摘要 :
A new multi-step prediction formulation is developed and used to generate the long range predictions
of the future process outputs required for predictive control. The basic idea behind this new approach
is to simultaneously and...
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A new multi-step prediction formulation is developed and used to generate the long range predictions
of the future process outputs required for predictive control. The basic idea behind this new approach
is to simultaneously and directly construct a separate j-step prediction model for each future output
y(k + j) where j = 1, 2,? N, and N is the prediction horizon. This is different from the conventional
approach, which constructs only the one-step prediction model and calculates the N multi-step output
predictions either by repeated use of the one-step prediction model or by use of the Diophantine
equation. A simulated example is given which shows that the extra computation inherent in the
proposed approach is justified by much better predictions than the conventional approach. The
proposed multiple model prediction approach is then combined with a multiple model control
technique to create a long range predictive controller. Results from an experimental application of this
control strategy to a 2 ? 2 pilot-scale level process demonstrate the excellent control that can be
obtained on a real process.
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摘要 :
A new multi-step prediction formulation is developed and used to generate the long range predictions of the future process outputs required for predictive control. The basic idea behind this new approach is to simultaneously and d...
展开
A new multi-step prediction formulation is developed and used to generate the long range predictions of the future process outputs required for predictive control. The basic idea behind this new approach is to simultaneously and directly construct a separate j-step prediction model for each future output y(k + j) where j = 1, 2,*, N, and N is the prediction horizon. This is different from the conventional approach, which constructs only the one-step prediction model and calculates the N multi-stepoutput predictions either by repeated use of the one-step prediction model or by use of the Diophantine equation. A simulated example is given which shows that the extra computation inherent in the proposed approach is justified by much better predictions than the conventional approach. The proposed multiple model prediction approach is then combined with a multiple model control technique to create a long range predictive controller. Results from an experimental application of this control strategy to a 2 2 pilot-scale level process demonstrate the excellent control that can be obtained on a real process.
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摘要 :
A new multi-step prediction formulation is developed and used to generate the long range predictions of the future process outputs required for predictive control. The basic idea behind this new approach is to simultaneously and d...
展开
A new multi-step prediction formulation is developed and used to generate the long range predictions of the future process outputs required for predictive control. The basic idea behind this new approach is to simultaneously and directly construct a separate j-step prediction model for each future output y(k + j) where j = 1,2, ···, N, and N is the prediction horizon. This is different from the conventional approach, which constructs only the one-step prediction model and calculates the N multi-step output predictions either by repeated use of the one-step prediction model or by use of the Diophantine equation. A simulated example is given which shows that the extra computation inherent in the proposed approach is justified by much better predictions than the conventional approach. The proposed multiple model prediction approach is then combined with a multiple model control technique to create a long range predictive controller. Results from an experimental application of this control strategy to a 2 × 2 pilot-scale level process demonstrate the excellent control that can be obtained on a real process.
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A cyber-physical system (CPS) approach for branch and bound algorithm based direct model predictive control (BnB-DMPC) for grid-tied Active Front End (AFE) power converters is proposed in this work. The proposed control strategy d...
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A cyber-physical system (CPS) approach for branch and bound algorithm based direct model predictive control (BnB-DMPC) for grid-tied Active Front End (AFE) power converters is proposed in this work. The proposed control strategy deals with both the physical states and a cyber state, i.e. the sampling frequency. With the help of cyber controller, the cyber resource and sampling rate are regulated based on the performance of the physical systems and the reference cyber state. The proposed control scheme provides a computationally efficient predictive control alternative. Additionally, the proposed CPS based BnB-MPC controller achieves better performances in comparison with the recently reported branch-and-bound predictive control solution, which is validated with simulation data.
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This short paper investigates the temperature control of a flat-plate water-heating solar collector. This nonlinear system is modelled via a quasi-linear parameter varying setting. To address this control problem, a model predicti...
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This short paper investigates the temperature control of a flat-plate water-heating solar collector. This nonlinear system is modelled via a quasi-linear parameter varying setting. To address this control problem, a model predictive control algorithm is formulated, considering a frozen guess for the evolution of the scheduling parameters, set-sequence constraints and a Lyapunov-decreasing terminal cost. The advantage of this method is that it uses standard quadratic programming problems and does not have to resort to nonlinear optimization. Through simulation, it is demonstrated that it can yield successful performances.
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摘要 :
This short paper investigates the temperature control of a flat-plate water-heating solar collector. This nonlinear system is modelled via a quasi-linear parameter varying setting. To address this control problem, a model predicti...
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This short paper investigates the temperature control of a flat-plate water-heating solar collector. This nonlinear system is modelled via a quasi-linear parameter varying setting. To address this control problem, a model predictive control algorithm is formulated, considering a frozen guess for the evolution of the scheduling parameters, set-sequence constraints and a Lyapunov-decreasing terminal cost. The advantage of this method is that it uses standard quadratic programming problems and does not have to resort to nonlinear optimization. Through simulation, it is demonstrated that it can yield successful performances.
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The purpose of the following article is to introduce new students and researchers in the basic concepts of Model-Based Predictive Control when applied to Multilevel Converters. Therefore, the main aspects of the theory are demonst...
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The purpose of the following article is to introduce new students and researchers in the basic concepts of Model-Based Predictive Control when applied to Multilevel Converters. Therefore, the main aspects of the theory are demonstrated, as well as the steps that should be taken to develop an algorithm capable of controlling the output current of a grid-connected Cascaded Multilevel Converter with an L filter. Also, a simulation of a 7 level Cascaded H-Bridge STATCOM is taken in Matlab/Simulink to prove all the concepts given in this paper.
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摘要 :
The purpose of the following article is to introduce new students and researchers in the basic concepts of Model-Based Predictive Control when applied to Multilevel Converters. Therefore, the main aspects of the theory are demonst...
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The purpose of the following article is to introduce new students and researchers in the basic concepts of Model-Based Predictive Control when applied to Multilevel Converters. Therefore, the main aspects of the theory are demonstrated, as well as the steps that should be taken to develop an algorithm capable of controlling the output current of a grid-connected Cascaded Multilevel Converter with an L filter. Also, a simulation of a 7 level Cascaded H-Bridge STATCOM is taken in Matlab/Simulink to prove all the concepts given in this paper.
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An adaptive generalized predictive control approach based on just-in-time learning(JITL) in latent space is proposed to deal with the problems associating with multivariate, nonlinearity and time-varying characteristics in industr...
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An adaptive generalized predictive control approach based on just-in-time learning(JITL) in latent space is proposed to deal with the problems associating with multivariate, nonlinearity and time-varying characteristics in industrial process systems. To begin with, the latent variable space is constructed by the partial least squares algorithm, thus the complicated multivariable control problem can be decomposed into univariate ones, subsequently the local model of each SISO subsystem can be established online by JITL at every sampling instant in latent space, where the generalized predictive control is implemented to these subsystems. To improve the real-time performance of modeling, the similarity measure will be utilized to determine whether or not to update the current local model at each sampling instant. The proposed approach not only can obtain the satisfactory control results for nonlinear and multivariate system, but also can solve the unstable problem caused by model mismatch. The proposed adaptive predictive control approach is applied to a pH neutralization process. Simulation studies are presented to verify the advantage of the proposed approach.
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摘要 :
An adaptive generalized predictive control approach based on just-in-time learning(JITL) in latent space is proposed to deal with the problems associating with multivariate, nonlinearity and time-varying characteristics in industr...
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An adaptive generalized predictive control approach based on just-in-time learning(JITL) in latent space is proposed to deal with the problems associating with multivariate, nonlinearity and time-varying characteristics in industrial process systems. To begin with, the latent variable space is constructed by the partial least squares algorithm, thus the complicated multivariable control problem can be decomposed into univariate ones, subsequently the local model of each SISO subsystem can be established online by JITL at every sampling instant in latent space, where the generalized predictive control is implemented to these subsystems. To improve the real-time performance of modeling, the similarity measure will be utilized to determine whether or not to update the current local model at each sampling instant. The proposed approach not only can obtain the satisfactory control results for nonlinear and multivariate system, but also can solve the unstable problem caused by model mismatch. The proposed adaptive predictive control approach is applied to a pH neutralization process. Simulation studies are presented to verify the advantage of the proposed approach.
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