Disturbance feedback in model predictive control systems

Navrátil, Jan; Lim, Kian Yong; Fisher, D. Grant

doi:10.1016/b978-0-08-035735-5.50013-9

Cited by 13 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some researchers (Navratil, 1988) have suggested the use of step response coefficients to describe the effect of disturbances on the output. In this case, T should contain the step response coefficients describing the change of the output caused by the changes in the disturbances.…”

Section: External Disturbance and Measurement Noisementioning

confidence: 99%

“…K is the optimal Kalman filter gain (20) or (28) for the specific case of disturbances in the form of integrated white noise filtered through first-order dynamics.…”

Section: Prediction With Correction Based On Measurementsmentioning

confidence: 99%

“…Since the general state-space model or the CARIMA model includes the step response model as a special case, extension of the traditional MPC (step response) techniques to accommodate general stochastic disturbances and measurement noise based on the above-mentioned developments is straightforward, at least in concept. However, direct application of the state estimation technique (Li et al, 1989;Navratil et al, 1988) to step response models adds significant numerical complexity such as the requirement to solve a Riccati equation of potentially very large order. The order is generally equal to the number of step response coefficients times the number of outputs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

State-Space Interpretation of Model Predictive Control

Lee

Morari

García

1995

Methods of Model Based Process Control

View full text Add to dashboard Cite

A model predictive control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal control can be used to construct the optimal prediction vector without introducing significant additional numerical complexity. In the case of integrated or double integrated white noise disturbances filtered through general first-order dynamics and white measurement noise, the optirnal filter gain is parametrized explicitly

show abstract

Section: External Disturbance and Measurement Noisementioning

confidence: 99%

“…K is the optimal Kalman filter gain (20) or (28) for the specific case of disturbances in the form of integrated white noise filtered through first-order dynamics.…”

Section: Prediction With Correction Based On Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

State-Space Interpretation of Model Predictive Control

Lee

Morari

García

1995

Methods of Model Based Process Control

View full text Add to dashboard Cite

show abstract

“…The MPC formulation by state‐space models [22] allows for the generalization to complex cases. The authors [23, 24] present how an MPC is developed by the optimal stochastic control theory. How a traditional MPC algorithm can be developed by state‐space models is shown in [25].…”

Section: Introductionmentioning

confidence: 99%

Incremental state model in predictive control: A new fuzzy control proposal for nonlinear systems

Al‐Hadithi

Adánez

Comina

et al. 2022

IET Control Theory & Appl

View full text Add to dashboard Cite

In this work, a fuzzy predictive optimal control for multivariable nonlinear systems with pure time delays is presented. Therefore, dynamic local linear state models are used at each point of the state space obtained by fuzzy Takagi‐Sugeno (T‐S) modelling. The modelling error is considered as white noise, and the state is observed using the Kalman Filter (KF). This method does not take into account possible input constraints. Therefore, it applies to systems where there are no saturation problems. In this work, a new approach in the Model Predictive Control (MPC) method is proposed by calculating the control signal increment as a function of the error between a reference state vector and the prediction at N‐steps of the state vector instead of using the traditional MPC approach which is based on calculating the error between a reference and the predicted output. The proposed method has a satisfactory tracking performance. The main feature of the proposed method is that it attains computational savings compared to other methods that have used incremental state models, which making it more appropriate for real‐time applications.

show abstract

“…This not only permits the use of well-known state-space theorems, but also allows straightforward generalization to more complex cases such as systems with general stochastic disturbances and measurement noise. Li et al (1989) and Navratil et al (1988) showed that the step response model can be put into the general state-space model structure and presented an MPC technique using the tools available from stochastic optimal control theory. They showed how open-loop and closed-loop observers can be incorporated into the predictive control framework to improve regulatory control of MPC.…”

Section: Introductionmentioning

confidence: 99%

Improved State Estimation in an MPC Algorithm Based on Fuzzy Decision

Daneshpour

Motlagh

2005

IFAC Proceedings Volumes

View full text Add to dashboard Cite

Due to the difficulties arising in state estimation in Model Predictive Control (MPC) algorithms, Kalman filtering and dynamic matrix control (DMC) estimation approaches were combined in the current work. Then a weighting average of both estimated states was passed to the algorithm. To determine the weighting coefficient of the mentioned average, a fuzzy supervisor was designed to control the combined estimation. An industrial process 'heavy oil fractionator' was used for simulation. The results demonstrated the improved performance of the approach particularly in better disturbance rejection capability.

show abstract

Disturbance feedback in model predictive control systems

Cited by 13 publications

References 3 publications

State-Space Interpretation of Model Predictive Control

State-Space Interpretation of Model Predictive Control

Incremental state model in predictive control: A new fuzzy control proposal for nonlinear systems

Improved State Estimation in an MPC Algorithm Based on Fuzzy Decision

Contact Info

Product

Resources

About