regulation. CTCF and cohesin control domain location by folding domains into Loop structures, which are thought to be highly stable. Combining genomic, biochemical and single-molecule imaging approaches, we show that although CTCF and cohesin can physically interact, CTCF binds chromatin much more dynamically than cohesin (~1 min vs. ~22 min residence time). Moreover, after unbinding, CTCF quickly rebinds another cognate site unlike cohesin (~1 min vs. ~33 min). Thus, CTCF and cohesin form a rapidly exchanging "dynamic complex" rather than a typical stable complex. Since CTCF and cohesin are required for Loop domain formation, our results suggest that chromatin Loops constantly break and reform throughout the cell cycle.
Joao P Hespanha - One of the best experts on this subject based on the ideXlab platform.
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mistuning based control design to improve closed Loop Stability margin of vehicular platoons
IEEE Transactions on Automatic Control, 2009Co-Authors: Prabir Barooah, Prashant G Mehta, Joao P HespanhaAbstract:We consider a decentralized bidirectional control of a platoon of N identical vehicles moving in a straight line. The control objective is for each vehicle to maintain a constant velocity and inter-vehicular separation using only the local information from itself and its two nearest neighbors. Each vehicle is modeled as a double integrator. To aid the analysis, we use continuous approximation to derive a partial differential equation (PDE) approximation of the discrete platoon dynamics. The PDE model is used to explain the progressive loss of closed-Loop Stability with increasing number of vehicles, and to devise ways to combat this loss of Stability. If every vehicle uses the same controller, we show that the least stable closed-Loop eigenvalue approaches zero as O(1/N2) in the limit of a large number (N) of vehicles. We then show how to ameliorate this loss of Stability by small amounts of "mistuning", i.e., changing the controller gains from their nominal values. We prove that with arbitrary small amounts of mistuning, the asymptotic behavior of the least stable closed Loop eigenvalue can be improved to O(1/N). All the conclusions drawn from analysis of the PDE model are corroborated via numerical calculations of the state-space platoon model.
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mistuning based control design to improve closed Loop Stability of vehicular platoons
arXiv: Optimization and Control, 2008Co-Authors: Prabir Barooah, Prashant G Mehta, Joao P HespanhaAbstract:We consider a decentralized bidirectional control of a platoon of N identical vehicles moving in a straight line. The control objective is for each vehicle to maintain a constant velocity and inter-vehicular separation using only the local information from itself and its two nearest neighbors. Each vehicle is modeled as a double integrator. To aid the analysis, we use continuous approximation to derive a partial differential equation (PDE) approximation of the discrete platoon dynamics. The PDE model is used to explain the progressive loss of closed-Loop Stability with increasing number of vehicles, and to devise ways to combat this loss of Stability. If every vehicle uses the same controller, we show that the least stable closed-Loop eigenvalue approaches zero as O(1/N^2) in the limit of a large number (N) of vehicles. We then show how to ameliorate this loss of Stability by small amounts of "mistuning", i.e., changing the controller gains from their nominal values. We prove that with arbitrary small amounts of mistuning, the asymptotic behavior of the least stable closed Loop eigenvalue can be improved to O(1/N) All the conclusions drawn from analysis of the PDE model are corroborated via numerical calculations of the state-space platoon model.
Zhe Wu - One of the best experts on this subject based on the ideXlab platform.
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decentralized machine learning based predictive control of nonlinear processes
Chemical Engineering Research & Design, 2020Co-Authors: Scarlett Chen, Zhe Wu, Panagiotis D ChristofidesAbstract:Abstract This work focuses on the design of decentralized model predictive control (MPC) systems for nonlinear processes, where the nonlinear process is broken down into multiple, yet coupled subsystems and the dynamic behavior of each subsystem is described by a machine learning model. One decentralized MPC is designed and used to control each subsystem while accounting for the interactions between subsystems through feedback of the entire process state. The closed-Loop Stability of the overall nonlinear process network and the performance properties of the decentralized model predictive control system using machine-learning prediction models are analyzed. More specifically, multiple recurrent neural network models suited for each different subsystem need to be trained with a sufficiently small modeling error from their respective actual nonlinear process models to ensure closed-Loop Stability. These recurrent neural network models are subsequently used as the prediction model in decentralized Lyapunov-based MPCs to achieve efficient real-time computation time while ensuring closed-Loop state boundedness and convergence to the origin. The simulation results of a nonlinear chemical process network example demonstrate the effective closed-Loop control performance when the process is operated under the decentralized MPCs using the independently-trained recurrent neural network models, as well as the improved computational efficiency compared to the closed-Loop simulation of a centralized MPC system.
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control lyapunov barrier function based predictive control of nonlinear processes using machine learning modeling
Computers & Chemical Engineering, 2020Co-Authors: Zhe Wu, Panagiotis D ChristofidesAbstract:Abstract Control Lyapunov-Barrier functions (CLBF) have been adopted to design model predictive controllers (MPC) for input-constrained nonlinear systems to ensure closed-Loop Stability and process operational safety simultaneously. In this work, a CLBF-MPC using an ensemble of recurrent neural network (RNN) models is proposed with guaranteed closed-Loop Stability and process operational safety for two types of unsafe regions, i.e., bounded and unbounded sets, for nonlinear processes. The application of the proposed RNN-based CLBF-MPC method is demonstrated through a chemical process example.
