What are the mathematical models used in developing MLD adaptable model predictive controllers?
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The mathematical models used in developing MLD adaptable model predictive controllers include regularized finite impulse response models , adaptive regression-based MPC , and fuzzy Takagi-Sugeno (T-S) models . Regularized finite impulse response models are utilized to decrease variance error in online parameter estimation and ensure robust system identification, particularly for controlling time-variant or nonlinear processes. The adaptive regression-based MPC predicts the optimal horizon length and sample count by training a support vector regressor on features extracted from time-varying state changes, leading to a significant reduction in computational time without sacrificing performance. On the other hand, fuzzy T-S models are employed to build fuzzy predictors that minimize uncertainties and provide stable control laws, as demonstrated in simulations on a chemical process.
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| Papers (5) | Insight |
|---|---|
| The paper utilizes a fuzzy Takagi-Sugeno model and a fuzzy predictor to develop a fuzzy adaptive MPC for nonlinear time-varying delayed systems in a chemical process simulation. | |
Proceedings Article 13 Jun 2023 | Not addressed in the paper. |
| The paper proposes an adaptive regression-based MPC that utilizes a support vector regressor to predict the optimal horizon length and sample count, enhancing computational efficiency without sacrificing performance. | |
01 Jan 2022 | Regularized finite impulse response models are utilized in developing MLD adaptable model predictive controllers, enhancing parameter estimation, system identification, and control for nonlinear processes. |
1 Citations | Not addressed in the paper. |
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