For the first time, a distributed output feedback control scheme is presented which combines distributed model predictive control with distributed moving horizon estimation. More specifically, we combine the iterative...
For the first time, a distributed output feedback control scheme is presented which combines distributed model predictive control with distributed moving horizon estimation. More specifically, we combine the iterative methods of sensitivity-driven distributed model predictive control (S-DMPC) with sensitivity-driven partition-based moving horizon estimation (S-PMHE). To that end, S-PMHE is extended such that it can handle inputs of S-DMPC. The resulting distributed output feedback scheme is then applied to an alkylation benchmark process from the literature. We find that its control performance is comparable to that of fully centralized MPC and MHE but our distributed output feedback scheme is faster.
In this work, two numerical solution methods are presented for discounted economic nonlinear model predictive control on infinite horizons without terminal constraints. While the first formulation simply replaces the ...
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We transfer the ideas behind sensitivity-driven distributed model predictive control (c.f. Scheu and Marquardt, 2011) to the moving horizon state estimation problem and present a novel decentralized state estimation a...
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ISBN:
(纸本)9781479901890
We transfer the ideas behind sensitivity-driven distributed model predictive control (c.f. Scheu and Marquardt, 2011) to the moving horizon state estimation problem and present a novel decentralized state estimation algorithm, namely, sensitivity-driven partition-based moving horizon estimation (S-PMHE). We discuss convergence and optimality of S-PMHE for the case of given positive-definite arrival cost weights. Finally, we demonstrate the method on a numerical example.
In this work, two numerical solution methods are presented for discounted economic nonlinear model predictive control on infinite horizons without terminal constraints. While the first formulation simply replaces the ...
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In this work, two numerical solution methods are presented for discounted economic nonlinear model predictive control on infinite horizons without terminal constraints. While the first formulation simply replaces the infinite by a finite horizon, the second formulation uses a time transformation function to project the infinite to a finite horizon. For the first formulation, an algorithm is presented which heuristically determines a sufficiently long final time with the help of the turnpike property in order to ensure good closed-loop control performance. For the second formulation, a two-stage formulation is introduced to deal with large differences in the dynamics of the objective function and the states. The solution accuracy is improved for both formulations by using a control vector adaptation strategy such that an adequate number of decision variables is obtained. Both solution methods are compared in a case study.
Model Predictive Control (MPC) is a powerful tool in the control of large scale chemical processes and has become the standard method for constrained multivariable control problems. Hence, the number of MPC applicatio...
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Model Predictive Control (MPC) is a powerful tool in the control of large scale chemical processes and has become the standard method for constrained multivariable control problems. Hence, the number of MPC applicatio...
Model Predictive Control (MPC) is a powerful tool in the control of large scale chemical processes and has become the standard method for constrained multivariable control problems. Hence, the number of MPC applications is increasing steadily and it is being used in application domains other than petrochemical industries. A common observation by the industrial practitioners is that success of any MPC application requires not only encient initial deployment but also maintenance of initial effectiveness. To this end, we propose a novel high level automated support strategy for MPC systems. Such a strategy consists of components such as performance monitoring, performance diagnosis, least costly closed loop experiment design, re-identification and autotuning. This work presents the novel technological developments in each component and demonstrates them on a distillation column case study. We show that automated support strategy restores nominal performance after a performance drop is detected and takes the right course of action depending on its cause.
A robust closed-loop iterative learning control (ILC) method is proposed for industrial batch processes with state delay and time-varying uncertainties from cycle to cycle. Based on a twodimensional (2D) system descri...
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This work focuses on the control design for feedback linearizable nonlinear systems with unknown time-varying disturbances and uncertainty such that bounds on inputs and state constraints are not violated. Exploiting ...
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