The topic of this paper is a new modelpredictivecontrol (MPC) approach for the sampled-data implementation of continuous-time stabilizing feedback laws. The given continuous-time feedback controller is used to gener...
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ISBN:
(纸本)9783540726982
The topic of this paper is a new modelpredictivecontrol (MPC) approach for the sampled-data implementation of continuous-time stabilizing feedback laws. The given continuous-time feedback controller is used to generate a reference trajectory which we track numerically using a sampled-data controller via an MPC strategy. Here our goal is to minimize the mismatch between the reference solution and the trajectory under control. We summarize the necessary theoretical results, discuss several aspects of the numerical implemenation and illustrate the algorithm by an example.
In this contribution we present two interior-point path-following algorithms that solve the convex optimisation problem that arises in recentred barrier function modelpredictivecontrol (MPC), which includes standard...
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ISBN:
(纸本)9783540726982
In this contribution we present two interior-point path-following algorithms that solve the convex optimisation problem that arises in recentred barrier function modelpredictivecontrol (MPC), which includes standard MPC as a limiting case. However the optimisation problem that arises in nonlinear MPC may not be convex. In this case we propose sequential convex programming (SCP) as an alternative to sequential quadratic programming. The algorithms are appropriate for the convex program that arises at each iteration of such an SCP.
A problem of synthesis of optimal measurement feedbacks for dynamical systems under uncertainty is under consideration. An online control scheme providing a guaranteed result under the worst-case conditions is described.
ISBN:
(纸本)9783540726982
A problem of synthesis of optimal measurement feedbacks for dynamical systems under uncertainty is under consideration. An online control scheme providing a guaranteed result under the worst-case conditions is described.
This paper presents a review of recent contributions that unite predictivecontrol approaches with Lyapunov-based control approaches at the implementation level (Hybrid predictivecontrol) and at the design level (Lya...
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ISBN:
(纸本)9783540726982
This paper presents a review of recent contributions that unite predictivecontrol approaches with Lyapunov-based control approaches at the implementation level (Hybrid predictivecontrol) and at the design level (Lyapunov-based predictivecontrol) in a way that allows for an explicit characterization of the set of initial conditions starting from where closed-loop stability is guaranteed in the presence of constraints.
nonlinearmodelpredictivecontrollers (NLMPC) using fundamental dynamic models and online nonlinear optimization have been in service in ExxonMobil Chemical since 1994. The NLMPC algorithm used in this work employs a...
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ISBN:
(纸本)9783540726982
nonlinearmodelpredictivecontrollers (NLMPC) using fundamental dynamic models and online nonlinear optimization have been in service in ExxonMobil Chemical since 1994. The NLMPC algorithm used in this work employs a state space formulation, a finite prediction horizon, a performance specification in terms of desired closed loop response characteristics for the outputs, and costs on incremental manipulated variable action. The controller can utilize fundamental or empirical models. The simulation and optimization problems are solved simultaneously using sequential quadratic programming (SQP). In the paper, we present results illustrating regulatory and grade transition (servo) control by NLMPC on several industrial polymerization processes. The paper outlines the NLMPC technology employed, describes the current status in industry for extending linear modelpredictivecontrol to nonlinear processes or applying NLMPC directly, and identifies several needs for improvements to components of NLMPC.
In many practical situations in process industry, the measurements of process quality variables, such as product concentrations, are available at different sampling rates and than other measured variables and also at ...
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A number of plants of technological interest include transport phenomena in which mass, or energy, or both, flow along one space dimension, with or without reactions taking place, but with neglected dispersion. This t...
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This paper summarizes recent developments and applications of dynamic real-time optimization (D-RTO). A decomposition strategy is presented to separate economical and control objectives by formulating two subproblems ...
ISBN:
(纸本)9783540726982
This paper summarizes recent developments and applications of dynamic real-time optimization (D-RTO). A decomposition strategy is presented to separate economical and control objectives by formulating two subproblems in closed-loop. Two approaches (model-based and model-free at the implementation level) are developed to provide tight integration of economical optimization and control, and to handle uncertainty. Simulated industrial applications involving different dynamic operational scenarios demonstrate significant economical benefits.
This paper focuses on the design of a nonlinearmodelpredictivecontrol (NMPC) scheme for a cement grinding circuit, i.e., a ball mill in closed loop with an air classifier. The multivariable controller uses two mass...
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ISBN:
(纸本)9783540726982
This paper focuses on the design of a nonlinearmodelpredictivecontrol (NMPC) scheme for a cement grinding circuit, i.e., a ball mill in closed loop with an air classifier. The multivariable controller uses two mass fractions as controlled variables, and the input flow rate and the classifier selectivity as manipulated variables. As the particle size distribution inside the mill is not directly measurable, a receding-horizon observer is designed, using measurements at the mill exit only. The performance of the control scheme in the face of measurement errors and plant-model mismatches is investigated in simulation.
The application of nonlinearmodelpredictivecontrol (NMPC) for the temperature control of an industrial batch polymerization reactor is illustrated. A real-time formulation of the NMPC that takes computational delay...
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ISBN:
(纸本)9783540726982
The application of nonlinearmodelpredictivecontrol (NMPC) for the temperature control of an industrial batch polymerization reactor is illustrated. A real-time formulation of the NMPC that takes computational delay into account and uses an efficient multiple shooting algorithm for on-line optimization problem is described. The control relevant model used in the NMPC is derived from the complex first-principles model and is fitted to the experimental data using maximum likelihood estimation. A parameter adaptive extended Kalman filter (PAEKF) is used for state estimation and on-line model adaptation. The performance of the NMPC implementation is assessed via simulation and experimental studies.
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