Model predictive control for batch processes: Ensuring validity of predictions

Lauri, David; Lennox, Barry; Camacho, José

doi:10.1016/j.jprocont.2013.11.005

Cited by 31 publications

(14 citation statements)

References 37 publications

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“…In addition, the proposed design uses validity restrictions inside the MVT optimization to limit the solution of the QP problem to the region within which there is confidence in the predictions made by the PLS model. Similar validity restrictions have been used before for MPC in the literature [11,23], but not to batch-to-batch optimization.…”

Section: Introductionmentioning

confidence: 93%

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Multivariate batch to batch optimisation of fermentation processes incorporating validity constraints

Duran-Villalobos

Lennox

Lauri

2016

Journal of Process Control

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This paper presents an innovative optimisation technique, which utilises an adaptive Multiway Partial Least Squares (MPLS) model to track the dynamics of a batch process from one batch to the next. Utilising this model, an optimisation algorithm solves a quadratic cost function that identifies operating conditions for the subsequent batch that should increase yield. Hard constraints are shown to be required when solving the cost function to ensure that batch conditions do not vary too greatly from one batch to the next. Furthermore, validity constraints are imposed to prevent the PLS model from extrapolating significantly when determining new operating conditions. The capabilities of the proposed technique are illustrated through its application to two benchmark fermentation simulations, where its performance is shown to compare favourably with alternative batch-to-batch optimisation techniques.

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Section: Introductionmentioning

confidence: 93%

“…Further studies in to the use of multivariate statistical models to regulate batch processes have been conducted by several research groups, including [9][10][11][12][13]. Whilst this work has been demonstrated to be successful, multivariate techniques are not without their limitations.…”

Section: Introductionmentioning

confidence: 99%

Multivariate batch to batch optimisation of fermentation processes incorporating validity constraints

Duran-Villalobos

Lennox

Lauri

2016

Journal of Process Control

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“…In the following paragraphs, it will be proven that the upper bound θ i > 0 of performance function J i ∞ (k) exists, so that (17) can be established. Since 0 <ᾱ i < 1, in combination with formula (28), the following inequality is obtained:…”

Section: The Increment Function Is Defined Asmentioning

confidence: 99%

Improved H-Infinity Hybrid Model Predictive Fault-Tolerant Control for Time-Delayed Batch Processes Against Disturbances

Chen

2020

IEEE Access

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An H-infinity model predictive fault-tolerant control strategy is proposed for multi-phase batch processes with interval delay and actuator failures. First, state variables, state errors and output tracking errors are introduced to establish an extended-state-space switched system model. Then, based on this model, a predictive fault-tolerant control law is designed for tracking the set point by the output that satisfies the requirements of the optimal performance index under input and output constraints. The feasibility conditions for the solvability of the control law are presented in the form of linear matrix inequalities. In addition, the designed switching law is constructed, and the gain of the control law is obtained via the optimization algorithm. This design has several advantages: the output tracking is faster, the tracking performance is superior, and the trace is smoother at the switching time. Finally, through a comparison with traditional methods, the effectiveness and feasibility of this method are demonstrated via injection molding simulation. INDEX TERMS Batch production systems, added delay, fault tolerant control, linear matrix inequalities, Lyapunov methods, predictive models, switching systems, predictive control.

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“…Model predictive control (MPC) has been recognised as an efficient means and applied in many industrial applications successfully [1][2][3][4][5]. MPC refers to a class of computer algorithms that use a dynamic model to predict future behavior of the process [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Optimization Framework for Nonlinear Model Predictive Control

Zhang

Cao

Jia

et al. 2017

Mathematical Problems in Engineering

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The structure of the optimization procedure may affect the control quality of nonlinear model predictive control (MPC). In this paper, a data-driven optimization framework for nonlinear MPC is proposed, where the linguistic model is employed as the prediction model. The linguistic model consists of a series of fuzzy rules, whose antecedents are the membership functions of the input variables and the consequents are the predicted output represented by linear combinations of the input variables. The linear properties of the consequents lead to a quadratic optimization framework without online linearisation, which has analytical solution in the calculation of control sequence. Both the parameters in the antecedents and the consequents are calculated by a hybrid-learning algorithm based on plant data, and the data-driven determination of the parameters leads to an optimization framework with optimized controller parameters, which could provide higher control accuracy. Experiments are conducted in the process control of biochemical continuous sterilization, and the performance of the proposed method is compared with those of the methods of MPC based on linear model, the nonlinear MPC with neural network approximator, and MPC nonlinear with successive linearisations. The experimental results verify that the proposed framework could achieve higher control accuracy.

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Model predictive control for batch processes: Ensuring validity of predictions

Cited by 31 publications

References 37 publications

Multivariate batch to batch optimisation of fermentation processes incorporating validity constraints

Multivariate batch to batch optimisation of fermentation processes incorporating validity constraints

Improved H-Infinity Hybrid Model Predictive Fault-Tolerant Control for Time-Delayed Batch Processes Against Disturbances

Data‐Driven Optimization Framework for Nonlinear Model Predictive Control

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