Data‐driven plant‐model mismatch estimation for dynamic matrix control systems

Xu, Xiaodong; Simkoff, Jodie M.; Bâldea, Michael; Chiang, Leo H.; Castillo, Iván; Bindlish, Rahul; Ashcraft, Brian

doi:10.1002/rnc.5162

Cited by 18 publications

(18 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, in the practical industrial processes, due to the changes of the production environment and operating conditions, there will be drifts in model parameters and thus there are usually model uncertainties between the real process and the pre‐obtained mathematical model 33 . The robustness is a significant and practical advantage of the proposed controller in the work.…”

Section: Simulation Resultsmentioning

confidence: 99%

Robust tracking control of column froth flotation process with an unknown disturbance

Dubljević

2021

AIChE Journal

Self Cite

View full text Add to dashboard Cite

This manuscript develops a robust output regulator for the column froth flotation process in the presence of unknown disturbance. In particular, motivated by relevant physical modeling setting, all information (including magnitude, phase, bias, and frequency) of the disturbance is unavailable. The system considered is consisting of froth and collection zones and is governed by a set of conservative heterodirectional transport hyperbolic partial differential equations (PDEs). Moreover, the transport related time delay between the froth and the collection region is considered, and is associated with the boundary conditions of the PDEs. The velocity of the feed is considered the input and the concentration of the solid particle with air phase at the top of froth region as the output. We propose a disturbance frequency identifier and formulate a robust controller according to the internal model principle such that the output tracks the desired concentration despite the model uncertainties and unknown disturbance.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Robust tracking control of column froth flotation process with an unknown disturbance

Dubljević

2021

AIChE Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, renewing models requires re‐identification, which entails invasive plant tests. Such tests will have economic repercussions as it will cause harmful impacts to the equipment and disrupt the normal operation of the plant 18,19 . Performance problems have been reported in up to 60% of all industrial controllers 20 .…”

Section: Introductionmentioning

confidence: 99%

“…Simkoff et al 12 presented a novel auto‐covariance‐based plant model mismatch estimation method for unconstrained MPC based on linear state space models. Xu et al 18 developed an autocovariance‐based method to identify mismatches. The method estimates mismatches by minimizing the error between the theoretical autocovariance matrices derived from implementing the explicit dynamic matrix control (DMC) control law and the autocovariance matrices of the sampled data.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven plant‐model mismatch quantification in closed‐loop system based on output predictions

Shi,

Xu,

Dubljevic

2024

AIChE Journal

Self Cite

View full text Add to dashboard Cite

The assessment and diagnosis of controller performance for model‐based closed‐loop control systems has received considerable attention in recent years. A recognized factor dilapidating the controller performance is mismatching the true dynamical model of the plant and the mathematic model employed in the controller. In order to reduce the heavy effort to re‐identify the entire model, a large amount of recent works have been focusing on locating the mismatch. To further improve the mathematic model as well as controller performance, in this article, we provide a novel prediction‐based plant‐model mismatch quantification approach, which belongs to the class of moment match methodology. In particular, two cases of output prediction are considered: one‐step ahead prediction and multistep ahead prediction. Compared with existing efforts along the same line of mismatch quantification, when using the one‐step ahead prediction, our method shows an advantage of light computational complexity. On the other hand, when utilizing multistep predictions, it shows better convergence and robustness than the former, especially in the case of structural mismatches, and the long‐term prediction capability of the model is employed. With both predictions and consideration of the existence of unknown noise models in practice, we show that the plant‐model mismatch and unknown parameters of noise models can be quantified as the solution of a minimization issue that penalizes the discrepancy between the sample of plant outputs and the predictions calculated by considering the plant‐model mismatch and noise model parameters. Several examples are provided to demonstrate the effectiveness of the proposed methods, respectively.

show abstract

“…Due to the unknown interference or unmodeled dynamics, model plant mismatch often exists between the real plant and established model. The existence of model plant mismatch will deteriorate the tracking performance and control performance and may even lead to the system instability 1‐4 . Linear systems and nonlinear systems under model plant mismatch have received widespread attention, but the research on switched systems under model plant mismatch is rare.…”

Section: Introductionmentioning

confidence: 99%

Robust exponential stability analysis of switched systems under switching boundary mismatch

Song

Zhao

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Switching boundary is an integral part of the switched system, but the existing literature on switched systems does not focus on the system under switching boundary mismatch. In this article, the switching boundary mismatch is considered, and the control strategy based on observation algorithm is designed for the state dependent switched system. By implementing the proposed strategy, the system can realize robust exponential stability and track the desired trajectory. Since the switching boundary mismatch is unmeasurable, an observation algorithm is designed. Combining the observation algorithm and iterative learning control law, the new control strategy can be derived. According to the Lyapunov stability theory and mode dependent average dwell time method, the robust exponential stability conditions of the closed-loop system based on linear matrix inequalities are given. The mode dependent average dwell time of each subsystem can be solved simultaneously. Finally, simulations have been taken to verify the designed strategy can render the closed-loop system achieve robust exponential stability and can track the desired trajectory.

show abstract

Data‐driven plant‐model mismatch estimation for dynamic matrix control systems

Cited by 18 publications

References 20 publications

Robust tracking control of column froth flotation process with an unknown disturbance

Robust tracking control of column froth flotation process with an unknown disturbance

Data‐driven plant‐model mismatch quantification in closed‐loop system based on output predictions

Robust exponential stability analysis of switched systems under switching boundary mismatch

Contact Info

Product

Resources

About